CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: functional magnetic resonance imaging (fMRI)</title> <meta name="description" content="Search results for: functional magnetic resonance imaging (fMRI)"> <meta name="keywords" content="functional magnetic resonance imaging (fMRI)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="functional magnetic resonance imaging (fMRI)" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="functional magnetic resonance imaging (fMRI)"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5485</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: functional magnetic resonance imaging (fMRI)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5485</span> The Functional Magnetic Resonance Imaging and the Consumer Behaviour: Reviewing Recent Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mikel%20Alonso%20L%C3%B3pez">Mikel Alonso López</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the first decade of the twenty-first century, advanced imaging techniques began to be applied for neuroscience research. The Functional Magnetic Resonance Imaging (fMRI) is one of the most important and most used research techniques for the investigation of emotions, because of its ease to observe the brain areas that oxygenate when performing certain tasks. In this research, we make a review about the main research carried out on the influence of the emotions in the decision-making process that is exposed by using the fMRI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=emotions" title=" emotions"> emotions</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20behaviour" title=" consumer behaviour"> consumer behaviour</a> </p> <a href="https://publications.waset.org/abstracts/48203/the-functional-magnetic-resonance-imaging-and-the-consumer-behaviour-reviewing-recent-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">479</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5484</span> Self-Supervised Pretraining on Sequences of Functional Magnetic Resonance Imaging Data for Transfer Learning to Brain Decoding Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sean%20Paulsen">Sean Paulsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Casey"> Michael Casey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised" title=" self-supervised"> self-supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20decoding" title=" brain decoding"> brain decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=multitask%20training" title=" multitask training"> multitask training</a> </p> <a href="https://publications.waset.org/abstracts/165380/self-supervised-pretraining-on-sequences-of-functional-magnetic-resonance-imaging-data-for-transfer-learning-to-brain-decoding-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5483</span> Activation of Mirror Neuron System Response to Drumming Training: A Functional Magnetic Resonance Imaging Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manal%20Alosaimi">Manal Alosaimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many rehabilitation strategies exist to aid persons with neurological disorders relearn motor skills through intensive training. Evidence supporting the theory that cortical areas involved in motor execution can be triggered by observing actions performed by others is attributed to the function of the mirror neuron system (MNS) indicates that activation of the MNS is associated with improvements in physical action and motor learning. Therefore, it is important to investigate the relationship between motor training (in this case, playing the drums) and the activation of the MNS. To achieve this, 15 healthy right-handed participants received drum-kit training for 21 weeks, during which time blood oxygen level-dependent (BOLD) signals were monitored in the brain using functional magnetic resonance imaging (fMRI). Participants were required to perform action–observation and action–execution fMRI tasks. The main results are that BOLD signals in classical regions of the MNS such as supramarginal gyri, inferior parietal lobule, and supplementary motor area increase significantly over the training period. Activation of these areas indicates that passive-observation of others performing these same skills may facilitate recovery of persons suffering from neurological disorders, and complement conventional rehabilitation programs that focus on action execution or intense training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fMRI" title="fMRI">fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=mirror%20neuron%20system" title=" mirror neuron system"> mirror neuron system</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroplasticity" title=" neuroplasticity"> neuroplasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=drumming" title=" drumming"> drumming</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=music" title=" music"> music</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20observation" title=" action observation"> action observation</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20execution" title=" action execution"> action execution</a> </p> <a href="https://publications.waset.org/abstracts/186635/activation-of-mirror-neuron-system-response-to-drumming-training-a-functional-magnetic-resonance-imaging-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">37</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5482</span> Understanding Cognitive Fatigue From FMRI Scans With Self-supervised Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Jaiswal">Ashish Jaiswal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashwin%20Ramesh%20Babu"> Ashwin Ramesh Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Zaki%20Zadeh"> Mohammad Zaki Zadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Fillia%20Makedon"> Fillia Makedon</a>, <a href="https://publications.waset.org/abstracts/search?q=Glenn%20Wylie"> Glenn Wylie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fMRI" title="fMRI">fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20imaging" title=" brain imaging"> brain imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title=" self-supervised learning"> self-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=contrastive%20learning" title=" contrastive learning"> contrastive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20fatigue" title=" cognitive fatigue"> cognitive fatigue</a> </p> <a href="https://publications.waset.org/abstracts/146445/understanding-cognitive-fatigue-from-fmri-scans-with-self-supervised-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146445.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5481</span> Linking Enhanced Resting-State Brain Connectivity with the Benefit of Desirable Difficulty to Motor Learning: A Functional Magnetic Resonance Imaging Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chien-Ho%20Lin">Chien-Ho Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Ching%20Yang"> Ho-Ching Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Barbara%20Knowlton"> Barbara Knowlton</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin-Leh%20Huang"> Shin-Leh Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming-Chang%20Chiang"> Ming-Chang Chiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Practicing motor tasks arranged in an interleaved order (interleaved practice, or IP) generally leads to better learning than practicing tasks in a repetitive order (repetitive practice, or RP), an example of how desirable difficulty during practice benefits learning. Greater difficulty during practice, e.g. IP, is associated with greater brain activity measured by higher blood-oxygen-level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) in the sensorimotor areas of the brain. In this study resting-state fMRI was applied to investigate whether increase in resting-state brain connectivity immediately after practice predicts the benefit of desirable difficulty to motor learning. 26 healthy adults (11M/15F, age = 23.3±1.3 years) practiced two sets of three sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, fMRI data were acquired in a resting state after practice. The resting-state fMRI data was decomposed using a group-level spatial independent component analysis (ICA), yielding 9 independent components (IC) matched to the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipito-temporal network, and the frontal network. A weighted resting-state functional connectivity (wRSFC) was then defined to incorporate information from within- and between-network brain connectivity. The within-network functional connectivity between a voxel and an IC was gauged by a z-score derived from the Fisher transformation of the IC map. The between-network connectivity was derived from the cross-correlation of time courses across all possible pairs of ICs, leading to a symmetric nc x nc matrix of cross-correlation coefficients, denoted by C = (pᵢⱼ). Here pᵢⱼ is the extremum of cross-correlation between ICs i and j; nc = 9 is the number of ICs. This component-wise cross-correlation matrix C was then projected to the voxel space, with the weights for each voxel set to the z-score that represents the above within-network functional connectivity. The wRSFC map incorporates the global characteristics of brain networks measured by the between-network connectivity, and the spatial information contained in the IC maps measured by the within-network connectivity. Pearson correlation analysis revealed that greater IP-minus-RP difference in wRSFC was positively correlated with the RP-minus-IP difference in the response time on Day 5, particularly in brain regions crucial for motor learning, such as the right dorsolateral prefrontal cortex (DLPFC), and the right premotor and supplementary motor cortices. This indicates that enhanced resting brain connectivity during the early phase of memory consolidation is associated with enhanced learning following interleaved practice, and as such wRSFC could be applied as a biomarker that measures the beneficial effects of desirable difficulty on motor sequence learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=desirable%20difficulty" title="desirable difficulty">desirable difficulty</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging" title=" functional magnetic resonance imaging"> functional magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=resting-state%20networks" title=" resting-state networks"> resting-state networks</a> </p> <a href="https://publications.waset.org/abstracts/81233/linking-enhanced-resting-state-brain-connectivity-with-the-benefit-of-desirable-difficulty-to-motor-learning-a-functional-magnetic-resonance-imaging-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81233.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5480</span> Development of a Pain Detector Using Microwave Radiometry Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nanditha%20Rajamani">Nanditha Rajamani</a>, <a href="https://publications.waset.org/abstracts/search?q=Anirudhaa%20R.%20Rao"> Anirudhaa R. Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=Divya%20Sriram"> Divya Sriram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the greatest difficulties in treating patients with pain is the highly subjective nature of pain sensation. The measurement of pain intensity is primarily dependent on the patient’s report, often with little physical evidence to provide objective corroboration. This is also complicated by the fact that there are only few and expensive existing technologies (Functional Magnetic Resonance Imaging-fMRI). The need is thus clear and urgent for a reliable, non-invasive, non-painful, objective, readily adoptable, and coefficient diagnostic platform that provides additional diagnostic information to supplement its current regime with more information to assist doctors in diagnosing these patients. Thus, our idea of developing a pain detector was conceived to take a step further the detection and diagnosis of chronic and acute pain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pain%20sensor" title="pain sensor">pain sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=microwave%20radiometery" title=" microwave radiometery"> microwave radiometery</a>, <a href="https://publications.waset.org/abstracts/search?q=pain%20sensation" title=" pain sensation"> pain sensation</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a> </p> <a href="https://publications.waset.org/abstracts/22639/development-of-a-pain-detector-using-microwave-radiometry-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22639.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">456</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5479</span> Application of Nanoparticles in Biomedical and MRI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raziyeh%20Mohammadi">Raziyeh Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At present, nanoparticles are used for various biomedical applications where they facilitate laboratory diagnostics and therapeutics. The performance of nanoparticles for biomedical applications is often assessed by their narrow size distribution, suitable magnetic saturation, and low toxicity effects. Superparamagnetic iron oxide nanoparticles have received great attention due to their applications as contrast agents for magnetic resonance imaging (MRI. (Processes in the tissue where the blood brain barrier is intact in this way shielded from the contact to this conventional contrast agent and will only reveal changes in the tissue if it involves an alteration in the vasculature. This technique is very useful for detecting tumors and can even be used for detecting metabolic functional alterations in the brain, such as epileptic activity.SPIONs have found application in Magnetic Resonance Imaging (MRI) and magnetic hyperthermia. Unlike bulk iron, SPIONs do not have remnant magnetization in the absence of the external magnetic field; therefore, a precise remote control over their action is possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nanoparticles" title="nanoparticles">nanoparticles</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical" title=" biomedical"> biomedical</a>, <a href="https://publications.waset.org/abstracts/search?q=iron%20oxide" title=" iron oxide"> iron oxide</a>, <a href="https://publications.waset.org/abstracts/search?q=spions" title=" spions"> spions</a> </p> <a href="https://publications.waset.org/abstracts/145609/application-of-nanoparticles-in-biomedical-and-mri" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5478</span> Monitoring Memories by Using Brain Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deniz%20Er%C3%A7elen">Deniz Erçelen</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zlem%20Selcuk%20Bozkurt"> Özlem Selcuk Bozkurt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The course of daily human life calls for the need for memories and remembering the time and place for certain events. Recalling memories takes up a substantial amount of time for an individual. Unfortunately, scientists lack the proper technology to fully understand and observe different brain regions that interact to form or retrieve memories. The hippocampus, a complex brain structure located in the temporal lobe, plays a crucial role in memory. The hippocampus forms memories as well as allows the brain to retrieve them by ensuring that neurons fire together. This process is called “neural synchronization.” Sadly, the hippocampus is known to deteriorate often with age. Proteins and hormones, which repair and protect cells in the brain, typically decline as the age of an individual increase. With the deterioration of the hippocampus, an individual becomes more prone to memory loss. Many memory loss starts off as mild but may evolve into serious medical conditions such as dementia and Alzheimer’s disease. In their quest to fully comprehend how memories work, scientists have created many different kinds of technology that are used to examine the brain and neural pathways. For instance, Magnetic Resonance Imaging - or MRI- is used to collect detailed images of an individual's brain anatomy. In order to monitor and analyze brain functions, a different version of this machine called Functional Magnetic Resonance Imaging - or fMRI- is used. The fMRI is a neuroimaging procedure that is conducted when the target brain regions are active. It measures brain activity by detecting changes in blood flow associated with neural activity. Neurons need more oxygen when they are active. The fMRI measures the change in magnetization between blood which is oxygen-rich and oxygen-poor. This way, there is a detectable difference across brain regions, and scientists can monitor them. Electroencephalography - or EEG - is also a significant way to monitor the human brain. The EEG is more versatile and cost-efficient than an fMRI. An EEG measures electrical activity which has been generated by the numerous cortical layers of the brain. EEG allows scientists to be able to record brain processes that occur after external stimuli. EEGs have a very high temporal resolution. This quality makes it possible to measure synchronized neural activity and almost precisely track the contents of short-term memory. Science has come a long way in monitoring memories using these kinds of devices, which have resulted in the inspections of neurons and neural pathways becoming more intense and detailed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain" title="brain">brain</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=hippocampus" title=" hippocampus"> hippocampus</a>, <a href="https://publications.waset.org/abstracts/search?q=memories" title=" memories"> memories</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20pathways" title=" neural pathways"> neural pathways</a>, <a href="https://publications.waset.org/abstracts/search?q=neurons" title=" neurons"> neurons</a> </p> <a href="https://publications.waset.org/abstracts/161066/monitoring-memories-by-using-brain-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161066.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">85</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5477</span> Diffusion Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy in Detecting Malignancy in Maxillofacial Lesions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Khalifa%20Zayet">Mohamed Khalifa Zayet</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20Belal%20Eiid"> Salma Belal Eiid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mushira%20Mohamed%20Dahaba"> Mushira Mohamed Dahaba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Malignant tumors may not be easily detected by traditional radiographic techniques especially in an anatomically complex area like maxillofacial region. At the same time, the advent of biological functional MRI was a significant footstep in the diagnostic imaging field. Objective: The purpose of this study was to define the malignant metabolic profile of maxillofacial lesions using diffusion MRI and magnetic resonance spectroscopy, as adjunctive aids for diagnosing of such lesions. Subjects and Methods: Twenty-one patients with twenty-two lesions were enrolled in this study. Both morphological and functional MRI scans were performed, where T1, T2 weighted images, diffusion-weighted MRI with four apparent diffusion coefficient (ADC) maps were constructed for analysis, and magnetic resonance spectroscopy with qualitative and semi-quantitative analyses of choline and lactate peaks were applied. Then, all patients underwent incisional or excisional biopsies within two weeks from MR scans. Results: Statistical analysis revealed that not all the parameters had the same diagnostic performance, where lactate had the highest areas under the curve (AUC) of 0.9 and choline was the lowest with insignificant diagnostic value. The best cut-off value suggested for lactate was 0.125, where any lesion above this value is supposed to be malignant with 90 % sensitivity and 83.3 % specificity. Despite that ADC maps had comparable AUCs still, the statistical measure that had the final say was the interpretation of likelihood ratio. As expected, lactate again showed the best combination of positive and negative likelihood ratios, whereas for the maps, ADC map with 500 and 1000 b-values showed the best realistic combination of likelihood ratios, however, with lower sensitivity and specificity than lactate. Conclusion: Diffusion weighted imaging and magnetic resonance spectroscopy are state-of-art in the diagnostic arena and they manifested themselves as key players in the differentiation process of orofacial tumors. The complete biological profile of malignancy can be decoded as low ADC values, high choline and/or high lactate, whereas that of benign entities can be translated as high ADC values, low choline and no lactate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diffusion%20magnetic%20resonance%20imaging" title="diffusion magnetic resonance imaging">diffusion magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20spectroscopy" title=" magnetic resonance spectroscopy"> magnetic resonance spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=malignant%20tumors" title=" malignant tumors"> malignant tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=maxillofacial" title=" maxillofacial"> maxillofacial</a> </p> <a href="https://publications.waset.org/abstracts/83030/diffusion-magnetic-resonance-imaging-and-magnetic-resonance-spectroscopy-in-detecting-malignancy-in-maxillofacial-lesions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5476</span> The Findings EEG-LORETA about Epilepsy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Maleki">Leila Maleki</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Esmali%20Kooraneh"> Ahmad Esmali Kooraneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20%20Taghi%20Derakhshi"> Hossein Taghi Derakhshi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural activity in the human brain starts from the early stages of prenatal development. This activity or signals generated by the brain are electrical in nature and represent not only the brain function but also the status of the whole body. At the present moment, three methods can record functional and physiological changes within the brain with high temporal resolution of neuronal interactions at the network level: the electroencephalogram (EEG), the magnet oencephalogram (MEG), and functional magnetic resonance imaging (fMRI); each of these has advantages and shortcomings. EEG recording with a large number of electrodes is now feasible in clinical practice. Multichannel EEG recorded from the scalp surface provides a very valuable but indirect information about the source distribution. However, deep electrode measurements yield more reliable information about the source locations، Intracranial recordings and scalp EEG are used with the source imaging techniques to determine the locations and strengths of the epileptic activity. As a source localization method, Low Resolution Electro-Magnetic Tomography (LORETA) is solved for the realistic geometry based on both forward methods, the Boundary Element Method (BEM) and the Finite Difference Method (FDM). In this paper, we review The findings EEG- LORETA about epilepsy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG-LORETA" title=" EEG-LORETA"> EEG-LORETA</a> </p> <a href="https://publications.waset.org/abstracts/32799/the-findings-eeg-loreta-about-epilepsy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32799.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">545</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5475</span> Neuromarketing: Discovering the Somathyc Marker in the Consumer´s Brain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mikel%20Alonso%20L%C3%B3pez">Mikel Alonso López</a>, <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20Francisca%20Blasco%20L%C3%B3pez"> María Francisca Blasco López</a>, <a href="https://publications.waset.org/abstracts/search?q=V%C3%ADctor%20Molero%20Ayala"> Víctor Molero Ayala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study explains the somatic marker theory of Antonio Damasio, which indicates that when making a decision, the stored or possible future scenarios (future memory) images allow people to feel for a moment what would happen when they make a choice, and how this is emotionally marked. This process can be conscious or unconscious. The development of new Neuromarketing techniques such as functional magnetic resonance imaging (fMRI), carries a greater understanding of how the brain functions and consumer behavior. In the results observed in different studies using fMRI, the evidence suggests that the somatic marker and future memories influence the decision-making process, adding a positive or negative emotional component to the options. This would mean that all decisions would involve a present emotional component, with a rational cost-benefit analysis that can be performed later. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotions" title="emotions">emotions</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=somatic%20marker" title=" somatic marker"> somatic marker</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%C2%B4s%20brain" title=" consumer´s brain"> consumer´s brain</a> </p> <a href="https://publications.waset.org/abstracts/44849/neuromarketing-discovering-the-somathyc-marker-in-the-consumers-brain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5474</span> Artificial Intelligence Based Analysis of Magnetic Resonance Signals for the Diagnosis of Tissue Abnormalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kapila%20Warnakulasuriya">Kapila Warnakulasuriya</a>, <a href="https://publications.waset.org/abstracts/search?q=Walimuni%20Janaka%20Mendis"> Walimuni Janaka Mendis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an artificial intelligence-based approach is developed to diagnose abnormal tissues in human or animal bodies by analyzing magnetic resonance signals. As opposed to the conventional method of generating an image from the magnetic resonance signals, which are then evaluated by a radiologist for the diagnosis of abnormalities, in the discussed approach, the magnetic resonance signals are analyzed by an artificial intelligence algorithm without having to generate or analyze an image. The AI-based program compares magnetic resonance signals with millions of possible magnetic resonance waveforms which can be generated from various types of normal tissues. Waveforms generated by abnormal tissues are then identified, and images of the abnormal tissues are generated with the possible location of them in the body for further diagnostic tests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance" title="magnetic resonance">magnetic resonance</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20waveform%20analysis" title=" magnetic waveform analysis"> magnetic waveform analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=abnormal%20tissues" title=" abnormal tissues"> abnormal tissues</a> </p> <a href="https://publications.waset.org/abstracts/164140/artificial-intelligence-based-analysis-of-magnetic-resonance-signals-for-the-diagnosis-of-tissue-abnormalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164140.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5473</span> Managing the Magnetic Protection of Workers in Magnetic Resonance Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safoin%20Aktaou">Safoin Aktaou</a>, <a href="https://publications.waset.org/abstracts/search?q=Aya%20Al%20Masri"> Aya Al Masri</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Guerchouche"> Kamel Guerchouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Malorie%20Martin"> Malorie Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=Fouad%20Maaloul"> Fouad Maaloul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: In the ‘Magnetic Resonance Imaging (MRI)’ department, all workers involved in preparing the patient, setting it up, tunnel cleaning, etc. are likely to be exposed to ‘ElectroMagnetic fields (EMF)’ emitted by the MRI device. Exposure to EMF can cause adverse radio-biological effects to workers. The purpose of this study is to propose an organizational process to manage and control EMF risks. Materials and methods: The study was conducted at seven MRI departments using machines with 1.5 and 3 Tesla magnetic fields. We assessed the exposure of each one by measuring the two electromagnetic fields (static and dynamic) at different distances from the MRI machine both inside and around the examination room. Measurement values were compared with British and American references (those of the UK's ‘Medicines and Healthcare Regulatory Agency (MHRA)’ and the ‘American Radiology Society (ACR)’). Results: Following the results of EMF measurements and their comparison with the recommendations of learned societies, a zoning system that adapts to needs of different MRI services across the country has been proposed. In effect, three risk areas have been identified within the MRI services. This has led to the development of a good practice guide related to the magnetic protection of MRI workers. Conclusion: The guide established by our study is a standard that allows MRI workers to protect themselves against the risk of electromagnetic fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparison%20with%20international%20references" title="comparison with international references">comparison with international references</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20of%20electromagnetic%20fields" title=" measurement of electromagnetic fields"> measurement of electromagnetic fields</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20protection%20of%20workers" title=" magnetic protection of workers"> magnetic protection of workers</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a> </p> <a href="https://publications.waset.org/abstracts/119063/managing-the-magnetic-protection-of-workers-in-magnetic-resonance-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119063.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5472</span> Valence Effects on Episodic Memory Retrieval Following Exposure to Arousing Stimuli in Young and Old Adults</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marianna%20Constantinou">Marianna Constantinou</a>, <a href="https://publications.waset.org/abstracts/search?q=Hana%20Burianova"> Hana Burianova</a>, <a href="https://publications.waset.org/abstracts/search?q=Ala%20Yankouskaya"> Ala Yankouskaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Episodic memory retrieval benefits from arousal, with better performance linked to arousing to-be-remembered information. However, the enduring impact of arousal on subsequent memory processes, particularly for non-arousing stimuli, remains unclear. This functional Magnetic Resonance Imaging (fMRI) study examined the effects of arousal on episodic memory processes in young and old adults, focusing on memory of neutral information following arousal exposure. Neural activity was assessed at three distinct timepoints: during exposure to arousing and non-arousing stimuli, memory consolidation (with or without arousing stimulus exposure), and during memory retrieval (with or without arousing stimulus exposure). Behavioural results show that across both age groups, participants performed worse when retrieving episodic memories about a video preceded by a highly arousing negative image. Our fMRI findings reveal three key findings: i) the extension of the influence of negative arousal beyond encoding; ii) the presence of this influence in both young and old adults; iii) and the differential treatment of positive arousal between these age groups. Our findings emphasise valence-specific effects on memory processes and support the enduring impact of negative arousal. We further propose an age-related alteration in the old adult brain in differentiating between positive and negative arousal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=episodic%20memory" title="episodic memory">episodic memory</a>, <a href="https://publications.waset.org/abstracts/search?q=ageing" title=" ageing"> ageing</a>, <a href="https://publications.waset.org/abstracts/search?q=fmri" title=" fmri"> fmri</a>, <a href="https://publications.waset.org/abstracts/search?q=arousal" title=" arousal"> arousal</a>, <a href="https://publications.waset.org/abstracts/search?q=valence" title=" valence"> valence</a> </p> <a href="https://publications.waset.org/abstracts/178934/valence-effects-on-episodic-memory-retrieval-following-exposure-to-arousing-stimuli-in-young-and-old-adults" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178934.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">63</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5471</span> Localization of Frontal and Temporal Speech Areas in Brain Tumor Patients by Their Structural Connections with Probabilistic Tractography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.Shukir">B.Shukir</a>, <a href="https://publications.waset.org/abstracts/search?q=H.Woo"> H.Woo</a>, <a href="https://publications.waset.org/abstracts/search?q=P.Barzo"> P.Barzo</a>, <a href="https://publications.waset.org/abstracts/search?q=D.Kis"> D.Kis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Preoperative brain mapping in tumors involving the speech areas has an important role to reduce surgical risks. Functional magnetic resonance imaging (fMRI) is the gold standard method to localize cortical speech areas preoperatively, but its availability in clinical routine is difficult. Diffusion MRI based probabilistic tractography is available in head MRI. It’s used to segment cortical subregions by their structural connectivity. In our study, we used probabilistic tractography to localize the frontal and temporal cortical speech areas. 15 patients with left frontal tumor were enrolled to our study. Speech fMRI and diffusion MRI acquired preoperatively. The standard automated anatomical labelling atlas 3 (AAL3) cortical atlas used to define 76 left frontal and 118 left temporal potential speech areas. 4 types of tractography were run according to the structural connection of these regions to the left arcuate fascicle (FA) to localize those cortical areas which have speech functions: 1, frontal through FA; 2, frontal with FA; 3, temporal to FA; 4, temporal with FA connections were determined. Thresholds of 1%, 5%, 10% and 15% applied. At each level, the number of affected frontal and temporal regions by fMRI and tractography were defined, the sensitivity and specificity were calculated. At the level of 1% threshold showed the best results. Sensitivity was 61,631,4% and 67,1523,12%, specificity was 87,210,4% and 75,611,37% for frontal and temporal regions, respectively. From our study, we conclude that probabilistic tractography is a reliable preoperative technique to localize cortical speech areas. However, its results are not feasible that the neurosurgeon rely on during the operation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20mapping" title="brain mapping">brain mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title=" brain tumor"> brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20tractography" title=" probabilistic tractography"> probabilistic tractography</a> </p> <a href="https://publications.waset.org/abstracts/165964/localization-of-frontal-and-temporal-speech-areas-in-brain-tumor-patients-by-their-structural-connections-with-probabilistic-tractography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165964.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">166</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5470</span> A Numerical Computational Method of MRI Static Magnetic Field for an Ergonomic Facility Design Guidelines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sherine%20Farrag">Sherine Farrag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic resonance imaging (MRI) presents safety hazards, with the general physical environment. The principal hazard of the MRI is the presence of static magnetic fields. Proper architectural design of MRI’s room ensure environment and health care staff safety. This research paper presents an easy approach for numerical computation of fringe static magnetic fields. Iso-gauss line of different MR intensities (0.3, 0.5, 1, 1.5 Tesla) was mapped and a polynomial function of the 7th degree was generated and tested. Matlab script was successfully applied for MRI SMF mapping. This method can be valid for any kind of commercial scanner because it requires only the knowledge of the MR scanner room map with iso-gauss lines. Results help to develop guidelines to guide healthcare architects to design of a safer Magnetic resonance imaging suite. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=designing%20MRI%20suite" title="designing MRI suite">designing MRI suite</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI%20safety" title=" MRI safety"> MRI safety</a>, <a href="https://publications.waset.org/abstracts/search?q=radiology%20occupational%20exposure" title=" radiology occupational exposure"> radiology occupational exposure</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20magnetic%20fields" title=" static magnetic fields "> static magnetic fields </a> </p> <a href="https://publications.waset.org/abstracts/12933/a-numerical-computational-method-of-mri-static-magnetic-field-for-an-ergonomic-facility-design-guidelines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12933.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5469</span> Cognitive Dysfunctioning and the Fronto-Limbic Network in Bipolar Disorder Patients: A Fmri Meta-Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahele%20Mesbah">Rahele Mesbah</a>, <a href="https://publications.waset.org/abstracts/search?q=Nic%20Van%20Der%20Wee"> Nic Van Der Wee</a>, <a href="https://publications.waset.org/abstracts/search?q=Manja%20Koenders"> Manja Koenders</a>, <a href="https://publications.waset.org/abstracts/search?q=Erik%20Giltay"> Erik Giltay</a>, <a href="https://publications.waset.org/abstracts/search?q=Albert%20Van%20Hemert"> Albert Van Hemert</a>, <a href="https://publications.waset.org/abstracts/search?q=Max%20De%20Leeuw"> Max De Leeuw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20functioning" title="cognitive functioning">cognitive functioning</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI%20analysis" title=" fMRI analysis"> fMRI analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=bipolar%20disorder" title=" bipolar disorder"> bipolar disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=fronto-limbic%20network" title=" fronto-limbic network"> fronto-limbic network</a> </p> <a href="https://publications.waset.org/abstracts/136510/cognitive-dysfunctioning-and-the-fronto-limbic-network-in-bipolar-disorder-patients-a-fmri-meta-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136510.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5468</span> End-to-End Pyramid Based Method for Magnetic Resonance Imaging Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omer%20Cahana">Omer Cahana</a>, <a href="https://publications.waset.org/abstracts/search?q=Ofer%20Levi"> Ofer Levi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maya%20Herman"> Maya Herman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title="magnetic resonance imaging">magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20reconstruction" title=" image reconstruction"> image reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=pyramid%20network" title=" pyramid network"> pyramid network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/150838/end-to-end-pyramid-based-method-for-magnetic-resonance-imaging-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5467</span> Evaluation of Longitudinal Relaxation Time (T1) of Bone Marrow in Lumbar Vertebrae of Leukaemia Patients Undergoing Magnetic Resonance Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20G.%20R.%20S.%20Perera">M. G. R. S. Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20S.%20Weerakoon"> B. S. Weerakoon</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20P.%20G.%20Sherminie"> L. P. G. Sherminie</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20L.%20Jayatilake"> M. L. Jayatilake</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20D.%20Jayasinghe"> R. D. Jayasinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Huang"> W. Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study was to measure and evaluate the Longitudinal Relaxation Times (T1) in bone marrow of an Acute Myeloid Leukaemia (AML) patient in order to explore the potential for a prognostic biomarker using Magnetic Resonance Imaging (MRI) which will be a non-invasive prognostic approach to AML. MR image data were collected in the DICOM format and MATLAB Simulink software was used in the image processing and data analysis. For quantitative MRI data analysis, Region of Interests (ROI) on multiple image slices were drawn encompassing vertebral bodies of L3, L4, and L5. T1 was evaluated using the T1 maps obtained. The estimated bone marrow mean value of T1 was 790.1 (ms) at 3T. However, the reported T1 value of healthy subjects is significantly (946.0 ms) higher than the present finding. This suggests that the T1 for bone marrow can be considered as a potential prognostic biomarker for AML patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acute%20myeloid%20leukaemia" title="acute myeloid leukaemia">acute myeloid leukaemia</a>, <a href="https://publications.waset.org/abstracts/search?q=longitudinal%20relaxation%20time" title=" longitudinal relaxation time"> longitudinal relaxation time</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=prognostic%20biomarker." title=" prognostic biomarker."> prognostic biomarker.</a> </p> <a href="https://publications.waset.org/abstracts/12985/evaluation-of-longitudinal-relaxation-time-t1-of-bone-marrow-in-lumbar-vertebrae-of-leukaemia-patients-undergoing-magnetic-resonance-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12985.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">531</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5466</span> Multimodal Integration of EEG, fMRI and Positron Emission Tomography Data Using Principal Component Analysis for Prognosis in Coma Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Denis%20Jordan">Denis Jordan</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Golkowski"> Daniel Golkowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathias%20Lukas"> Mathias Lukas</a>, <a href="https://publications.waset.org/abstracts/search?q=Katharina%20Merz"> Katharina Merz</a>, <a href="https://publications.waset.org/abstracts/search?q=Caroline%20Mlynarcik"> Caroline Mlynarcik</a>, <a href="https://publications.waset.org/abstracts/search?q=Max%20Maurer"> Max Maurer</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentin%20Riedl"> Valentin Riedl</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Foerster"> Stefan Foerster</a>, <a href="https://publications.waset.org/abstracts/search?q=Eberhard%20F.%20Kochs"> Eberhard F. Kochs</a>, <a href="https://publications.waset.org/abstracts/search?q=Andreas%20Bender"> Andreas Bender</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruediger%20Ilg"> Ruediger Ilg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coma%20states%20and%20prognosis" title="coma states and prognosis">coma states and prognosis</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging" title=" functional magnetic resonance imaging"> functional magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=positron%20emission%20tomography" title=" positron emission tomography"> positron emission tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/55193/multimodal-integration-of-eeg-fmri-and-positron-emission-tomography-data-using-principal-component-analysis-for-prognosis-in-coma-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55193.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">339</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5465</span> Integrating Dynamic Brain Connectivity and Transcriptomic Imaging in Major Depressive Disorder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qingjin%20Liu">Qingjin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinpeng%20Niu"> Jinpeng Niu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kangjia%20Chen"> Kangjia Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiao%20Li"> Jiao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Huafu%20Chen"> Huafu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Liao"> Wei Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Functional connectomics is essential in cognitive science and neuropsychiatry, offering insights into the brain's complex network structures and dynamic interactions. Although neuroimaging has uncovered functional connectivity issues in Major Depressive Disorder (MDD) patients, the dynamic shifts in connectome topology and their link to gene expression are yet to be fully understood. To explore the differences in dynamic connectome topology between MDD patients and healthy individuals, we conducted an extensive analysis of resting-state functional magnetic resonance imaging (fMRI) data from 434 participants (226 MDD patients and 208 controls). We used multilayer network models to evaluate brain module dynamics and examined the association between whole-brain gene expression and dynamic module variability in MDD using publicly available transcriptomic data. Our findings revealed that compared to healthy individuals, MDD patients showed lower global mean values and higher standard deviations, indicating unstable patterns and increased regional differentiation. Notably, MDD patients exhibited more frequent module switching, primarily within the executive control network (ECN), particularly in the left dorsolateral prefrontal cortex and right fronto-insular regions, whereas the default mode network (DMN), including the superior frontal gyrus, temporal lobe, and right medial prefrontal cortex, displayed lower variability. These brain dynamics predicted the severity of depressive symptoms. Analyzing human brain gene expression data, we found that the spatial distribution of MDD-related gene expression correlated with dynamic module differences. Cell type-specific gene analyses identified oligodendrocytes (OPCs) as major contributors to the transcriptional relationships underlying module variability in MDD. To the best of our knowledge, this is the first comprehensive description of altered brain module dynamics in MDD patients linked to depressive symptom severity and changes in whole-brain gene expression profiles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=major%20depressive%20disorder" title="major depressive disorder">major depressive disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=module%20dynamics" title=" module dynamics"> module dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=transcriptomic" title=" transcriptomic"> transcriptomic</a> </p> <a href="https://publications.waset.org/abstracts/190173/integrating-dynamic-brain-connectivity-and-transcriptomic-imaging-in-major-depressive-disorder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190173.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">25</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5464</span> Neural Correlates of Decision-Making Under Ambiguity and Conflict </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helen%20Pushkarskaya">Helen Pushkarskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Smithson"> Michael Smithson</a>, <a href="https://publications.waset.org/abstracts/search?q=Jane%20E.%20Joseph"> Jane E. Joseph</a>, <a href="https://publications.waset.org/abstracts/search?q=Christine%20Corbly"> Christine Corbly</a>, <a href="https://publications.waset.org/abstracts/search?q=Ifat%20Levy"> Ifat Levy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Studies of decision making under uncertainty generally focus on imprecise information about outcome probabilities (“ambiguity”). It is not clear, however, whether conflicting information about outcome probabilities affects decision making in the same manner as ambiguity does. Here we combine functional Magnetic Resonance Imaging (fMRI) and a simple gamble design to study this question. In this design, the levels of ambiguity and conflict are parametrically varied, and ambiguity and conflict gambles are matched on both expected value and variance. Behaviorally, participants avoided conflict more than ambiguity, and attitudes toward ambiguity and conflict did not correlate across subjects. Neurally, regional brain activation was differentially modulated by ambiguity level and aversion to ambiguity and by conflict level and aversion to conflict. Activation in the medial prefrontal cortex was correlated with the level of ambiguity and with ambiguity aversion, whereas activation in the ventral striatum was correlated with the level of conflict and with conflict aversion. This novel double dissociation indicates that decision makers process imprecise and conflicting information differently, a finding that has important implications for basic and clinical research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity" title=" ambiguity"> ambiguity</a>, <a href="https://publications.waset.org/abstracts/search?q=conflict" title=" conflict"> conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a> </p> <a href="https://publications.waset.org/abstracts/27681/neural-correlates-of-decision-making-under-ambiguity-and-conflict" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27681.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">564</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5463</span> Neural Correlates of Diminished Humor Comprehension in Schizophrenia: A Functional Magnetic Resonance Imaging Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Przemys%C5%82aw%20Adamczyk">Przemysław Adamczyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Miros%C5%82aw%20Wyczesany"> Mirosław Wyczesany</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Domagalik"> Aleksandra Domagalik</a>, <a href="https://publications.waset.org/abstracts/search?q=Artur%20Daren"> Artur Daren</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamil%20Cepuch"> Kamil Cepuch</a>, <a href="https://publications.waset.org/abstracts/search?q=Piotr%20B%C5%82%C4%85dzi%C5%84ski"> Piotr Błądziński</a>, <a href="https://publications.waset.org/abstracts/search?q=Tadeusz%20Marek"> Tadeusz Marek</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrzej%20Cechnicki"> Andrzej Cechnicki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed at evaluation of neural correlates of humor comprehension impairments observed in schizophrenia. To investigate the nature of this deficit in schizophrenia and to localize cortical areas involved in humor processing we used functional magnetic resonance imaging (fMRI). The study included chronic schizophrenia outpatients (SCH; n=20), and sex, age and education level matched healthy controls (n=20). The task consisted of 60 stories (setup) of which 20 had funny, 20 nonsensical and 20 neutral (not funny) punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible (yes/no) and how funny it was (1-9 Likert-type scale). fMRI was performed on a 3T scanner (Magnetom Skyra, Siemens) using 32-channel head coil. Three contrasts in accordance with the three stages of humor processing were analyzed in both groups: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution; funny vs neutral - elaboration. Additionally, parametric modulation analysis was performed using both subjective ratings separately in order to further differentiate the areas involved in incongruity resolution processing. Statistical analysis for behavioral data used U Mann-Whitney test and Bonferroni’s correction, fMRI data analysis utilized whole-brain voxel-wise t-tests with 10-voxel extent threshold and with Family Wise Error (FWE) correction at alpha = 0.05, or uncorrected at alpha = 0.001. Between group comparisons revealed that the SCH subjects had attenuated activation in: the right superior temporal gyrus in case of irresolvable incongruity processing of nonsensical puns (nonsensical > neutral); the left medial frontal gyrus in case of incongruity resolution processing of funny puns (funny > nonsensical) and the interhemispheric ACC in case of elaboration of funny puns (funny > neutral). Additionally, the SCH group revealed weaker activation during funniness ratings in the left ventro-medial prefrontal cortex, the medial frontal gyrus, the angular and the supramarginal gyrus, and the right temporal pole. In comprehension ratings the SCH group showed suppressed activity in the left superior and medial frontal gyri. Interestingly, these differences were accompanied by protraction of time in both types of rating responses in the SCH group, a lower level of comprehension for funny punchlines and a higher funniness for absurd punchlines. Presented results indicate that, in comparison to healthy controls, schizophrenia is characterized by difficulties in humor processing revealed by longer reaction times, impairments of understanding jokes and finding nonsensical punchlines more funny. This is accompanied by attenuated brain activations, especially in the left fronto-parietal and the right temporal cortices. Disturbances of the humor processing seem to be impaired at the all three stages of the humor comprehension process, from incongruity detection, through its resolution to elaboration. The neural correlates revealed diminished neural activity of the schizophrenia brain, as compared with the control group. The study was supported by the National Science Centre, Poland (grant no 2014/13/B/HS6/03091). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20skills" title="communication skills">communication skills</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging" title=" functional magnetic resonance imaging"> functional magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=humor" title=" humor"> humor</a>, <a href="https://publications.waset.org/abstracts/search?q=schizophrenia" title=" schizophrenia"> schizophrenia</a> </p> <a href="https://publications.waset.org/abstracts/60165/neural-correlates-of-diminished-humor-comprehension-in-schizophrenia-a-functional-magnetic-resonance-imaging-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60165.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">213</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5462</span> An Ultra-Low Output Impedance Power Amplifier for Tx Array in 7-Tesla Magnetic Resonance Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20Abuelhaija">Ashraf Abuelhaija</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus%20Solbach"> Klaus Solbach</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Ultra high-field MRI scanners (3T and higher), parallel RF transmission techniques using multiple RF chains with multiple transmit elements are a promising approach to overcome the high-field MRI challenges in terms of inhomogeneity in the RF magnetic field and SAR. However, mutual coupling between the transmit array elements disturbs the desirable independent control of the RF waveforms for each element. This contribution demonstrates a 18 dB improvement of decoupling (isolation) performance due to the very low output impedance of our 1 kW power amplifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EM%20coupling" title="EM coupling">EM coupling</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-element%20isolation" title=" inter-element isolation"> inter-element isolation</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging%20%28mri%29" title=" magnetic resonance imaging (mri)"> magnetic resonance imaging (mri)</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20transmit" title=" parallel transmit"> parallel transmit</a> </p> <a href="https://publications.waset.org/abstracts/31126/an-ultra-low-output-impedance-power-amplifier-for-tx-array-in-7-tesla-magnetic-resonance-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">495</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5461</span> Automatic Processing of Trauma-Related Visual Stimuli in Female Patients Suffering From Post-Traumatic Stress Disorder after Interpersonal Traumatization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theresa%20Slump">Theresa Slump</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Neumeister"> Paula Neumeister</a>, <a href="https://publications.waset.org/abstracts/search?q=Katharina%20Feldker"> Katharina Feldker</a>, <a href="https://publications.waset.org/abstracts/search?q=Carina%20Y.%20Heitmann"> Carina Y. Heitmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Straube"> Thomas Straube</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A characteristic feature of post-traumatic stress disorder (PTSD) is the automatic processing of disorder-specific stimuli that expresses itself in intrusive symptoms such as intense physical and psychological reactions to trauma-associated stimuli. That automatic processing plays an essential role in the development and maintenance of symptoms. The aim of our study was, therefore, to investigate the behavioral and neural correlates of automatic processing of trauma-related stimuli in PTSD. Although interpersonal traumatization is a form of traumatization that often occurs, it has not yet been sufficiently studied. That is why, in our study, we focused on patients suffering from interpersonal traumatization. While previous imaging studies on PTSD mainly used faces, words, or generally negative visual stimuli, our study presented complex trauma-related and neutral visual scenes. We examined 19 female subjects suffering from PTSD and examined 19 healthy women as a control group. All subjects did a geometric comparison task while lying in a functional-magnetic-resonance-imaging (fMRI) scanner. Trauma-related scenes and neutral visual scenes that were not relevant to the task were presented while the subjects were doing the task. Regarding the behavioral level, there were not any significant differences between the task performance of the two groups. Regarding the neural level, the PTSD patients showed significant hyperactivation of the hippocampus for task-irrelevant trauma-related stimuli versus neutral stimuli when compared with healthy control subjects. Connectivity analyses revealed altered connectivity between the hippocampus and other anxiety-related areas in PTSD patients, too. Overall, those findings suggest that fear-related areas are involved in PTSD patients' processing of trauma-related stimuli even if the stimuli that were used in the study were task-irrelevant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=post-traumatic%20stress%20disorder" title="post-traumatic stress disorder">post-traumatic stress disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20processing" title=" automatic processing"> automatic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=hippocampus" title=" hippocampus"> hippocampus</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging" title=" functional magnetic resonance imaging"> functional magnetic resonance imaging</a> </p> <a href="https://publications.waset.org/abstracts/140162/automatic-processing-of-trauma-related-visual-stimuli-in-female-patients-suffering-from-post-traumatic-stress-disorder-after-interpersonal-traumatization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140162.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5460</span> Task Based Functional Connectivity within Reward Network in Food Image Viewing Paradigm Using Functional MRI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preetham%20Shankapal">Preetham Shankapal</a>, <a href="https://publications.waset.org/abstracts/search?q=Jill%20King"> Jill King</a>, <a href="https://publications.waset.org/abstracts/search?q=Kori%20Murray"> Kori Murray</a>, <a href="https://publications.waset.org/abstracts/search?q=Corby%20Martin"> Corby Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Giselman"> Paula Giselman</a>, <a href="https://publications.waset.org/abstracts/search?q=Jason%20Hicks"> Jason Hicks</a>, <a href="https://publications.waset.org/abstracts/search?q=Owen%20Carmicheal"> Owen Carmicheal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fMRI" title="fMRI">fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20connectivity" title=" functional connectivity"> functional connectivity</a>, <a href="https://publications.waset.org/abstracts/search?q=task-based" title=" task-based"> task-based</a>, <a href="https://publications.waset.org/abstracts/search?q=beta%20series%20correlation" title=" beta series correlation"> beta series correlation</a> </p> <a href="https://publications.waset.org/abstracts/71145/task-based-functional-connectivity-within-reward-network-in-food-image-viewing-paradigm-using-functional-mri" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71145.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">270</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5459</span> Classifications of Neuroscientific-Radiological Findings on “Practicing” in Mathematics Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Felicitas%20Pielsticker">Felicitas Pielsticker</a>, <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Pielsticker"> Christoph Pielsticker</a>, <a href="https://publications.waset.org/abstracts/search?q=Ingo%20Witzke"> Ingo Witzke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many people know ‘Mathematics needs practice!’ statement or similar ones from their mathematics lessons. It seems important to practice when learning mathematics. At the same time, it also seems important to practice how to learn mathematics. This paper places neuroscientific-radiological findings on “practicing” while learning mathematics in a context of mathematics education. To accomplish this, we use a literature-based discussion of our case study on practice. We want to describe neuroscientific-radiological findings in the context of mathematics education and point out stimulating connections between both perspectives. From a connective perspective we expect incentives that lead discussions in future research in the field of mathematics education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging" title="functional magnetic resonance imaging">functional magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics%20learning" title=" mathematics learning"> mathematics learning</a>, <a href="https://publications.waset.org/abstracts/search?q=practicing" title=" practicing"> practicing</a> </p> <a href="https://publications.waset.org/abstracts/132925/classifications-of-neuroscientific-radiological-findings-on-practicing-in-mathematics-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5458</span> Medical Experience: Usability Testing of Displaying Computed Tomography Scans and Magnetic Resonance Imaging in Virtual and Augmented Reality for Accurate Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alyona%20Gencheva">Alyona Gencheva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most common way to study diagnostic results is using specialized programs at a stationary workplace. Magnetic Resonance Imaging is presented in a two-dimensional (2D) format, and Computed Tomography sometimes looks like a three-dimensional (3D) model that can be interacted with. The main idea of the research is to compare ways of displaying diagnostic results in virtual reality that can help a surgeon during or before an operation in augmented reality. During the experiment, the medical staff examined liver vessels in the abdominal area and heart boundaries. The search time and detection accuracy were measured on black-and-white and coloured scans. Usability testing in virtual reality shows convenient ways of interaction like hand input, voice activation, displaying risk to the patient, and the required number of scans. The results of the experiment will be used in the new C# program based on Magic Leap technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality" title="augmented reality">augmented reality</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title=" computed tomography"> computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=magic%20leap" title=" magic leap"> magic leap</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=usability%20testing" title=" usability testing"> usability testing</a>, <a href="https://publications.waset.org/abstracts/search?q=VTE%20risk" title=" VTE risk"> VTE risk</a> </p> <a href="https://publications.waset.org/abstracts/163957/medical-experience-usability-testing-of-displaying-computed-tomography-scans-and-magnetic-resonance-imaging-in-virtual-and-augmented-reality-for-accurate-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163957.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">112</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5457</span> The Value of Dynamic Magnetic Resonance Defecography in Assessing the Severity of Defecation Disorders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ge%20Sun">Ge Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Monika%20Trzpis"> Monika Trzpis</a>, <a href="https://publications.waset.org/abstracts/search?q=Robbert%20J.%20de%20Haas"> Robbert J. de Haas</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20M.%20A.%20Broens"> Paul M. A. Broens</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Dynamic magnetic resonance defecography is frequently used to assess defecation disorders. We aimed to investigate the usefulness of dynamic magnetic resonance defecography for assessing the severity of defecation disorder. Methods: We included patients retrospectively from our tertiary referral hospital who had undergone dynamic magnetic resonance defecography, anorectal manometry, and anal electrical sensitivity tests to assess defecation disorders between 2014 and 2020. The primary outcome was the association between the dynamic magnetic resonance defecography variables and the severity of defecation disorders. We assessed the severity of fecal incontinence and constipation with the Wexner incontinence and Agachan constipation scores. Results: Out of the 32 patients included, 24 completed the defecation questionnaire. During defecation, the M line length at magnetic resonance correlated with the Agachan score (r = 0.45, p = 0.03) and was associated with anal sphincter pressure (r=0.39, p=0.03) just before defecation. During rest and squeezing, the H line length at imaging correlated with the Wexner incontinence score (r=0.49, p=0.01 and r=0.69, p< 0.001, respectively). H line length also correlated positively with the anal electrical sensation threshold during squeezing (r=0.50, p=0.004) and during rest (r= 0.42, p=0.02). Conclusions: The M and H line lengths at dynamic magnetic resonance defecography can be used to assess the severity of constipation and fecal incontinence respectively and reflect anatomic changes of the pelvic floor. However, as these anatomic changes are generally late-stage and irreversible, anal manometry seems a better diagnostic approach to assess early and potentially reversible changes in patients with defecation disorders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defecation%20disorders" title="defecation disorders">defecation disorders</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20magnetic%20resonance%20defecography" title=" dynamic magnetic resonance defecography"> dynamic magnetic resonance defecography</a>, <a href="https://publications.waset.org/abstracts/search?q=anorectal%20manometry" title=" anorectal manometry"> anorectal manometry</a>, <a href="https://publications.waset.org/abstracts/search?q=anal%20electrical%20sensitivity%20tests" title=" anal electrical sensitivity tests"> anal electrical sensitivity tests</a>, <a href="https://publications.waset.org/abstracts/search?q=H%20line" title=" H line"> H line</a>, <a href="https://publications.waset.org/abstracts/search?q=M%20%20line" title=" M line"> M line</a> </p> <a href="https://publications.waset.org/abstracts/158300/the-value-of-dynamic-magnetic-resonance-defecography-in-assessing-the-severity-of-defecation-disorders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158300.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5456</span> Magnetic Nanoparticles for Cancer Therapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sachinkumar%20Patil">Sachinkumar Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonali%20Patil"> Sonali Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Shitalkumar%20Patil"> Shitalkumar Patil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nanoparticles played important role in the biomedicine. New advanced methods having great potential apllication in the diagnosis and therapy of cancer. Now a day’s magnetic nanoparticles used in cancer therapy. Cancer is the major disease causes death. Magnetic nanoparticles show response to the magnetic field on the basis of this property they are used in cancer therapy. Cancer treated with hyperthermia by using magnetic nanoparticles it is unconventional but more safe and effective method. Magnetic nanoparticles prepared by using different innovative techniques that makes particles in uniform size and desired effect. Magnetic nanoparticles already used as contrast media in magnetic resonance imaging. A magnetic nanoparticle has been great potential application in cancer diagnosis and treatment as well as in gene therapy. In this review we will discuss the progress in cancer therapy based on magnetic nanoparticles, mainly including magnetic hyperthermia, synthesis and characterization of magnetic nanoparticles, mechanism of magnetic nanoparticles and application of magnetic nanoparticles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20nanoparticles" title="magnetic nanoparticles">magnetic nanoparticles</a>, <a href="https://publications.waset.org/abstracts/search?q=synthesis" title=" synthesis"> synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=characterization" title=" characterization"> characterization</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20therapy" title=" cancer therapy"> cancer therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperthermia" title=" hyperthermia"> hyperthermia</a>, <a href="https://publications.waset.org/abstracts/search?q=application" title=" application"> application</a> </p> <a href="https://publications.waset.org/abstracts/31421/magnetic-nanoparticles-for-cancer-therapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">639</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=182">182</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=183">183</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=functional%20magnetic%20resonance%20imaging%20%28fMRI%29&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>