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class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="39420980" href="https://www.academia.edu/Documents/in/Deception_Lying_Deception_Lying_"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/JonathanBurdette","location":"/JonathanBurdette","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/JonathanBurdette","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Deception / Lying (Deception Lying)"]}" 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href="https://www.academia.edu/124721126/The_Brain_as_a_Complex_System_Using_Network_Science_as_a_Tool_for_Understanding_the_Brain"><img alt="Research paper thumbnail of The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain" class="work-thumbnail" src="https://attachments.academia-assets.com/118894166/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124721126/The_Brain_as_a_Complex_System_Using_Network_Science_as_a_Tool_for_Understanding_the_Brain">The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain</a></div><div class="wp-workCard_item"><span>Brain connectivity</span><span>, Oct 1, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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Arterial Spin-Labeled MR Perfusion Imaging: Clinical Applications" class="work-thumbnail" src="https://attachments.academia-assets.com/84493836/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960711/Arterial_Spin_Labeled_MR_Perfusion_Imaging_Clinical_Applications">Arterial Spin-Labeled MR Perfusion Imaging: Clinical Applications</a></div><div class="wp-workCard_item"><span>Magnetic Resonance Imaging Clinics of North America</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="10bdd72c9b1cc7b41aed6069092860f0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960708"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960708/An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets"><img alt="Research paper thumbnail of An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets" class="work-thumbnail" src="https://attachments.academia-assets.com/84493832/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960708/An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets">An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets</a></div><div class="wp-workCard_item"><span>NeuroImage</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dc53b9f5c0c1f2ae78b53d6ae94f52ef" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84493832,"asset_id":76960708,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84493832/download_file?st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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_.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "dc53b9f5c0c1f2ae78b53d6ae94f52ef" } } $('.js-work-strip[data-work-id=76960708]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960708,"title":"An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged brain volume. This form of analysis can be likened to a \"fishing expedition.\" As we become more knowledgeable about the structure-function relationships of different brain regions, tools for a priori hypothesis testing are needed. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. Additionally, we interfaced these multivolume atlases to a widely used fMRI software package, SPM99, and demonstrate the use of the atlas tool with representative fMRI data. This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.","publication_date":{"day":null,"month":null,"year":2003,"errors":{}},"publication_name":"NeuroImage","grobid_abstract_attachment_id":84493832},"translated_abstract":null,"internal_url":"https://www.academia.edu/76960708/An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets","translated_internal_url":"","created_at":"2022-04-19T07:37:02.372-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":84493832,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84493832/thumbnails/1.jpg","file_name":"s1053-8119_2803_2900169-120220419-1-1thjyo.pdf","download_url":"https://www.academia.edu/attachments/84493832/download_file?st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_automated_method_for_neuroanatomic_an.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84493832/s1053-8119_2803_2900169-120220419-1-1thjyo-libre.pdf?1650399694=\u0026response-content-disposition=attachment%3B+filename%3DAn_automated_method_for_neuroanatomic_an.pdf\u0026Expires=1733262662\u0026Signature=UPX-8KlVPIf~g8l1fstfIwg6gCfGkLm1QwCXhjIdlvhiIR7alKLjX~UeqLSw8AmAjLfesmJxhAY~8lMGBHXjL5UFamdAO49fKnQELx-eLKMk7AvcdDwphtkWaKT8NiWH4wY4jwHJzqGKe9owd3RlR-uhbMJ72~~xpDIWfg63Mew-H-UeIij3IRIYf5CKI5cGUigvAzfI-2ObH~hFbrB6A0JnwVrOCBMaczSZJN4wVaZpwK1oLY1gZrJLZSoPkLeU2bnZ4lRZ2YCMraWv7DAxQKoNeV2vHBJaoGoybTgWUOEJGVqe3OZiqeMEXPZAByvpMkU8hwUofhtD3CSEKGgGwA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan 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profile--work_container" data-work-id="76960706"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960706/Changes_in_global_and_regional_modularity_associated_with_increasing_working_memory_load"><img alt="Research paper thumbnail of Changes in global and regional modularity associated with increasing working memory load" class="work-thumbnail" src="https://attachments.academia-assets.com/84493796/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960706/Changes_in_global_and_regional_modularity_associated_with_increasing_working_memory_load">Changes in global and regional modularity associated with increasing working memory load</a></div><div class="wp-workCard_item"><span>Frontiers in Human Neuroscience</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb1642763e4f65e72091d88edff982a4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84493796,"asset_id":76960706,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84493796/download_file?st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960706"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960706"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960706; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960706]").text(description); $(".js-view-count[data-work-id=76960706]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960706; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960706']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + 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WowProfile.WorkStripView({ el: this, workJSON: {"id":76960706,"title":"Changes in global and regional modularity associated with increasing working memory load","translated_title":"","metadata":{"publisher":"Frontiers Media SA","grobid_abstract":"Using graph theory measures common to complex network analyses of neuroimaging data, the objective of this study was to explore the effects of increasing working memory processing load on functional brain network topology in a cohort of young adults. Measures of modularity in complex brain networks quantify how well a network is organized into densely interconnected communities. We investigated changes in both the large-scale modular organization of the functional brain network as a whole and regional changes in modular organization as demands on working memory increased from n = 1 to n = 2 on the standard n-back task. We further investigated the relationship between modular properties across working memory load conditions and behavioral performance. Our results showed that regional modular organization within the default mode and working memory circuits significantly changed from 1-back to 2-back task conditions. However, the regional modular organization was not associated with behavioral performance. Global measures of modular organization did not change with working memory load but were associated with individual variability in behavioral performance. These findings indicate that regional and global network properties are modulated by different aspects of working memory under increasing load conditions. These findings highlight the importance of assessing multiple features of functional brain network topology at both global and regional scales rather than focusing on a single network property.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Frontiers in Human Neuroscience","grobid_abstract_attachment_id":84493796},"translated_abstract":null,"internal_url":"https://www.academia.edu/76960706/Changes_in_global_and_regional_modularity_associated_with_increasing_working_memory_load","translated_internal_url":"","created_at":"2022-04-19T07:37:02.183-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":84493796,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84493796/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/84493796/download_file?st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Changes_in_global_and_regional_modularit.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84493796/pdf-libre.pdf?1650399225=\u0026response-content-disposition=attachment%3B+filename%3DChanges_in_global_and_regional_modularit.pdf\u0026Expires=1733262662\u0026Signature=VIzVEoj1CA1BpqMIKwDjuLX2JXUl19WScJmPcBqJEGVn6IAnvMrSXv3ZqU~zF6T3Mv56gCJ6rVmr~li9SoBA814VSIu2cFp0zXgpV3B1FeeGh2cwqiY7CtjxUYleRVWo50fZqZSNEgtRu9g~Dm0QQFWiRyp19-jWJaXHwoMc1~MmLqD4wzyHsHq-m-dw3SU42yUFeap0D2U--39Nr5TWMCTPst1PWAkKDFUihy~wox8m~tXa771j2zT352-Z0GMLvoE~vzQ705Q--7rdrfuKoDEH5yyUp05LbuBsPLIs8MVaEm3wiZX-nGUffZaG8Fpp92q2J6CruKcm8WVaXnM9zw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Changes_in_global_and_regional_modularity_associated_with_increasing_working_memory_load","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[{"id":84493796,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84493796/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/84493796/download_file?st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Changes_in_global_and_regional_modularit.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84493796/pdf-libre.pdf?1650399225=\u0026response-content-disposition=attachment%3B+filename%3DChanges_in_global_and_regional_modularit.pdf\u0026Expires=1733262662\u0026Signature=VIzVEoj1CA1BpqMIKwDjuLX2JXUl19WScJmPcBqJEGVn6IAnvMrSXv3ZqU~zF6T3Mv56gCJ6rVmr~li9SoBA814VSIu2cFp0zXgpV3B1FeeGh2cwqiY7CtjxUYleRVWo50fZqZSNEgtRu9g~Dm0QQFWiRyp19-jWJaXHwoMc1~MmLqD4wzyHsHq-m-dw3SU42yUFeap0D2U--39Nr5TWMCTPst1PWAkKDFUihy~wox8m~tXa771j2zT352-Z0GMLvoE~vzQ705Q--7rdrfuKoDEH5yyUp05LbuBsPLIs8MVaEm3wiZX-nGUffZaG8Fpp92q2J6CruKcm8WVaXnM9zw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory"},{"id":8538,"name":"Working Memory","url":"https://www.academia.edu/Documents/in/Working_Memory"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":36812,"name":"Network science","url":"https://www.academia.edu/Documents/in/Network_science"},{"id":99499,"name":"Complex network","url":"https://www.academia.edu/Documents/in/Complex_network"},{"id":154234,"name":"Modularity","url":"https://www.academia.edu/Documents/in/Modularity"}],"urls":[]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960702"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960702/Fully_Automated_Processing_of_fMRI_Data_in_SPM_from_MRI_Scanner_to_PACS"><img alt="Research paper thumbnail of Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960702/Fully_Automated_Processing_of_fMRI_Data_in_SPM_from_MRI_Scanner_to_PACS">Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS</a></div><div class="wp-workCard_item"><span>Neuroinformatics</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Here we describe the Wake Forest University Pipeline, a fully automated method for the processing...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Here we describe the Wake Forest University Pipeline, a fully automated method for the processing of fMRI data using SPM. The method includes fully automated data transfer and archiving from the point of acquisition, real-time batch script generation, distributed grid processing, interface to SPM in MATLAB, error recovery and data provenance, DICOM conversion and PACS insertion. It has been used for automated processing of fMRI experiments, as well as for the clinical implementation of fMRI and spin-tag perfusion imaging. The pipeline requires no manual intervention, and can be extended to any studies requiring offline processing.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960702"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960702"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960702; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960702]").text(description); $(".js-view-count[data-work-id=76960702]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960702; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960702']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 76960702, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=76960702]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960702,"title":"Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS","translated_title":"","metadata":{"abstract":"Here we describe the Wake Forest University Pipeline, a fully automated method for the processing of fMRI data using SPM. The method includes fully automated data transfer and archiving from the point of acquisition, real-time batch script generation, distributed grid processing, interface to SPM in MATLAB, error recovery and data provenance, DICOM conversion and PACS insertion. It has been used for automated processing of fMRI experiments, as well as for the clinical implementation of fMRI and spin-tag perfusion imaging. The pipeline requires no manual intervention, and can be extended to any studies requiring offline processing.","publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"Neuroinformatics"},"translated_abstract":"Here we describe the Wake Forest University Pipeline, a fully automated method for the processing of fMRI data using SPM. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960697"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960697/Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance"><img alt="Research paper thumbnail of Semantic congruence is a critical factor in multisensory behavioral performance" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960697/Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance">Semantic congruence is a critical factor in multisensory behavioral performance</a></div><div class="wp-workCard_item"><span>Experimental Brain Research</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It has repeatedly been demonstrated that the presence of multiple cues in different sensory modal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (eg., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960697"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960697"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960697; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960697]").text(description); $(".js-view-count[data-work-id=76960697]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960697; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960697']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 76960697, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=76960697]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960697,"title":"Semantic congruence is a critical factor in multisensory behavioral performance","translated_title":"","metadata":{"abstract":"It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (eg., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.","publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2004,"errors":{}},"publication_name":"Experimental Brain Research"},"translated_abstract":"It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (eg., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.","internal_url":"https://www.academia.edu/76960697/Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance","translated_internal_url":"","created_at":"2022-04-19T07:37:01.385-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":4307,"name":"Behavior","url":"https://www.academia.edu/Documents/in/Behavior"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":36837,"name":"Information Processing","url":"https://www.academia.edu/Documents/in/Information_Processing"},{"id":88325,"name":"Cues","url":"https://www.academia.edu/Documents/in/Cues"},{"id":99915,"name":"Integration","url":"https://www.academia.edu/Documents/in/Integration"},{"id":220049,"name":"Accuracy","url":"https://www.academia.edu/Documents/in/Accuracy"},{"id":246163,"name":"Knowledge base","url":"https://www.academia.edu/Documents/in/Knowledge_base"},{"id":413194,"name":"Analysis of Variance","url":"https://www.academia.edu/Documents/in/Analysis_of_Variance"},{"id":637718,"name":"Nervous System","url":"https://www.academia.edu/Documents/in/Nervous_System"},{"id":638808,"name":"Precision","url":"https://www.academia.edu/Documents/in/Precision"},{"id":978828,"name":"Congruence","url":"https://www.academia.edu/Documents/in/Congruence"},{"id":2428413,"name":"Acoustic Stimulation","url":"https://www.academia.edu/Documents/in/Acoustic_Stimulation"},{"id":2849038,"name":"photic stimulation","url":"https://www.academia.edu/Documents/in/photic_stimulation"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960695"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960695/Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study"><img alt="Research paper thumbnail of Brain MRI predictors of global and domain specific cognitive function at 10 years follow up: ARIC brain MRI study" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960695/Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study">Brain MRI predictors of global and domain specific cognitive function at 10 years follow up: ARIC brain MRI study</a></div><div class="wp-workCard_item"><span>Alzheimer's & Dementia</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The total acquisition time is roughly 15 minutes. The AAL (Automated Anatomical Labeling) template was registered with 12 degree of freedom affine transformation and 2 stage of B-Spline mutual information based non-rigid registration of the grid size of 5mm and 2.5mm deformed to our AD subject spatial space[4]. The AD subject selection is complied with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th) criteria. There are total 12 AD subjects with age of 78.92 6 6.07,MMSE of 146 6.90 and education of 2.56 4.58 yrs. In eachmedial temporal ROI, we calculate the FA, MD and AD. Results: The calculation of FA andMD can be found in [5]. The AD is the largest eigen-value ?1. The AD can avoid the situation like when?1€ Eœ?2€ Eœ?3 the magnitude of FA is still large. Table 1 shows the correlation results between brain regions with age and regions with MMSE. In correlation with age, the MD of right of amygdala, right thalamus, right of hippocampus and left of parahippocampal correlate to age well (p &lt; 0.05). The FA-age correlations in the right of post cingulum, left of parahippocampal and right of amygdala are well. The AD-age correlations in the right of hippocampus, left of parahippocampus, right of amygdala, right of thalamus are strongly correlated. In MMSE and DTI metrics correlations, the cingulum is strongly correlated with both AD and MD. The Amygdala, parahippocampus and temporal pole are in good correlations with FA, MD and AD. Conclusions: The DTI metrics on hippocampus correlate both age and MMSE well. In the correlation between MMSE, AD and MD, cingulum shows strong correlation. The quantitative DTI metrics results demonstrate the possibility of using these metrics as the clinical criteria of discriminating the progression of AD.References: [1]M. D. Denis Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging, vol. 13, p. 534, 2001. [2] I. N. C. Lawes, et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, vol. 39, pp. 62-79, 2008. [3] S. S. Mori and P. C. P. C. M. van Zijl, “Fiber tracking: principles and strategies a technical review,” NMR in Biomedicine, vol. 15, pp. 468-80, 2002. [4] G. K. Rohde, et al., “The adaptive bases algorithm for intensity-based nonrigid image registration,” Medical Imaging, IEEE Transactions, vol. 22, pp. 1470-1479, 2003. [5] C. F. Westin, “Processing and visualization for diffusion tensor MRI,” Medical Image Analysis, vol. 6, p. 93, 2002.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960695"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960695"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960695; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960695]").text(description); $(".js-view-count[data-work-id=76960695]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960695; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960695']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 76960695, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=76960695]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960695,"title":"Brain MRI predictors of global and domain specific cognitive function at 10 years follow up: ARIC brain MRI study","translated_title":"","metadata":{"abstract":"are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The total acquisition time is roughly 15 minutes. The AAL (Automated Anatomical Labeling) template was registered with 12 degree of freedom affine transformation and 2 stage of B-Spline mutual information based non-rigid registration of the grid size of 5mm and 2.5mm deformed to our AD subject spatial space[4]. The AD subject selection is complied with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th) criteria. There are total 12 AD subjects with age of 78.92 6 6.07,MMSE of 146 6.90 and education of 2.56 4.58 yrs. In eachmedial temporal ROI, we calculate the FA, MD and AD. Results: The calculation of FA andMD can be found in [5]. The AD is the largest eigen-value ?1. The AD can avoid the situation like when?1€ Eœ?2€ Eœ?3 the magnitude of FA is still large. Table 1 shows the correlation results between brain regions with age and regions with MMSE. In correlation with age, the MD of right of amygdala, right thalamus, right of hippocampus and left of parahippocampal correlate to age well (p \u0026lt; 0.05). The FA-age correlations in the right of post cingulum, left of parahippocampal and right of amygdala are well. The AD-age correlations in the right of hippocampus, left of parahippocampus, right of amygdala, right of thalamus are strongly correlated. In MMSE and DTI metrics correlations, the cingulum is strongly correlated with both AD and MD. The Amygdala, parahippocampus and temporal pole are in good correlations with FA, MD and AD. Conclusions: The DTI metrics on hippocampus correlate both age and MMSE well. In the correlation between MMSE, AD and MD, cingulum shows strong correlation. The quantitative DTI metrics results demonstrate the possibility of using these metrics as the clinical criteria of discriminating the progression of AD.References: [1]M. D. Denis Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging, vol. 13, p. 534, 2001. [2] I. N. C. Lawes, et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, vol. 39, pp. 62-79, 2008. [3] S. S. Mori and P. C. P. C. M. van Zijl, “Fiber tracking: principles and strategies a technical review,” NMR in Biomedicine, vol. 15, pp. 468-80, 2002. [4] G. K. Rohde, et al., “The adaptive bases algorithm for intensity-based nonrigid image registration,” Medical Imaging, IEEE Transactions, vol. 22, pp. 1470-1479, 2003. [5] C. F. Westin, “Processing and visualization for diffusion tensor MRI,” Medical Image Analysis, vol. 6, p. 93, 2002.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Alzheimer's \u0026 Dementia"},"translated_abstract":"are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The total acquisition time is roughly 15 minutes. The AAL (Automated Anatomical Labeling) template was registered with 12 degree of freedom affine transformation and 2 stage of B-Spline mutual information based non-rigid registration of the grid size of 5mm and 2.5mm deformed to our AD subject spatial space[4]. The AD subject selection is complied with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th) criteria. There are total 12 AD subjects with age of 78.92 6 6.07,MMSE of 146 6.90 and education of 2.56 4.58 yrs. In eachmedial temporal ROI, we calculate the FA, MD and AD. Results: The calculation of FA andMD can be found in [5]. The AD is the largest eigen-value ?1. The AD can avoid the situation like when?1€ Eœ?2€ Eœ?3 the magnitude of FA is still large. Table 1 shows the correlation results between brain regions with age and regions with MMSE. In correlation with age, the MD of right of amygdala, right thalamus, right of hippocampus and left of parahippocampal correlate to age well (p \u0026lt; 0.05). The FA-age correlations in the right of post cingulum, left of parahippocampal and right of amygdala are well. The AD-age correlations in the right of hippocampus, left of parahippocampus, right of amygdala, right of thalamus are strongly correlated. In MMSE and DTI metrics correlations, the cingulum is strongly correlated with both AD and MD. The Amygdala, parahippocampus and temporal pole are in good correlations with FA, MD and AD. Conclusions: The DTI metrics on hippocampus correlate both age and MMSE well. In the correlation between MMSE, AD and MD, cingulum shows strong correlation. The quantitative DTI metrics results demonstrate the possibility of using these metrics as the clinical criteria of discriminating the progression of AD.References: [1]M. D. Denis Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging, vol. 13, p. 534, 2001. [2] I. N. C. Lawes, et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, vol. 39, pp. 62-79, 2008. [3] S. S. Mori and P. C. P. C. M. van Zijl, “Fiber tracking: principles and strategies a technical review,” NMR in Biomedicine, vol. 15, pp. 468-80, 2002. [4] G. K. Rohde, et al., “The adaptive bases algorithm for intensity-based nonrigid image registration,” Medical Imaging, IEEE Transactions, vol. 22, pp. 1470-1479, 2003. [5] C. F. Westin, “Processing and visualization for diffusion tensor MRI,” Medical Image Analysis, vol. 6, p. 93, 2002.","internal_url":"https://www.academia.edu/76960695/Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study","translated_internal_url":"","created_at":"2022-04-19T07:37:01.231-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":386576,"name":"Domain Specificity","url":"https://www.academia.edu/Documents/in/Domain_Specificity"},{"id":441653,"name":"Cognitive Function","url":"https://www.academia.edu/Documents/in/Cognitive_Function"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="71997492"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/71997492/Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity"><img alt="Research paper thumbnail of Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/71997492/Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity">Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="71997492"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="71997492"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 71997492; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=71997492]").text(description); $(".js-view-count[data-work-id=71997492]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 71997492; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='71997492']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 71997492, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=71997492]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":71997492,"title":"Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity","translated_title":"","metadata":{"abstract":"The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure.","publisher":"Obesity","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure.","internal_url":"https://www.academia.edu/71997492/Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity","translated_internal_url":"","created_at":"2022-02-20T12:31:24.517-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":3851,"name":"Obesity","url":"https://www.academia.edu/Documents/in/Obesity"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="67145583"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67145583/Effects_of_a_Motor_Imagery_Task_on_Functional_Brain_Network_Community_Structure_in_Older_Adults_Data_from_the_Brain_Networks_and_Mobility_Function_B_NET_Study"><img alt="Research paper thumbnail of Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study" class="work-thumbnail" src="https://attachments.academia-assets.com/78073476/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67145583/Effects_of_a_Motor_Imagery_Task_on_Functional_Brain_Network_Community_Structure_in_Older_Adults_Data_from_the_Brain_Networks_and_Mobility_Function_B_NET_Study">Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Elucidating the neural correlates of mobility is critical given the increasing population of olde...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the c...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a8c2b058100bcff1a9a9ae06bb66ca4e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78073476,"asset_id":67145583,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78073476/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67145583"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67145583"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67145583; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67145583]").text(description); $(".js-view-count[data-work-id=67145583]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67145583; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67145583']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 67145583, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a8c2b058100bcff1a9a9ae06bb66ca4e" } } $('.js-work-strip[data-work-id=67145583]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67145583,"title":"Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study","translated_title":"","metadata":{"abstract":"Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789422"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789422/Therapeutic_Instrumental_Music_Training_and_Motor_Imagery_in_Post_Stroke_Upper_Extremity_Rehabilitation_A_Randomized_Controlled_Pilot_Study"><img alt="Research paper thumbnail of Therapeutic Instrumental Music Training and Motor Imagery in Post-Stroke Upper-Extremity Rehabilitation: A Randomized-Controlled Pilot Study" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789422/Therapeutic_Instrumental_Music_Training_and_Motor_Imagery_in_Post_Stroke_Upper_Extremity_Rehabilitation_A_Randomized_Controlled_Pilot_Study">Therapeutic Instrumental Music Training and Motor Imagery in Post-Stroke Upper-Extremity Rehabilitation: A Randomized-Controlled Pilot Study</a></div><div class="wp-workCard_item"><span>Archives of Rehabilitation Research and Clinical Translation</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789422"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789422"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789422; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789419"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789419/Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress"><img alt="Research paper thumbnail of Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789419/Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress">Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress</a></div><div class="wp-workCard_item"><span>Journal of neuroimaging : official journal of the American Society of Neuroimaging</span><span>, Jan 10, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Post-traumatic stress disorder is associated with connectivity changes in the default mode, centr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sa...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789419"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789419"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789419; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789419]").text(description); $(".js-view-count[data-work-id=61789419]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789419; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789419']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789419, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789419]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789419,"title":"Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress","translated_title":"","metadata":{"abstract":"Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sa...","publication_date":{"day":10,"month":1,"year":2018,"errors":{}},"publication_name":"Journal of neuroimaging : official journal of the American Society of Neuroimaging"},"translated_abstract":"Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sa...","internal_url":"https://www.academia.edu/61789419/Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress","translated_internal_url":"","created_at":"2021-11-16T08:31:50.181-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":2639,"name":"Neuroimaging","url":"https://www.academia.edu/Documents/in/Neuroimaging"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789417"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789417/Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults"><img alt="Research paper thumbnail of Dynamic fMRI networks predict success in a behavioral weight loss program among older adults" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789417/Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults">Dynamic fMRI networks predict success in a behavioral weight loss program among older adults</a></div><div class="wp-workCard_item"><span>NeuroImage</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">More than one-third of adults in the United States are obese, with a higher prevalence among olde...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnet...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789417"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789417"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789417; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789417]").text(description); $(".js-view-count[data-work-id=61789417]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789417; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789417']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789417, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789417]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789417,"title":"Dynamic fMRI networks predict success in a behavioral weight loss program among older adults","translated_title":"","metadata":{"abstract":"More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. 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Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnet...","internal_url":"https://www.academia.edu/61789417/Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults","translated_internal_url":"","created_at":"2021-11-16T08:31:50.041-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":103260,"name":"Neuroimage","url":"https://www.academia.edu/Documents/in/Neuroimage"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789414"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789414/Beet_Root_Juice_An_Ergogenic_Aid_for_Exercise_and_the_Aging_Brain"><img alt="Research paper thumbnail of Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain" class="work-thumbnail" src="https://attachments.academia-assets.com/74737385/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789414/Beet_Root_Juice_An_Ergogenic_Aid_for_Exercise_and_the_Aging_Brain">Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain</a></div><div class="wp-workCard_item"><span>The journals of gerontology. Series A, Biological sciences and medical sciences</span><span>, Jan 9, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingest...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). Differences in second-order connections between the somatomotor cortex and insular cortex were also significant; the exercise + BRJ group (M = 3.28, SE = 0.167) had a significantly lower number of connections than exercise + placebo (M =...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="21b02380b3395894ca4b978b5dd147e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74737385,"asset_id":61789414,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74737385/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789414"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789414"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789414; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789414]").text(description); $(".js-view-count[data-work-id=61789414]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789414; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789414']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789414, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "21b02380b3395894ca4b978b5dd147e6" } } $('.js-work-strip[data-work-id=61789414]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789414,"title":"Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain","translated_title":"","metadata":{"abstract":"Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). Differences in second-order connections between the somatomotor cortex and insular cortex were also significant; the exercise + BRJ group (M = 3.28, SE = 0.167) had a significantly lower number of connections than exercise + placebo (M =...","publication_date":{"day":9,"month":1,"year":2016,"errors":{}},"publication_name":"The journals of gerontology. Series A, Biological sciences and medical sciences"},"translated_abstract":"Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789411"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789411/The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers"><img alt="Research paper thumbnail of The Impacts of Pesticide and Nicotine Exposures on Functional Brain Networks in Latino Immigrant workers" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789411/The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers">The Impacts of Pesticide and Nicotine Exposures on Functional Brain Networks in Latino Immigrant workers</a></div><div class="wp-workCard_item"><span>Neurotoxicology</span><span>, Jan 2, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, su...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789411"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789411"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789411; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789411]").text(description); $(".js-view-count[data-work-id=61789411]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789411; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789411']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789411, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789411]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789411,"title":"The Impacts of Pesticide and Nicotine Exposures on Functional Brain Networks in Latino Immigrant workers","translated_title":"","metadata":{"abstract":"Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and ...","publication_date":{"day":2,"month":1,"year":2017,"errors":{}},"publication_name":"Neurotoxicology"},"translated_abstract":"Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and ...","internal_url":"https://www.academia.edu/61789411/The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers","translated_internal_url":"","created_at":"2021-11-16T08:31:49.748-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging"},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability"},{"id":51688,"name":"Neurotoxicology","url":"https://www.academia.edu/Documents/in/Neurotoxicology"},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain"},{"id":85437,"name":"Pesticides","url":"https://www.academia.edu/Documents/in/Pesticides"},{"id":91360,"name":"Nicotine","url":"https://www.academia.edu/Documents/in/Nicotine"},{"id":120646,"name":"Acetylcholinesterase","url":"https://www.academia.edu/Documents/in/Acetylcholinesterase"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":380825,"name":"Oxygen","url":"https://www.academia.edu/Documents/in/Oxygen"},{"id":396914,"name":"Occupational Exposure","url":"https://www.academia.edu/Documents/in/Occupational_Exposure"},{"id":704401,"name":"Neural pathways","url":"https://www.academia.edu/Documents/in/Neural_pathways"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"},{"id":1559335,"name":"Cotinine","url":"https://www.academia.edu/Documents/in/Cotinine"},{"id":2519258,"name":"Butyrylcholinesterase","url":"https://www.academia.edu/Documents/in/Butyrylcholinesterase"},{"id":3789884,"name":"Pharmacology and pharmaceutical sciences","url":"https://www.academia.edu/Documents/in/Pharmacology_and_pharmaceutical_sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789408"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789408/Effects_of_supervised_exercise_and_dietary_nitrate_in_older_adults_with_controlled_hypertension_and_or_heart_failure_with_preserved_ejection_fraction"><img alt="Research paper thumbnail of Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789408/Effects_of_supervised_exercise_and_dietary_nitrate_in_older_adults_with_controlled_hypertension_and_or_heart_failure_with_preserved_ejection_fraction">Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction</a></div><div class="wp-workCard_item"><span>Nitric oxide : biology and chemistry</span><span>, Jan 23, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in ind...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFp...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789408"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789408"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789408; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789408]").text(description); $(".js-view-count[data-work-id=61789408]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789408; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789408']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789408, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789408]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789408,"title":"Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction","translated_title":"","metadata":{"abstract":"Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFp...","publication_date":{"day":23,"month":1,"year":2017,"errors":{}},"publication_name":"Nitric oxide : biology and chemistry"},"translated_abstract":"Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFp...","internal_url":"https://www.academia.edu/61789408/Effects_of_supervised_exercise_and_dietary_nitrate_in_older_adults_with_controlled_hypertension_and_or_heart_failure_with_preserved_ejection_fraction","translated_internal_url":"","created_at":"2021-11-16T08:31:49.611-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Effects_of_supervised_exercise_and_dietary_nitrate_in_older_adults_with_controlled_hypertension_and_or_heart_failure_with_preserved_ejection_fraction","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences"},{"id":49633,"name":"Heart Failure","url":"https://www.academia.edu/Documents/in/Heart_Failure"},{"id":71399,"name":"Hypertension","url":"https://www.academia.edu/Documents/in/Hypertension"},{"id":88321,"name":"Blood Pressure","url":"https://www.academia.edu/Documents/in/Blood_Pressure"},{"id":93922,"name":"Nitric oxide","url":"https://www.academia.edu/Documents/in/Nitric_oxide"},{"id":122402,"name":"Nitrates","url":"https://www.academia.edu/Documents/in/Nitrates"},{"id":135185,"name":"Exercise","url":"https://www.academia.edu/Documents/in/Exercise"},{"id":152562,"name":"Dietary Supplements","url":"https://www.academia.edu/Documents/in/Dietary_Supplements"},{"id":260118,"name":"CHEMICAL SCIENCES","url":"https://www.academia.edu/Documents/in/CHEMICAL_SCIENCES"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":380825,"name":"Oxygen","url":"https://www.academia.edu/Documents/in/Oxygen"},{"id":1166928,"name":"Beta Vulgaris","url":"https://www.academia.edu/Documents/in/Beta_Vulgaris"},{"id":1654024,"name":"Nitrites","url":"https://www.academia.edu/Documents/in/Nitrites"},{"id":2183225,"name":"Physical Endurance","url":"https://www.academia.edu/Documents/in/Physical_Endurance"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789406"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789406/Baseline_gray_and_white_matter_volume_predict_successful_weight_loss_in_the_elderly"><img alt="Research paper thumbnail of Baseline gray- and white-matter volume predict successful weight loss in the elderly" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789406/Baseline_gray_and_white_matter_volume_predict_successful_weight_loss_in_the_elderly">Baseline gray- and white-matter volume predict successful weight loss in the elderly</a></div><div class="wp-workCard_item"><span>Obesity (Silver Spring, Md.)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this study was to investigate whether structural brain phenotypes could be used to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this study was to investigate whether structural brain phenotypes could be used to predict weight loss success following behavioral interventions in older adults with overweight or obesity and cardiometabolic dysfunction. A support vector machine with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter and white matter volume from 52 individuals who completed the intervention and a magnetic resonance imaging session. The support vector machine resulted in an average classification accuracy of 72.62% based on gray matter and white matter volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Findings suggest that baseline brain structure was able to predict weight loss success foll...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789406"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789406"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789406; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789406]").text(description); $(".js-view-count[data-work-id=61789406]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789406; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789406']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789406, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789406]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789406,"title":"Baseline gray- and white-matter volume predict successful weight loss in the elderly","translated_title":"","metadata":{"abstract":"The purpose of this study was to investigate whether structural brain phenotypes could be used to predict weight loss success following behavioral interventions in older adults with overweight or obesity and cardiometabolic dysfunction. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="4181272" id="papers"><div class="js-work-strip profile--work_container" data-work-id="124721126"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124721126/The_Brain_as_a_Complex_System_Using_Network_Science_as_a_Tool_for_Understanding_the_Brain"><img alt="Research paper thumbnail of The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain" class="work-thumbnail" src="https://attachments.academia-assets.com/118894166/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124721126/The_Brain_as_a_Complex_System_Using_Network_Science_as_a_Tool_for_Understanding_the_Brain">The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain</a></div><div class="wp-workCard_item"><span>Brain connectivity</span><span>, Oct 1, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="893511bc786a37432cf1a38e5aacea49" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118894166,"asset_id":124721126,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118894166/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124721126"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124721126"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124721126; 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href="https://www.academia.edu/76960708/An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets">An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets</a></div><div class="wp-workCard_item"><span>NeuroImage</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dc53b9f5c0c1f2ae78b53d6ae94f52ef" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84493832,"asset_id":76960708,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84493832/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: 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This form of analysis can be likened to a \"fishing expedition.\" As we become more knowledgeable about the structure-function relationships of different brain regions, tools for a priori hypothesis testing are needed. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. 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This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.","publication_date":{"day":null,"month":null,"year":2003,"errors":{}},"publication_name":"NeuroImage","grobid_abstract_attachment_id":84493832},"translated_abstract":null,"internal_url":"https://www.academia.edu/76960708/An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets","translated_internal_url":"","created_at":"2022-04-19T07:37:02.372-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":84493832,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84493832/thumbnails/1.jpg","file_name":"s1053-8119_2803_2900169-120220419-1-1thjyo.pdf","download_url":"https://www.academia.edu/attachments/84493832/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_automated_method_for_neuroanatomic_an.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84493832/s1053-8119_2803_2900169-120220419-1-1thjyo-libre.pdf?1650399694=\u0026response-content-disposition=attachment%3B+filename%3DAn_automated_method_for_neuroanatomic_an.pdf\u0026Expires=1733262662\u0026Signature=UPX-8KlVPIf~g8l1fstfIwg6gCfGkLm1QwCXhjIdlvhiIR7alKLjX~UeqLSw8AmAjLfesmJxhAY~8lMGBHXjL5UFamdAO49fKnQELx-eLKMk7AvcdDwphtkWaKT8NiWH4wY4jwHJzqGKe9owd3RlR-uhbMJ72~~xpDIWfg63Mew-H-UeIij3IRIYf5CKI5cGUigvAzfI-2ObH~hFbrB6A0JnwVrOCBMaczSZJN4wVaZpwK1oLY1gZrJLZSoPkLeU2bnZ4lRZ2YCMraWv7DAxQKoNeV2vHBJaoGoybTgWUOEJGVqe3OZiqeMEXPZAByvpMkU8hwUofhtD3CSEKGgGwA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_automated_method_for_neuroanatomic_and_cytoarchitectonic_atlas_based_interrogation_of_fMRI_data_sets","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan 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class="wp-workCard_item"><span>Frontiers in Human Neuroscience</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb1642763e4f65e72091d88edff982a4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84493796,"asset_id":76960706,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84493796/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960706"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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Measures of modularity in complex brain networks quantify how well a network is organized into densely interconnected communities. We investigated changes in both the large-scale modular organization of the functional brain network as a whole and regional changes in modular organization as demands on working memory increased from n = 1 to n = 2 on the standard n-back task. We further investigated the relationship between modular properties across working memory load conditions and behavioral performance. Our results showed that regional modular organization within the default mode and working memory circuits significantly changed from 1-back to 2-back task conditions. However, the regional modular organization was not associated with behavioral performance. Global measures of modular organization did not change with working memory load but were associated with individual variability in behavioral performance. These findings indicate that regional and global network properties are modulated by different aspects of working memory under increasing load conditions. These findings highlight the importance of assessing multiple features of functional brain network topology at both global and regional scales rather than focusing on a single network property.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Frontiers in Human Neuroscience","grobid_abstract_attachment_id":84493796},"translated_abstract":null,"internal_url":"https://www.academia.edu/76960706/Changes_in_global_and_regional_modularity_associated_with_increasing_working_memory_load","translated_internal_url":"","created_at":"2022-04-19T07:37:02.183-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":84493796,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84493796/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/84493796/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Changes_in_global_and_regional_modularit.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84493796/pdf-libre.pdf?1650399225=\u0026response-content-disposition=attachment%3B+filename%3DChanges_in_global_and_regional_modularit.pdf\u0026Expires=1733262662\u0026Signature=VIzVEoj1CA1BpqMIKwDjuLX2JXUl19WScJmPcBqJEGVn6IAnvMrSXv3ZqU~zF6T3Mv56gCJ6rVmr~li9SoBA814VSIu2cFp0zXgpV3B1FeeGh2cwqiY7CtjxUYleRVWo50fZqZSNEgtRu9g~Dm0QQFWiRyp19-jWJaXHwoMc1~MmLqD4wzyHsHq-m-dw3SU42yUFeap0D2U--39Nr5TWMCTPst1PWAkKDFUihy~wox8m~tXa771j2zT352-Z0GMLvoE~vzQ705Q--7rdrfuKoDEH5yyUp05LbuBsPLIs8MVaEm3wiZX-nGUffZaG8Fpp92q2J6CruKcm8WVaXnM9zw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Changes_in_global_and_regional_modularity_associated_with_increasing_working_memory_load","translated_slug":"","page_count":14,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[{"id":84493796,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84493796/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/84493796/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Changes_in_global_and_regional_modularit.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84493796/pdf-libre.pdf?1650399225=\u0026response-content-disposition=attachment%3B+filename%3DChanges_in_global_and_regional_modularit.pdf\u0026Expires=1733262662\u0026Signature=VIzVEoj1CA1BpqMIKwDjuLX2JXUl19WScJmPcBqJEGVn6IAnvMrSXv3ZqU~zF6T3Mv56gCJ6rVmr~li9SoBA814VSIu2cFp0zXgpV3B1FeeGh2cwqiY7CtjxUYleRVWo50fZqZSNEgtRu9g~Dm0QQFWiRyp19-jWJaXHwoMc1~MmLqD4wzyHsHq-m-dw3SU42yUFeap0D2U--39Nr5TWMCTPst1PWAkKDFUihy~wox8m~tXa771j2zT352-Z0GMLvoE~vzQ705Q--7rdrfuKoDEH5yyUp05LbuBsPLIs8MVaEm3wiZX-nGUffZaG8Fpp92q2J6CruKcm8WVaXnM9zw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory"},{"id":8538,"name":"Working Memory","url":"https://www.academia.edu/Documents/in/Working_Memory"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":36812,"name":"Network science","url":"https://www.academia.edu/Documents/in/Network_science"},{"id":99499,"name":"Complex network","url":"https://www.academia.edu/Documents/in/Complex_network"},{"id":154234,"name":"Modularity","url":"https://www.academia.edu/Documents/in/Modularity"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960704"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960704/Using_network_science_to_evaluate_exercise_associated_brain_changes_in_older_adults"><img alt="Research paper thumbnail of Using network science to evaluate exercise-associated brain changes in older adults" class="work-thumbnail" src="https://attachments.academia-assets.com/84506999/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960704/Using_network_science_to_evaluate_exercise_associated_brain_changes_in_older_adults">Using network science to evaluate exercise-associated brain changes in older adults</a></div><div class="wp-workCard_item"><span>Frontiers in Aging Neuroscience</span><span>, 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d93324eacd0fa0bad93bee59d6306c6e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84506999,"asset_id":76960704,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84506999/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960704"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960704"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960704; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960702"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960702/Fully_Automated_Processing_of_fMRI_Data_in_SPM_from_MRI_Scanner_to_PACS"><img alt="Research paper thumbnail of Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960702/Fully_Automated_Processing_of_fMRI_Data_in_SPM_from_MRI_Scanner_to_PACS">Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS</a></div><div class="wp-workCard_item"><span>Neuroinformatics</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Here we describe the Wake Forest University Pipeline, a fully automated method for the processing...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Here we describe the Wake Forest University Pipeline, a fully automated method for the processing of fMRI data using SPM. The method includes fully automated data transfer and archiving from the point of acquisition, real-time batch script generation, distributed grid processing, interface to SPM in MATLAB, error recovery and data provenance, DICOM conversion and PACS insertion. It has been used for automated processing of fMRI experiments, as well as for the clinical implementation of fMRI and spin-tag perfusion imaging. The pipeline requires no manual intervention, and can be extended to any studies requiring offline processing.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960702"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960702"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960702; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960702]").text(description); $(".js-view-count[data-work-id=76960702]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960702; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960702']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 76960702, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=76960702]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960702,"title":"Fully Automated Processing of fMRI Data in SPM: from MRI Scanner to PACS","translated_title":"","metadata":{"abstract":"Here we describe the Wake Forest University Pipeline, a fully automated method for the processing of fMRI data using SPM. The method includes fully automated data transfer and archiving from the point of acquisition, real-time batch script generation, distributed grid processing, interface to SPM in MATLAB, error recovery and data provenance, DICOM conversion and PACS insertion. It has been used for automated processing of fMRI experiments, as well as for the clinical implementation of fMRI and spin-tag perfusion imaging. The pipeline requires no manual intervention, and can be extended to any studies requiring offline processing.","publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"Neuroinformatics"},"translated_abstract":"Here we describe the Wake Forest University Pipeline, a fully automated method for the processing of fMRI data using SPM. 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class="work-thumbnail" src="https://attachments.academia-assets.com/84507016/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960699/Acute_Effect_of_a_High_Nitrate_Diet_on_Brain_Perfusion_in_Older_Adults">Acute Effect of a High Nitrate Diet on Brain Perfusion in Older Adults</a></div><div class="wp-workCard_item"><span>Free Radical Biology and Medicine</span><span>, 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7fc3efca45abf79a2190fe9b6829caca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84507016,"asset_id":76960699,"asset_type":"Work","button_location":"profile"}" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960697"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960697/Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance"><img alt="Research paper thumbnail of Semantic congruence is a critical factor in multisensory behavioral performance" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960697/Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance">Semantic congruence is a critical factor in multisensory behavioral performance</a></div><div class="wp-workCard_item"><span>Experimental Brain Research</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It has repeatedly been demonstrated that the presence of multiple cues in different sensory modal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (eg., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960697"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960697"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960697; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960697]").text(description); $(".js-view-count[data-work-id=76960697]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960697; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960697']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 76960697, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=76960697]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960697,"title":"Semantic congruence is a critical factor in multisensory behavioral performance","translated_title":"","metadata":{"abstract":"It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (eg., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.","publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2004,"errors":{}},"publication_name":"Experimental Brain Research"},"translated_abstract":"It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (eg., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.","internal_url":"https://www.academia.edu/76960697/Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance","translated_internal_url":"","created_at":"2022-04-19T07:37:01.385-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Semantic_congruence_is_a_critical_factor_in_multisensory_behavioral_performance","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":4307,"name":"Behavior","url":"https://www.academia.edu/Documents/in/Behavior"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":36837,"name":"Information Processing","url":"https://www.academia.edu/Documents/in/Information_Processing"},{"id":88325,"name":"Cues","url":"https://www.academia.edu/Documents/in/Cues"},{"id":99915,"name":"Integration","url":"https://www.academia.edu/Documents/in/Integration"},{"id":220049,"name":"Accuracy","url":"https://www.academia.edu/Documents/in/Accuracy"},{"id":246163,"name":"Knowledge base","url":"https://www.academia.edu/Documents/in/Knowledge_base"},{"id":413194,"name":"Analysis of Variance","url":"https://www.academia.edu/Documents/in/Analysis_of_Variance"},{"id":637718,"name":"Nervous System","url":"https://www.academia.edu/Documents/in/Nervous_System"},{"id":638808,"name":"Precision","url":"https://www.academia.edu/Documents/in/Precision"},{"id":978828,"name":"Congruence","url":"https://www.academia.edu/Documents/in/Congruence"},{"id":2428413,"name":"Acoustic Stimulation","url":"https://www.academia.edu/Documents/in/Acoustic_Stimulation"},{"id":2849038,"name":"photic stimulation","url":"https://www.academia.edu/Documents/in/photic_stimulation"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76960695"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76960695/Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study"><img alt="Research paper thumbnail of Brain MRI predictors of global and domain specific cognitive function at 10 years follow up: ARIC brain MRI study" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76960695/Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study">Brain MRI predictors of global and domain specific cognitive function at 10 years follow up: ARIC brain MRI study</a></div><div class="wp-workCard_item"><span>Alzheimer's & Dementia</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The total acquisition time is roughly 15 minutes. The AAL (Automated Anatomical Labeling) template was registered with 12 degree of freedom affine transformation and 2 stage of B-Spline mutual information based non-rigid registration of the grid size of 5mm and 2.5mm deformed to our AD subject spatial space[4]. The AD subject selection is complied with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th) criteria. There are total 12 AD subjects with age of 78.92 6 6.07,MMSE of 146 6.90 and education of 2.56 4.58 yrs. In eachmedial temporal ROI, we calculate the FA, MD and AD. Results: The calculation of FA andMD can be found in [5]. The AD is the largest eigen-value ?1. The AD can avoid the situation like when?1€ Eœ?2€ Eœ?3 the magnitude of FA is still large. Table 1 shows the correlation results between brain regions with age and regions with MMSE. In correlation with age, the MD of right of amygdala, right thalamus, right of hippocampus and left of parahippocampal correlate to age well (p &lt; 0.05). The FA-age correlations in the right of post cingulum, left of parahippocampal and right of amygdala are well. The AD-age correlations in the right of hippocampus, left of parahippocampus, right of amygdala, right of thalamus are strongly correlated. In MMSE and DTI metrics correlations, the cingulum is strongly correlated with both AD and MD. The Amygdala, parahippocampus and temporal pole are in good correlations with FA, MD and AD. Conclusions: The DTI metrics on hippocampus correlate both age and MMSE well. In the correlation between MMSE, AD and MD, cingulum shows strong correlation. The quantitative DTI metrics results demonstrate the possibility of using these metrics as the clinical criteria of discriminating the progression of AD.References: [1]M. D. Denis Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging, vol. 13, p. 534, 2001. [2] I. N. C. Lawes, et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, vol. 39, pp. 62-79, 2008. [3] S. S. Mori and P. C. P. C. M. van Zijl, “Fiber tracking: principles and strategies a technical review,” NMR in Biomedicine, vol. 15, pp. 468-80, 2002. [4] G. K. Rohde, et al., “The adaptive bases algorithm for intensity-based nonrigid image registration,” Medical Imaging, IEEE Transactions, vol. 22, pp. 1470-1479, 2003. [5] C. F. Westin, “Processing and visualization for diffusion tensor MRI,” Medical Image Analysis, vol. 6, p. 93, 2002.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76960695"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76960695"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76960695; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76960695]").text(description); $(".js-view-count[data-work-id=76960695]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76960695; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76960695']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 76960695, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=76960695]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76960695,"title":"Brain MRI predictors of global and domain specific cognitive function at 10 years follow up: ARIC brain MRI study","translated_title":"","metadata":{"abstract":"are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The total acquisition time is roughly 15 minutes. The AAL (Automated Anatomical Labeling) template was registered with 12 degree of freedom affine transformation and 2 stage of B-Spline mutual information based non-rigid registration of the grid size of 5mm and 2.5mm deformed to our AD subject spatial space[4]. The AD subject selection is complied with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th) criteria. There are total 12 AD subjects with age of 78.92 6 6.07,MMSE of 146 6.90 and education of 2.56 4.58 yrs. In eachmedial temporal ROI, we calculate the FA, MD and AD. Results: The calculation of FA andMD can be found in [5]. The AD is the largest eigen-value ?1. The AD can avoid the situation like when?1€ Eœ?2€ Eœ?3 the magnitude of FA is still large. Table 1 shows the correlation results between brain regions with age and regions with MMSE. In correlation with age, the MD of right of amygdala, right thalamus, right of hippocampus and left of parahippocampal correlate to age well (p \u0026lt; 0.05). The FA-age correlations in the right of post cingulum, left of parahippocampal and right of amygdala are well. The AD-age correlations in the right of hippocampus, left of parahippocampus, right of amygdala, right of thalamus are strongly correlated. In MMSE and DTI metrics correlations, the cingulum is strongly correlated with both AD and MD. The Amygdala, parahippocampus and temporal pole are in good correlations with FA, MD and AD. Conclusions: The DTI metrics on hippocampus correlate both age and MMSE well. In the correlation between MMSE, AD and MD, cingulum shows strong correlation. The quantitative DTI metrics results demonstrate the possibility of using these metrics as the clinical criteria of discriminating the progression of AD.References: [1]M. D. Denis Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging, vol. 13, p. 534, 2001. [2] I. N. C. Lawes, et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, vol. 39, pp. 62-79, 2008. [3] S. S. Mori and P. C. P. C. M. van Zijl, “Fiber tracking: principles and strategies a technical review,” NMR in Biomedicine, vol. 15, pp. 468-80, 2002. [4] G. K. Rohde, et al., “The adaptive bases algorithm for intensity-based nonrigid image registration,” Medical Imaging, IEEE Transactions, vol. 22, pp. 1470-1479, 2003. [5] C. F. Westin, “Processing and visualization for diffusion tensor MRI,” Medical Image Analysis, vol. 6, p. 93, 2002.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Alzheimer's \u0026 Dementia"},"translated_abstract":"are total 16 gradient direction images and 1 b0 image for DTI reconstruction and b0 1⁄4 1000. The total acquisition time is roughly 15 minutes. The AAL (Automated Anatomical Labeling) template was registered with 12 degree of freedom affine transformation and 2 stage of B-Spline mutual information based non-rigid registration of the grid size of 5mm and 2.5mm deformed to our AD subject spatial space[4]. The AD subject selection is complied with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th) criteria. There are total 12 AD subjects with age of 78.92 6 6.07,MMSE of 146 6.90 and education of 2.56 4.58 yrs. In eachmedial temporal ROI, we calculate the FA, MD and AD. Results: The calculation of FA andMD can be found in [5]. The AD is the largest eigen-value ?1. The AD can avoid the situation like when?1€ Eœ?2€ Eœ?3 the magnitude of FA is still large. Table 1 shows the correlation results between brain regions with age and regions with MMSE. In correlation with age, the MD of right of amygdala, right thalamus, right of hippocampus and left of parahippocampal correlate to age well (p \u0026lt; 0.05). The FA-age correlations in the right of post cingulum, left of parahippocampal and right of amygdala are well. The AD-age correlations in the right of hippocampus, left of parahippocampus, right of amygdala, right of thalamus are strongly correlated. In MMSE and DTI metrics correlations, the cingulum is strongly correlated with both AD and MD. The Amygdala, parahippocampus and temporal pole are in good correlations with FA, MD and AD. Conclusions: The DTI metrics on hippocampus correlate both age and MMSE well. In the correlation between MMSE, AD and MD, cingulum shows strong correlation. The quantitative DTI metrics results demonstrate the possibility of using these metrics as the clinical criteria of discriminating the progression of AD.References: [1]M. D. Denis Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging, vol. 13, p. 534, 2001. [2] I. N. C. Lawes, et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, vol. 39, pp. 62-79, 2008. [3] S. S. Mori and P. C. P. C. M. van Zijl, “Fiber tracking: principles and strategies a technical review,” NMR in Biomedicine, vol. 15, pp. 468-80, 2002. [4] G. K. Rohde, et al., “The adaptive bases algorithm for intensity-based nonrigid image registration,” Medical Imaging, IEEE Transactions, vol. 22, pp. 1470-1479, 2003. [5] C. F. Westin, “Processing and visualization for diffusion tensor MRI,” Medical Image Analysis, vol. 6, p. 93, 2002.","internal_url":"https://www.academia.edu/76960695/Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study","translated_internal_url":"","created_at":"2022-04-19T07:37:01.231-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Brain_MRI_predictors_of_global_and_domain_specific_cognitive_function_at_10_years_follow_up_ARIC_brain_MRI_study","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":386576,"name":"Domain Specificity","url":"https://www.academia.edu/Documents/in/Domain_Specificity"},{"id":441653,"name":"Cognitive Function","url":"https://www.academia.edu/Documents/in/Cognitive_Function"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="71997492"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/71997492/Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity"><img alt="Research paper thumbnail of Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/71997492/Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity">Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="71997492"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="71997492"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 71997492; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=71997492]").text(description); $(".js-view-count[data-work-id=71997492]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 71997492; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='71997492']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 71997492, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=71997492]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":71997492,"title":"Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity","translated_title":"","metadata":{"abstract":"The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure.","publisher":"Obesity","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"The purpose of this study was to determine whether baseline measures of hedonic hunger—the Power of Food Scale—and self‐control for food consumption—the Weight Efficacy Lifestyle Questionnaire—were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure.","internal_url":"https://www.academia.edu/71997492/Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity","translated_internal_url":"","created_at":"2022-02-20T12:31:24.517-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Functional_Brain_Networks_Unique_Patterns_with_Hedonic_Appetite_and_Confidence_to_Resist_Eating_in_Older_Adults_with_Obesity","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":3851,"name":"Obesity","url":"https://www.academia.edu/Documents/in/Obesity"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="67145583"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/67145583/Effects_of_a_Motor_Imagery_Task_on_Functional_Brain_Network_Community_Structure_in_Older_Adults_Data_from_the_Brain_Networks_and_Mobility_Function_B_NET_Study"><img alt="Research paper thumbnail of Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study" class="work-thumbnail" src="https://attachments.academia-assets.com/78073476/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/67145583/Effects_of_a_Motor_Imagery_Task_on_Functional_Brain_Network_Community_Structure_in_Older_Adults_Data_from_the_Brain_Networks_and_Mobility_Function_B_NET_Study">Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Elucidating the neural correlates of mobility is critical given the increasing population of olde...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the c...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a8c2b058100bcff1a9a9ae06bb66ca4e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":78073476,"asset_id":67145583,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/78073476/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="67145583"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="67145583"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67145583; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67145583]").text(description); $(".js-view-count[data-work-id=67145583]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67145583; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='67145583']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 67145583, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a8c2b058100bcff1a9a9ae06bb66ca4e" } } $('.js-work-strip[data-work-id=67145583]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":67145583,"title":"Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study","translated_title":"","metadata":{"abstract":"Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789422"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789422/Therapeutic_Instrumental_Music_Training_and_Motor_Imagery_in_Post_Stroke_Upper_Extremity_Rehabilitation_A_Randomized_Controlled_Pilot_Study"><img alt="Research paper thumbnail of Therapeutic Instrumental Music Training and Motor Imagery in Post-Stroke Upper-Extremity Rehabilitation: A Randomized-Controlled Pilot Study" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789422/Therapeutic_Instrumental_Music_Training_and_Motor_Imagery_in_Post_Stroke_Upper_Extremity_Rehabilitation_A_Randomized_Controlled_Pilot_Study">Therapeutic Instrumental Music Training and Motor Imagery in Post-Stroke Upper-Extremity Rehabilitation: A Randomized-Controlled Pilot Study</a></div><div class="wp-workCard_item"><span>Archives of Rehabilitation Research and Clinical Translation</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789422"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789422"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789422; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789422]").text(description); $(".js-view-count[data-work-id=61789422]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789422; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789422']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789422, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789419"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789419/Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress"><img alt="Research paper thumbnail of Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789419/Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress">Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress</a></div><div class="wp-workCard_item"><span>Journal of neuroimaging : official journal of the American Society of Neuroimaging</span><span>, Jan 10, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Post-traumatic stress disorder is associated with connectivity changes in the default mode, centr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sa...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789419"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789419"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789419; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789419]").text(description); $(".js-view-count[data-work-id=61789419]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789419; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789419']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789419, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789419]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789419,"title":"Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress","translated_title":"","metadata":{"abstract":"Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sa...","publication_date":{"day":10,"month":1,"year":2018,"errors":{}},"publication_name":"Journal of neuroimaging : official journal of the American Society of Neuroimaging"},"translated_abstract":"Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sa...","internal_url":"https://www.academia.edu/61789419/Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress","translated_internal_url":"","created_at":"2021-11-16T08:31:50.181-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Functional_Brain_Network_Changes_Following_Use_of_an_Allostatic_Closed_Loop_Acoustic_Stimulation_Neurotechnology_for_Military_Related_Traumatic_Stress","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":2639,"name":"Neuroimaging","url":"https://www.academia.edu/Documents/in/Neuroimaging"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789417"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789417/Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults"><img alt="Research paper thumbnail of Dynamic fMRI networks predict success in a behavioral weight loss program among older adults" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789417/Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults">Dynamic fMRI networks predict success in a behavioral weight loss program among older adults</a></div><div class="wp-workCard_item"><span>NeuroImage</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">More than one-third of adults in the United States are obese, with a higher prevalence among olde...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnet...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789417"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789417"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789417; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789417]").text(description); $(".js-view-count[data-work-id=61789417]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789417; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789417']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789417, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789417]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789417,"title":"Dynamic fMRI networks predict success in a behavioral weight loss program among older adults","translated_title":"","metadata":{"abstract":"More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnet...","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"NeuroImage"},"translated_abstract":"More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnet...","internal_url":"https://www.academia.edu/61789417/Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults","translated_internal_url":"","created_at":"2021-11-16T08:31:50.041-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Dynamic_fMRI_networks_predict_success_in_a_behavioral_weight_loss_program_among_older_adults","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":103260,"name":"Neuroimage","url":"https://www.academia.edu/Documents/in/Neuroimage"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789414"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789414/Beet_Root_Juice_An_Ergogenic_Aid_for_Exercise_and_the_Aging_Brain"><img alt="Research paper thumbnail of Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain" class="work-thumbnail" src="https://attachments.academia-assets.com/74737385/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789414/Beet_Root_Juice_An_Ergogenic_Aid_for_Exercise_and_the_Aging_Brain">Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain</a></div><div class="wp-workCard_item"><span>The journals of gerontology. Series A, Biological sciences and medical sciences</span><span>, Jan 9, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingest...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). Differences in second-order connections between the somatomotor cortex and insular cortex were also significant; the exercise + BRJ group (M = 3.28, SE = 0.167) had a significantly lower number of connections than exercise + placebo (M =...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="21b02380b3395894ca4b978b5dd147e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74737385,"asset_id":61789414,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74737385/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789414"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789414"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789414; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789414]").text(description); $(".js-view-count[data-work-id=61789414]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789414; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789414']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789414, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "21b02380b3395894ca4b978b5dd147e6" } } $('.js-work-strip[data-work-id=61789414]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789414,"title":"Beet Root Juice: An Ergogenic Aid for Exercise and the Aging Brain","translated_title":"","metadata":{"abstract":"Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). Differences in second-order connections between the somatomotor cortex and insular cortex were also significant; the exercise + BRJ group (M = 3.28, SE = 0.167) had a significantly lower number of connections than exercise + placebo (M =...","publication_date":{"day":9,"month":1,"year":2016,"errors":{}},"publication_name":"The journals of gerontology. Series A, Biological sciences and medical sciences"},"translated_abstract":"Exercise has positive neuroplastic effects on the aging brain. It has also been shown that ingestion of beet root juice (BRJ) increases blood flow to the brain and enhances exercise performance. Here, we examined whether there are synergistic effects of BRJ and exercise on neuroplasticity in the aging brain. Peak metabolic equivalent (MET) capacity and resting-state magnetic resonance imaging functional brain network organization are reported on 26 older (mean age = 65.4 years) participants randomly assigned to 6 weeks of exercise + BRJ or exercise + placebo. Somatomotor community structure consistency was significantly enhanced in the exercise + BRJ group following the intervention (MBRJ = -2.27, SE = 0.145, MPlacebo = -2.89, SE = 0.156, p = .007). Differences in second-order connections between the somatomotor cortex and insular cortex were also significant; the exercise + BRJ group (M = 3.28, SE = 0.167) had a significantly lower number of connections than exercise + placebo (M =...","internal_url":"https://www.academia.edu/61789414/Beet_Root_Juice_An_Ergogenic_Aid_for_Exercise_and_the_Aging_Brain","translated_internal_url":"","created_at":"2021-11-16T08:31:49.885-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":74737385,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/74737385/thumbnails/1.jpg","file_name":"glw219.pdf","download_url":"https://www.academia.edu/attachments/74737385/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Beet_Root_Juice_An_Ergogenic_Aid_for_Exe.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/74737385/glw219-libre.pdf?1637080506=\u0026response-content-disposition=attachment%3B+filename%3DBeet_Root_Juice_An_Ergogenic_Aid_for_Exe.pdf\u0026Expires=1733262663\u0026Signature=QKeaiY1TioYXgIf2b4rdRj2ya189J5sWNzmAKhAlcpwbuZotNPyhqXa6KXbUljplcg8ofZ10THN76C~WG~K~DwzVwhDjYjiA60UcqPXOB6Xx0v0APFwn-ZmRA1QzwF~widbXg01cDfhrDhpk~DjZAX61XjSl~IB7fxEPLfjK6014BXYA39Ydu9WfEMCXmQNXp0t0~sbMZgeAczGqlEPQI~CyHK59bqt6aQHKP0rRG9LA9xWSoscw7-12ixxrZrh9G5GNWYbyCIQvrOiPcrp2vNSIvcJcIj1wTFc4voMekvixShNSWutFyOc6n6qAwcVDMxNfm3DEM4QYCEr5y7K9RA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Beet_Root_Juice_An_Ergogenic_Aid_for_Exercise_and_the_Aging_Brain","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[{"id":74737385,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/74737385/thumbnails/1.jpg","file_name":"glw219.pdf","download_url":"https://www.academia.edu/attachments/74737385/download_file?st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&st=MTczMzI1OTA2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Beet_Root_Juice_An_Ergogenic_Aid_for_Exe.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/74737385/glw219-libre.pdf?1637080506=\u0026response-content-disposition=attachment%3B+filename%3DBeet_Root_Juice_An_Ergogenic_Aid_for_Exe.pdf\u0026Expires=1733262663\u0026Signature=QKeaiY1TioYXgIf2b4rdRj2ya189J5sWNzmAKhAlcpwbuZotNPyhqXa6KXbUljplcg8ofZ10THN76C~WG~K~DwzVwhDjYjiA60UcqPXOB6Xx0v0APFwn-ZmRA1QzwF~widbXg01cDfhrDhpk~DjZAX61XjSl~IB7fxEPLfjK6014BXYA39Ydu9WfEMCXmQNXp0t0~sbMZgeAczGqlEPQI~CyHK59bqt6aQHKP0rRG9LA9xWSoscw7-12ixxrZrh9G5GNWYbyCIQvrOiPcrp2vNSIvcJcIj1wTFc4voMekvixShNSWutFyOc6n6qAwcVDMxNfm3DEM4QYCEr5y7K9RA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging"},{"id":132020,"name":"Neuronal Plasticity","url":"https://www.academia.edu/Documents/in/Neuronal_Plasticity"},{"id":135185,"name":"Exercise","url":"https://www.academia.edu/Documents/in/Exercise"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":277717,"name":"Somatosensory Cortex","url":"https://www.academia.edu/Documents/in/Somatosensory_Cortex"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":1166928,"name":"Beta Vulgaris","url":"https://www.academia.edu/Documents/in/Beta_Vulgaris"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789411"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789411/The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers"><img alt="Research paper thumbnail of The Impacts of Pesticide and Nicotine Exposures on Functional Brain Networks in Latino Immigrant workers" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789411/The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers">The Impacts of Pesticide and Nicotine Exposures on Functional Brain Networks in Latino Immigrant workers</a></div><div class="wp-workCard_item"><span>Neurotoxicology</span><span>, Jan 2, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, su...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789411"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789411"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789411; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789411]").text(description); $(".js-view-count[data-work-id=61789411]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789411; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789411']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789411, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789411]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789411,"title":"The Impacts of Pesticide and Nicotine Exposures on Functional Brain Networks in Latino Immigrant workers","translated_title":"","metadata":{"abstract":"Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and ...","publication_date":{"day":2,"month":1,"year":2017,"errors":{}},"publication_name":"Neurotoxicology"},"translated_abstract":"Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and ...","internal_url":"https://www.academia.edu/61789411/The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers","translated_internal_url":"","created_at":"2021-11-16T08:31:49.748-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":39420980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Impacts_of_Pesticide_and_Nicotine_Exposures_on_Functional_Brain_Networks_in_Latino_Immigrant_workers","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":39420980,"first_name":"Jonathan","middle_initials":null,"last_name":"Burdette","page_name":"JonathanBurdette","domain_name":"independent","created_at":"2015-11-29T17:24:07.587-08:00","display_name":"Jonathan Burdette","url":"https://independent.academia.edu/JonathanBurdette"},"attachments":[],"research_interests":[{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging"},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability"},{"id":51688,"name":"Neurotoxicology","url":"https://www.academia.edu/Documents/in/Neurotoxicology"},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain"},{"id":85437,"name":"Pesticides","url":"https://www.academia.edu/Documents/in/Pesticides"},{"id":91360,"name":"Nicotine","url":"https://www.academia.edu/Documents/in/Nicotine"},{"id":120646,"name":"Acetylcholinesterase","url":"https://www.academia.edu/Documents/in/Acetylcholinesterase"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":380825,"name":"Oxygen","url":"https://www.academia.edu/Documents/in/Oxygen"},{"id":396914,"name":"Occupational Exposure","url":"https://www.academia.edu/Documents/in/Occupational_Exposure"},{"id":704401,"name":"Neural pathways","url":"https://www.academia.edu/Documents/in/Neural_pathways"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"},{"id":1559335,"name":"Cotinine","url":"https://www.academia.edu/Documents/in/Cotinine"},{"id":2519258,"name":"Butyrylcholinesterase","url":"https://www.academia.edu/Documents/in/Butyrylcholinesterase"},{"id":3789884,"name":"Pharmacology and pharmaceutical sciences","url":"https://www.academia.edu/Documents/in/Pharmacology_and_pharmaceutical_sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="61789408"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/61789408/Effects_of_supervised_exercise_and_dietary_nitrate_in_older_adults_with_controlled_hypertension_and_or_heart_failure_with_preserved_ejection_fraction"><img alt="Research paper thumbnail of Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61789408/Effects_of_supervised_exercise_and_dietary_nitrate_in_older_adults_with_controlled_hypertension_and_or_heart_failure_with_preserved_ejection_fraction">Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction</a></div><div class="wp-workCard_item"><span>Nitric oxide : biology and chemistry</span><span>, Jan 23, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in ind...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFp...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61789408"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61789408"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61789408; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61789408]").text(description); $(".js-view-count[data-work-id=61789408]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61789408; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61789408']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 61789408, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=61789408]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61789408,"title":"Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction","translated_title":"","metadata":{"abstract":"Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFp...","publication_date":{"day":23,"month":1,"year":2017,"errors":{}},"publication_name":"Nitric oxide : biology and chemistry"},"translated_abstract":"Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). 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A support vector machine with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter and white matter volume from 52 individuals who completed the intervention and a magnetic resonance imaging session. The support vector machine resulted in an average classification accuracy of 72.62% based on gray matter and white matter volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. 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