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histology</title> <meta name="description" content="Search results for: brain histology"> <meta name="keywords" content="brain histology"> <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 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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="brain histology"> <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> 1284</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: brain histology</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1284</span> Education and Learning in Indonesia to Refer to the Democratic and Humanistic Learning System in Finland</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nur%20Sofi%20Hidayah">Nur Sofi Hidayah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratih%20Tri%20Purwatiningsih"> Ratih Tri Purwatiningsih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Learning is a process attempts person to obtain a new behavior changes as a whole, as a result of his own experience in the interaction with the environment. Learning involves our brain to think, while the ability of the brain to each student's performance is different. To obtain optimal learning results then need time to learn the exact hour that the brain's performance is not too heavy. Referring to the learning system in Finland which apply 45 minutes to learn and a 15-minute break is expected to be the brain work better, with the rest of the brain, the brain will be more focused and lessons can be absorbed well. It can be concluded that learning in this way students learn with brain always fresh and the best possible use of the time, but it can make students not saturated in a lesson. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning" title="learning">learning</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20hours%20brain" title=" working hours brain"> working hours brain</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20efficient%20learning" title=" time efficient learning"> time efficient learning</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20hours%20in%20the%20brain%20receive%20stimulus." title=" working hours in the brain receive stimulus."> working hours in the brain receive stimulus.</a> </p> <a href="https://publications.waset.org/abstracts/39794/education-and-learning-in-indonesia-to-refer-to-the-democratic-and-humanistic-learning-system-in-finland" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39794.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">397</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">1283</span> Human Brain Organoids-on-a-Chip Systems to Model Neuroinflammation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feng%20Guo">Feng Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human brain organoids, 3D brain tissue cultures derived from human pluripotent stem cells, hold promising potential in modeling neuroinflammation for a variety of neurological diseases. However, challenges remain in generating standardized human brain organoids that can recapitulate key physiological features of a human brain. Here, this study presents a series of organoids-on-a-chip systems to generate better human brain organoids and model neuroinflammation. By employing 3D printing and microfluidic 3D cell culture technologies, the study’s systems enable the reliable, scalable, and reproducible generation of human brain organoids. Compared with conventional protocols, this study’s method increased neural progenitor proliferation and reduced heterogeneity of human brain organoids. As a proof-of-concept application, the study applied this method to model substance use disorders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20brain%20organoids" title="human brain organoids">human brain organoids</a>, <a href="https://publications.waset.org/abstracts/search?q=microfluidics" title=" microfluidics"> microfluidics</a>, <a href="https://publications.waset.org/abstracts/search?q=organ-on-a-chip" title=" organ-on-a-chip"> organ-on-a-chip</a>, <a href="https://publications.waset.org/abstracts/search?q=neuroinflammation" title=" neuroinflammation"> neuroinflammation</a> </p> <a href="https://publications.waset.org/abstracts/138112/human-brain-organoids-on-a-chip-systems-to-model-neuroinflammation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138112.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">202</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">1282</span> Brainwave Classification for Brain Balancing Index (BBI) via 3D EEG Model Using k-NN Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Fuad">N. Fuad</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Taib"> M. N. Taib</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Jailani"> R. Jailani</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Marwan"> M. E. Marwan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20spectral%20density" title="power spectral density">power spectral density</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20EEG%20model" title=" 3D EEG model"> 3D EEG model</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20balancing" title=" brain balancing"> brain balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=kNN" title=" kNN"> kNN</a> </p> <a href="https://publications.waset.org/abstracts/11285/brainwave-classification-for-brain-balancing-index-bbi-via-3d-eeg-model-using-k-nn-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11285.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">487</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">1281</span> Evaluation of Diagnostic Values of Culture, Rapid Urease Test, and Histopathology in the Diagnosis of Helicobacter pylori Infection and in vitro Effects of Various Antimicrobials against Helicobacter pylori</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Recep%20Kesli">Recep Kesli</a>, <a href="https://publications.waset.org/abstracts/search?q=Huseyin%20Bilgin"> Huseyin Bilgin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasar%20Unlu"> Yasar Unlu</a>, <a href="https://publications.waset.org/abstracts/search?q=Gokhan%20Gungor"> Gokhan Gungor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aim: The aim of this study, was to investigate the presence of Helicobacter pylori (H. pylori) infection by culture, histology, and RUT (Rapid Urease Test) in gastric antrum biopsy samples taken from patients presented with dyspeptic complaints and to determine resistance rates of amoxicillin, clarithromycin, levofloxacin and metronidazole against the H. pylori strains by E-test. Material and Methods: A total of 278 patients who admitted to Konya Education and Research Hospital Department of Gastroenterology with dyspeptic complaints, between January 2011-July 2013, were included in the study. Microbiological and histopathological examinations of biopsy specimens taken from antrum and corpus regions were performed. The presence of H. pylori in biopsy samples was investigated by culture (Portagerm pylori-PORT PYL, Pylori agar-PYL, GENbox microaer, bioMerieux, France), histology (Giemsa, Hematoxylin and Eosin staining), and RUT(CLOtest, Cimberly-Clark, USA). Antimicrobial resistance of isolates against amoxicillin, clarithromycin, levofloxacin, and metronidazole was determined by E-test method (bioMerieux, France). As a gold standard in the diagnosis of H. pylori; it was accepted that the culture method alone was positive or both histology and RUT were positive together. Sensitivity and specificity for histology and RUT were calculated by taking the culture as a gold standard. Sensitivity and specificity for culture were also calculated by taking the co-positivity of both histology and RUT as a gold standard. Results: H. pylori was detected in 140 of 278 of patients with culture and 174 of 278 of patients with histology in the study. H. pylori positivity was also found in 191 patients with RUT. According to the gold standard criteria, a false negative result was found in 39 cases by culture method, 17 cases by histology, and 8 cases by RUT. Sensitivity and specificity of the culture, histology, and RUT methods of the patients were 76.5 % and 88.3 %, 87.8 % and 63 %, 94.2 % and 57.2 %, respectively. Antibiotic resistance was investigated by E-test in 140 H. pylori strains isolated from culture. The resistance rates of H. pylori strains to the amoxicillin, clarithromycin, levofloxacin, and metronidazole was detected as 9 (6.4 %), 22 (15.7 %), 17 (12.1 %), 57 (40.7 %), respectively. Conclusion: In our study, RUT was found to be the most sensitive, culture was the most specific test between culture, histology, and RUT methods. Although we detected the specificity of the culture method as high, its sensitivity was found to be quite low compared to other methods. The low sensitivity of H. pylori culture may be caused by the factors affect the chances of direct isolation such as spoild bacterium, difficult-to-breed microorganism, clinical sample retrieval, and transport conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antimicrobial%20resistance" title="antimicrobial resistance">antimicrobial resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=culture" title=" culture"> culture</a>, <a href="https://publications.waset.org/abstracts/search?q=histology" title=" histology"> histology</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20pylori" title=" H. pylori"> H. pylori</a>, <a href="https://publications.waset.org/abstracts/search?q=RUT" title=" RUT"> RUT</a> </p> <a href="https://publications.waset.org/abstracts/92086/evaluation-of-diagnostic-values-of-culture-rapid-urease-test-and-histopathology-in-the-diagnosis-of-helicobacter-pylori-infection-and-in-vitro-effects-of-various-antimicrobials-against-helicobacter-pylori" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92086.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">163</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">1280</span> The Effect of Excess Sulphur on Najdi Sheep</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Al-Humaid">Fatima Al-Humaid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research work was done to investigate the cause of paralysis in Najdi lambs born in certain farms where the drinking water and diet contained high concentrations of sulphur. The drinking water in these farms was obtained from deep bore wells drilled in the farm. The lambs developed paralysis of the hind limbs at the age of 4-6 weeks and their condition deteriorated continuously until they finally died. The appetite and suckling ability remained good throughout the course of the disease but when the lambs were completely unable to move and reach for the udder, feed and water they died. Postmortem examination of the brain of paralyzed lambs showed that it was liquefied. When the brain was examined histologically, a liquefactive necrosis was seen in the form of cavities in the nervous tissue. Similar histologic picture was seen in the spinal cord of the affected lambs. Analysis for the mineral content of the fodder showed that the concentration of sulphur was 21.6 3.4 g/kg DM which is considered very high for the nutrition of sheep. Analysis for the concentration of copper and selenium in the feed showed that the concentrations of both were normal. This excluded diseases such as swayback which is caused by copper deficiency and white muscle disease, which caused by selenium deficiency. Both of these two last diseases are characterized by paralysis of lambs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20histology" title="brain histology">brain histology</a>, <a href="https://publications.waset.org/abstracts/search?q=sulphur%20poisoning" title=" sulphur poisoning"> sulphur poisoning</a>, <a href="https://publications.waset.org/abstracts/search?q=Najdi%20sheep" title=" Najdi sheep"> Najdi sheep</a>, <a href="https://publications.waset.org/abstracts/search?q=veterinary%20medicine" title=" veterinary medicine"> veterinary medicine</a> </p> <a href="https://publications.waset.org/abstracts/16522/the-effect-of-excess-sulphur-on-najdi-sheep" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16522.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">605</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">1279</span> Partial Differential Equation-Based Modeling of Brain Response to Stimuli</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Razieh%20Khalafi">Razieh Khalafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The brain is the information processing centre of the human body. Stimuli in the form of information are transferred to the brain and then brain makes the decision on how to respond to them. In this research, we propose a new partial differential equation which analyses the EEG signals and make a relationship between the incoming stimuli and the brain response to them. In order to test the proposed model, a set of external stimuli applied to the model and the model’s outputs were checked versus the real EEG data. The results show that this model can model the EEG signal well. The proposed model is useful not only for modelling of EEG signal in case external stimuli but it can be used for modelling of brain response in case of internal stimuli. <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=stimuli" title=" stimuli"> stimuli</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20differential%20equation" title=" partial differential equation"> partial differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=response" title=" response"> response</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signal" title=" EEG signal"> EEG signal</a> </p> <a href="https://publications.waset.org/abstracts/29783/partial-differential-equation-based-modeling-of-brain-response-to-stimuli" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29783.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">554</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">1278</span> Clustering-Based Detection of Alzheimer's Disease Using Brain MR Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Matoug">Sofia Matoug</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20Abdel-Dayem"> Amr Abdel-Dayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comprehensive survey of recent research studies to segment and classify brain MR (magnetic resonance) images in order to detect significant changes to brain ventricles. The paper also presents a general framework for detecting regions that atrophy, which can help neurologists in detecting and staging Alzheimer. Furthermore, a prototype was implemented to segment brain MR images in order to extract the region of interest (ROI) and then, a classifier was employed to differentiate between normal and abnormal brain tissues. Experimental results show that the proposed scheme can provide a reliable second opinion that neurologists can benefit from. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer" title="Alzheimer">Alzheimer</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20images" title=" brain images"> brain images</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20techniques" title=" classification techniques"> classification techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=Magnetic%20Resonance%20Images%20MRI" title=" Magnetic Resonance Images MRI"> Magnetic Resonance Images MRI</a> </p> <a href="https://publications.waset.org/abstracts/49930/clustering-based-detection-of-alzheimers-disease-using-brain-mr-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49930.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">302</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">1277</span> Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.V.%20Suryawanshi">U.V. Suryawanshi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.S.%20Chowhan"> S.S. Chowhan</a>, <a href="https://publications.waset.org/abstracts/search?q=U.V%20Kulkarni"> U.V Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper portraits performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI has been developed. The objective of this paper is to perform a segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with a discussion on the trend of future research in brain segmentation and changing norms in IFCM for better results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=FCM" title=" FCM"> FCM</a>, <a href="https://publications.waset.org/abstracts/search?q=KFCM" title=" KFCM"> KFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=SFCM" title=" SFCM"> SFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=IFCM" title=" IFCM"> IFCM</a> </p> <a href="https://publications.waset.org/abstracts/12406/performance-evaluation-of-various-segmentation-techniques-on-mri-of-brain-tissue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12406.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">331</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">1276</span> A Mathematical-Based Formulation of EEG Fluctuations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Razi%20Khalafi">Razi Khalafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain is the information processing center of the human body. Stimuli in form of information are transferred to the brain and then brain makes the decision on how to respond to them. In this research we propose a new partial differential equation which analyses the EEG signals and make a relationship between the incoming stimuli and the brain response to them. In order to test the proposed model, a set of external stimuli applied to the model and the model’s outputs were checked versus the real EEG data. The results show that this model can model the EEG signal well. The proposed model is useful not only for modeling of the EEG signal in case external stimuli but it can be used for the modeling of brain response in case of internal stimuli. <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=stimuli" title=" stimuli"> stimuli</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20differential%20equation" title=" partial differential equation"> partial differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=response" title=" response"> response</a>, <a href="https://publications.waset.org/abstracts/search?q=eeg%20signal" title=" eeg signal"> eeg signal</a> </p> <a href="https://publications.waset.org/abstracts/30791/a-mathematical-based-formulation-of-eeg-fluctuations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30791.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">433</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">1275</span> EEG Diagnosis Based on Phase Space with Wavelet Transforms for Epilepsy Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohmmad%20A.%20Obeidat">Mohmmad A. Obeidat</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjed%20Al%20Fahoum"> Amjed Al Fahoum</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20M.%20Mansour"> Ayman M. Mansour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/17206/eeg-diagnosis-based-on-phase-space-with-wavelet-transforms-for-epilepsy-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17206.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">538</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">1274</span> Improvement of Brain Tumors Detection Using Markers and Boundaries Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousif%20Mohamed%20Y.%20Abdallah">Yousif Mohamed Y. Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mommen%20A.%20Alkhir"> Mommen A. Alkhir</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20S.%20Algaddal"> Amel S. Algaddal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=improvement" title="improvement">improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=brain" title=" brain"> brain</a>, <a href="https://publications.waset.org/abstracts/search?q=matlab" title=" matlab"> matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=markers" title=" markers"> markers</a>, <a href="https://publications.waset.org/abstracts/search?q=boundaries" title=" boundaries"> boundaries</a> </p> <a href="https://publications.waset.org/abstracts/31036/improvement-of-brain-tumors-detection-using-markers-and-boundaries-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31036.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">516</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">1273</span> Optimising Transcranial Alternating Current Stimulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Robert%20Lenzie">Robert Lenzie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transcranial electrical stimulation (tES) is significant in the research literature. However, the effects of tES on brain activity are still poorly understood at the surface level, the Brodmann Area level, and the impact on neural networks. Using a method like electroencephalography (EEG) in conjunction with tES might make it possible to comprehend the brain response and mechanisms behind published observed alterations in more depth. Using a method to directly see the effect of tES on EEG may offer high temporal resolution data on the brain activity changes/modulations brought on by tES that correlate to various processing stages within the brain. This paper provides unpublished information on a cutting-edge methodology that may reveal details about the dynamics of how the human brain works beyond what is now achievable with existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tACS" title="tACS">tACS</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency" title=" frequency"> frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal" title=" optimal"> optimal</a> </p> <a href="https://publications.waset.org/abstracts/159776/optimising-transcranial-alternating-current-stimulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159776.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">83</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">1272</span> Post-Contrast Susceptibility Weighted Imaging vs. Post-Contrast T1 Weighted Imaging for Evaluation of Brain Lesions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujith%20Rajashekar%20Swamy">Sujith Rajashekar Swamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Meghana%20Rajashekara%20Swamy"> Meghana Rajashekara Swamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although T1-weighted gadolinium-enhanced imaging (T1-Gd) has its established clinical role in diagnosing brain lesions of infectious and metastatic origins, the use of post-contrast susceptibility-weighted imaging (SWI) has been understudied. This observational study aims to explore and compare the prominence of brain parenchymal lesions between T1-Gd and SWI-Gd images. A cross-sectional study design was utilized to analyze 58 patients with brain parenchymal lesions using T1-Gd and SWI-Gd scanning techniques. Our results indicated that SWI-Gd enhanced the conspicuity of metastatic as well as infectious brain lesions when compared to T1-Gd. Consequently, it can be used as an adjunct to T1-Gd for post-contrast imaging, thereby avoiding additional contrast administration. Improved conspicuity of brain lesions translates directly to enhanced patient outcomes, and hence SWI-Gd imaging proves useful to meet that endpoint. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=susceptibility%20weighted" title="susceptibility weighted">susceptibility weighted</a>, <a href="https://publications.waset.org/abstracts/search?q=T1%20weighted" title=" T1 weighted"> T1 weighted</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20lesions" title=" brain lesions"> brain lesions</a>, <a href="https://publications.waset.org/abstracts/search?q=gadolinium%20contrast" title=" gadolinium contrast"> gadolinium contrast</a> </p> <a href="https://publications.waset.org/abstracts/160957/post-contrast-susceptibility-weighted-imaging-vs-post-contrast-t1-weighted-imaging-for-evaluation-of-brain-lesions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160957.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">128</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">1271</span> Patent on Brian: Brain Waves Stimulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalil%20Qoulizadeh">Jalil Qoulizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Sadeghi"> Hasan Sadeghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain waves are electrical wave patterns that are produced in the human brain. Knowing these waves and activating them can have a positive effect on brain function and ultimately create an ideal life. The brain has the ability to produce waves from 0.1 to above 65 Hz. (The Beta One device produces exactly these waves) This is because it is said that the waves produced by the Beta One device exactly match the waves produced by the brain. The function and method of this device is based on the magnetic stimulation of the brain. The technology used in the design and producƟon of this device works in a way to strengthen and improve the frequencies of brain waves with a pre-defined algorithm according to the type of requested function, so that the person can access the expected functions in life activities. to perform better. The effect of this field on neurons and their stimulation: In order to evaluate the effect of this field created by the device, on the neurons, the main tests are by conducting electroencephalography before and after stimulation and comparing these two baselines by qEEG or quantitative electroencephalography method using paired t-test in 39 subjects. It confirms the significant effect of this field on the change of electrical activity recorded after 30 minutes of stimulation in all subjects. The Beta One device is able to induce the appropriate pattern of the expected functions in a soft and effective way to the brain in a healthy and effective way (exactly in accordance with the harmony of brain waves), the process of brain activities first to a normal state and then to a powerful one. Production of inexpensive neuroscience equipment (compared to existing rTMS equipment) Magnetic brain stimulation for clinics - homes - factories and companies - professional sports clubs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stimulation" title="stimulation">stimulation</a>, <a href="https://publications.waset.org/abstracts/search?q=brain" title=" brain"> brain</a>, <a href="https://publications.waset.org/abstracts/search?q=waves" title=" waves"> waves</a>, <a href="https://publications.waset.org/abstracts/search?q=betaOne" title=" betaOne"> betaOne</a> </p> <a href="https://publications.waset.org/abstracts/160354/patent-on-brian-brain-waves-stimulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160354.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">81</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">1270</span> Recent Advancement in Dendrimer Based Nanotechnology for the Treatment of Brain Tumor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nitin%20Dwivedi">Nitin Dwivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jigna%20Shah"> Jigna Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumor is metastatic neoplasm of central nervous system, in most of cases it is life threatening disease with low survival rate. Despite of enormous efforts in the development of therapeutics and diagnostic tools, the treatment of brain tumors and gliomas remain a considerable challenge in the area of neuro-oncology. The most reason behind of this the presence of physiological barriers including blood brain barrier and blood brain tumor barrier, lead to insufficient reach ability of therapeutic agents at the site of tumor, result of inadequate destruction of gliomas. So there is an indeed need empowerment of brain tumor imaging for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional different generations of dendrimer offer an improved effort for potentiate drug delivery at the site of brain tumor and gliomas. So this article emphasizes the innovative dendrimer approaches in tumor targeting, tumor imaging and delivery of therapeutic agent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20brain%20barrier" title="blood brain barrier">blood brain barrier</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrimer" title=" dendrimer"> dendrimer</a>, <a href="https://publications.waset.org/abstracts/search?q=gliomas" title=" gliomas"> gliomas</a>, <a href="https://publications.waset.org/abstracts/search?q=nanotechnology" title=" nanotechnology"> nanotechnology</a> </p> <a href="https://publications.waset.org/abstracts/30047/recent-advancement-in-dendrimer-based-nanotechnology-for-the-treatment-of-brain-tumor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30047.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">561</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">1269</span> Brain Age Prediction Based on Brain Magnetic Resonance Imaging by 3D Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Keshavarz%20Afshar">Leila Keshavarz Afshar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hedieh%20Sajedi"> Hedieh Sajedi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20age%20estimation" title="brain age estimation">brain age estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20age" title=" biological age"> biological age</a>, <a href="https://publications.waset.org/abstracts/search?q=3D-CNN" title=" 3D-CNN"> 3D-CNN</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=T1-weighted%20image" title=" T1-weighted image"> T1-weighted image</a>, <a href="https://publications.waset.org/abstracts/search?q=SPM" title=" SPM"> SPM</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=canny" title=" canny"> canny</a>, <a href="https://publications.waset.org/abstracts/search?q=gray%20matter" title=" gray matter"> gray matter</a> </p> <a href="https://publications.waset.org/abstracts/113560/brain-age-prediction-based-on-brain-magnetic-resonance-imaging-by-3d-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113560.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">147</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">1268</span> Brain Atrophy in Alzheimer's Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tansa%20Nisan%20Gunerhan">Tansa Nisan Gunerhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dementia comes in different forms, including Alzheimer's disease. The most common dementia diagnosis among elderly individuals is Alzheimer's disease. On average, for patients with Alzheimer’s, life expectancy is around 4-8 years after the diagnosis; however, expectancy can go as high as twenty years or more, depending on the shrinkage of the brain. Normally, along with aging, the brain shrinks at some level but doesn’t lose a vast amount of neurons. However, Alzheimer's patients' neurons are destroyed rapidly; hence problems with loss of memory, communication, and other metabolic activities begin. The toxic changes in the brain affect the stability of the neurons. Beta-amyloid and tau are two proteins that are believed to play a role in the development of Alzheimer's disease through their toxic changes. Beta-amyloid is a protein that is produced in the brain and is normally broken down and removed from the body. However, in people with Alzheimer's disease, the production of beta-amyloid increases, and it begins to accumulate in the brain. These plaques are thought to disrupt communication between nerve cells and may contribute to the death of brain cells. Tau is a protein that helps to stabilize microtubules, which are essential for the transportation of nutrients and other substances within brain cells. In people with Alzheimer's disease, tau becomes abnormal and begins to accumulate inside brain cells, forming neurofibrillary tangles. These tangles disrupt the normal functioning of brain cells and may contribute to their death, forming amyloid plaques which are deposits of a protein called amyloid-beta that build up between nerve cells in the brain. The accumulation of amyloid plaques and neurofibrillary tangles in the brain is thought to contribute to the shrinkage of brain tissue. As the brain shrinks, the size of the brain may decrease, leading to a reduction in brain volume. Brain atrophy in Alzheimer's disease is often accompanied by changes in the structure and function of brain cells and the connections between them, leading to a decline in brain function. These toxic changes that accumulate can cause symptoms such as memory loss, difficulty with thinking and problem-solving, and changes in behavior and personality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer" title="Alzheimer">Alzheimer</a>, <a href="https://publications.waset.org/abstracts/search?q=amyloid-beta" title=" amyloid-beta"> amyloid-beta</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20atrophy" title=" brain atrophy"> brain atrophy</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron" title=" neuron"> neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=shrinkage" title=" shrinkage"> shrinkage</a> </p> <a href="https://publications.waset.org/abstracts/161073/brain-atrophy-in-alzheimers-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161073.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">95</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">1267</span> Descriptive Study of Role Played by Exercise and Diet on Brain Plasticity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mridul%20Sharma">Mridul Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Praveen%20Saroha"> Praveen Saroha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's world, everyone has become so busy in their to-do tasks and daily routine that they tend to ignore some of the basal components of our life, including exercise and diet. This comparative study analyzes the pathways of the relationship between exercise and brain plasticity and also includes another variable diet to study the effects of diet on learning by answering questions including which diet is known to be the best learning supporter and what are the recommended quantities of the same. Further, this study looks into inter-relation between diet and exercise, and also some other approach of the relation between diet and exercise on learning apart from through Brain Derived Neurotrophic Factor (BDNF). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20derived%20neurotrophic%20factor" title="brain derived neurotrophic factor">brain derived neurotrophic factor</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20plasticity" title=" brain plasticity"> brain plasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=diet" title=" diet"> diet</a>, <a href="https://publications.waset.org/abstracts/search?q=exercise" title=" exercise"> exercise</a> </p> <a href="https://publications.waset.org/abstracts/112374/descriptive-study-of-role-played-by-exercise-and-diet-on-brain-plasticity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112374.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">141</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">1266</span> The Effect of the Hemispheres of the Brain and the Tone of Voice on Persuasion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rica%20Jell%20de%20Laza">Rica Jell de Laza</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Alberto%20Fernandez"> Jose Alberto Fernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Marie%20Mendoza"> Andrea Marie Mendoza</a>, <a href="https://publications.waset.org/abstracts/search?q=Qristin%20Jeuel%20Regalado"> Qristin Jeuel Regalado</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates whether participants experience different levels of persuasion depending on the hemisphere of the brain and the tone of voice. The experiment was performed on 96 volunteer undergraduate students taking an introductory course in psychology. The participants took part in a 2 x 3 (Hemisphere: left, right x Tone of Voice: positive, neutral, negative) Mixed Factorial Design to measure how much a person was persuaded. Results showed that the hemisphere of the brain and the tone of voice used did not significantly affect the results individually. Furthermore, there was no interaction effect. Therefore, the hemispheres of the brain and the tone of voice employed play insignificant roles in persuading a person. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dichotic%20listening" title="dichotic listening">dichotic listening</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20hemisphere" title=" brain hemisphere"> brain hemisphere</a>, <a href="https://publications.waset.org/abstracts/search?q=tone%20of%20voice" title=" tone of voice"> tone of voice</a>, <a href="https://publications.waset.org/abstracts/search?q=persuasion" title=" persuasion "> persuasion </a> </p> <a href="https://publications.waset.org/abstracts/62379/the-effect-of-the-hemispheres-of-the-brain-and-the-tone-of-voice-on-persuasion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62379.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">306</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">1265</span> Computer Aided Diagnostic System for Detection and Classification of a Brain Tumor through MRI Using Level Set Based Segmentation Technique and ANN Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atanu%20K%20Samanta">Atanu K Samanta</a>, <a href="https://publications.waset.org/abstracts/search?q=Asim%20Ali%20Khan"> Asim Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good. <p class="card-text"><strong>Keywords:</strong> <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=computer-aided%20diagnostic%20%28CAD%29%20system" title=" computer-aided diagnostic (CAD) system"> computer-aided diagnostic (CAD) system</a>, <a href="https://publications.waset.org/abstracts/search?q=gray-level%20co-occurrence%20matrix%20%28GLCM%29" title=" gray-level co-occurrence matrix (GLCM)"> gray-level co-occurrence matrix (GLCM)</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20segmentation" title=" tumor segmentation"> tumor segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20set%20method" title=" level set method"> level set method</a> </p> <a href="https://publications.waset.org/abstracts/61237/computer-aided-diagnostic-system-for-detection-and-classification-of-a-brain-tumor-through-mri-using-level-set-based-segmentation-technique-and-ann-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61237.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">512</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">1264</span> Evaluation of Fetal brain using Magnetic Resonance Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Farajzadeh%20Ajirlou">Mahdi Farajzadeh Ajirlou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ordinary fetal brain development can be considered by in vivo attractive reverberation imaging (MRI) from the 18th gestational week (GW) to term and depends fundamentally on T2-weighted and diffusion-weighted (DW) arrangements. The foremost commonly suspected brain pathologies alluded to fetal MRI for assist assessment are ventriculomegaly, lost corpus callosum, and anomalies of the posterior fossa. Brain division could be a crucial to begin with step in neuroimage examination. Within the case of fetal MRI it is especially challenging and critical due to the subjective introduction of the hatchling, organs that encompass the fetal head, and irregular fetal movement. A few promising strategies have been proposed but are constrained in their execution in challenging cases and in realtime division. Fetal MRI is routinely performed on a 1.5-Tesla scanner without maternal or fetal sedation. The mother lies recumbent amid the course of the examination, the length of which is ordinarily 45 to 60 minutes. The accessibility and continuous approval of standardizing fetal brain development directions will give critical devices for early discovery of impeded fetal brain development upon which to oversee high-risk pregnancies. <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=fetal" title=" fetal"> fetal</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=imaging" title=" imaging"> imaging</a> </p> <a href="https://publications.waset.org/abstracts/173367/evaluation-of-fetal-brain-using-magnetic-resonance-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173367.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">79</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">1263</span> Analysis of Brain Signals Using Neural Networks Optimized by Co-Evolution Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Abdolkarimi">Zahra Abdolkarimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Naser%20Zourikalatehsamad"> Naser Zourikalatehsamad</a>, <a href="https://publications.waset.org/abstracts/search?q="></a> </p> <p class="card-text"><strong>Abstract:</strong></p> Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=co-evolution%20algorithms" title="co-evolution algorithms">co-evolution algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20signals" title=" brain signals"> brain signals</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS%20model" title=" ANFIS model"> ANFIS model</a>, <a href="https://publications.waset.org/abstracts/search?q=physiologic%20structure" title=" physiologic structure"> physiologic structure</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20prediction" title=" time prediction"> time prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy%20suffering" title=" epilepsy suffering"> epilepsy suffering</a>, <a href="https://publications.waset.org/abstracts/search?q=illustrates%20model" title=" illustrates model "> illustrates model </a> </p> <a href="https://publications.waset.org/abstracts/44734/analysis-of-brain-signals-using-neural-networks-optimized-by-co-evolution-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44734.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">282</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">1262</span> Mechanical Prosthesis Controlled by Brain-Computer Interface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tianyu%20Cao">Tianyu Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=KIRA%20%28Ruizhi%20Zhao%29"> KIRA (Ruizhi Zhao)</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of our research is to study the possibility of people with physical disabilities manipulating mechanical prostheses through brain-computer interface (BCI) technology. The brain-machine interface (BCI) of the neural prosthesis records signals from neurons and uses mathematical modeling to decode them, converting desired movements into body movements. In order to improve the patient's neural control, the prosthesis is given a natural feeling. It records data from sensitive areas from the body to the prosthetic limb and encodes signals in the form of electrical stimulation to the brain. In our research, the brain-computer interface (BCI) is a bridge connecting patients’ cognition and the real world, allowing information to interact with each other. The efficient work between the two is achieved through external devices. The flow of information is controlled by BCI’s ability to record neuronal signals and decode signals, which are converted into device control. In this way, we could encode information and then send it to the brain through electrical stimulation, which has significant medical application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering" title="biomedical engineering">biomedical engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title=" brain-computer interface"> brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=prosthesis" title=" prosthesis"> prosthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20control" title=" neural control"> neural control</a> </p> <a href="https://publications.waset.org/abstracts/138055/mechanical-prosthesis-controlled-by-brain-computer-interface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138055.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">181</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">1261</span> Brain Tumor Detection and Classification Using Pre-Trained Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Karade">Aditya Karade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharada%20Falane"> Sharada Falane</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhananjay%20Deshmukh"> Dhananjay Deshmukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijaykumar%20Mantri"> Vijaykumar Mantri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumour" title="brain tumour">brain tumour</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI%20image" title=" MRI image"> MRI image</a>, <a href="https://publications.waset.org/abstracts/search?q=detecting%20and%20classifying%20tumour" title=" detecting and classifying tumour"> detecting and classifying tumour</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-trained%20models" title=" pre-trained models"> pre-trained models</a>, <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=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a> </p> <a href="https://publications.waset.org/abstracts/178879/brain-tumor-detection-and-classification-using-pre-trained-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178879.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">74</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">1260</span> Meditation Based Brain Painting Promotes Foreign Language Memory through Establishing a Brain-Computer Interface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhepeng%20Rui">Zhepeng Rui</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhenyu%20Gu"> Zhenyu Gu</a>, <a href="https://publications.waset.org/abstracts/search?q=Caitilin%20de%20B%C3%A9rigny"> Caitilin de Bérigny</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the current study, we designed an interactive meditation and brain painting application to cultivate users’ creativity, promote meditation, reduce stress, and improve cognition while attempting to learn a foreign language. User tests and data analyses were conducted on 42 male and 42 female participants to better understand sex-associated psychological and aesthetic differences. Our method utilized brain-computer interfaces to import meditation and attention data to create artwork in meditation-based applications. Female participants showed statistically significantly different language learning outcomes following three meditation paradigms. The art style of brain painting helped females with language memory. Our results suggest that the most ideal methods for promoting memory attention were meditation methods and brain painting exercises contributing to language learning, memory concentration promotion, and foreign word memorization. We conclude that a short period of meditation practice can help in learning a foreign language. These findings provide new insights into meditation, creative language education, brain-computer interface, and human-computer interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title="brain-computer interface">brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=creative%20thinking" title=" creative thinking"> creative thinking</a>, <a href="https://publications.waset.org/abstracts/search?q=meditation" title=" meditation"> meditation</a>, <a href="https://publications.waset.org/abstracts/search?q=mental%20health" title=" mental health"> mental health</a> </p> <a href="https://publications.waset.org/abstracts/147651/meditation-based-brain-painting-promotes-foreign-language-memory-through-establishing-a-brain-computer-interface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147651.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">127</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">1259</span> Effect of Methanolic Extract of Punica granatum L. Fruit Rind on Kidney, Liver Marker Enzymes, Electrolytes, and Their Histology in Normal Healthy Rats</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20A.%20Shettima">Y. A. Shettima</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Tijjani"> M. A. Tijjani</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Modu"> S. Modu</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20I.%20Abdulrahman"> F. I. Abdulrahman</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20M.%20Abubakar"> B. M. Abubakar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The toxicity profile of the methanolic extract of Punica granatum L. fruit rind was studied in normal rats. The rats were administered orally by intubating graded doses of 150, 250, 500 and 750 mg/kg body weight of the extract for 28 days and the effects on biochemical parameters and histology of the liver and kidney were evaluated. There was a significant increase (P<0.05) in the levels of liver enzymes of the rats that received the highest dose of 750 mg/kg body weight. The AST and ALT levels were 41.59±0.18 ALP and 9.25±0.29 IU/L, respectively, while the ALP level was 15.68±10 IU/L.There was a significant difference in the albumin and globulin levels; 3.72±0.05 and 4.05±0.13 g/dl, respectively. Serum urea and creatinine levels remained normal, as well as the electrolyte levels. The increase in sodium concentration observed was not statistically significant (P≥0.05) when the control group (131.50±3.11) was compared with the experimental groups (132.25±3.86, 132.75±3.86, 133.50±3.11 and 134.00±1.83). The increase in potassium concentration was not statistically significant (P≥0.05) when the control group with a value of 95.50±3.51 mmol/L was compared with the experimental groups 98.00±3.16, 99.25±2.22, 99.79±0.36 and 99.99±0.02 mmol/L. The increase observed in bicarbonate concentration was not statistically significant (P≥0.05) when the control group with a value of 20.75±1.71 mmol/L was compared with the experimental groups 21.68±0.62, 24.25±2.99, 24.50±3.42, 25.50±2.65 mmol/L. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=punical%20granatum" title="punical granatum">punical granatum</a>, <a href="https://publications.waset.org/abstracts/search?q=methanolic" title=" methanolic"> methanolic</a>, <a href="https://publications.waset.org/abstracts/search?q=ALT" title=" ALT"> ALT</a>, <a href="https://publications.waset.org/abstracts/search?q=AST" title=" AST"> AST</a>, <a href="https://publications.waset.org/abstracts/search?q=electrolytes" title=" electrolytes"> electrolytes</a>, <a href="https://publications.waset.org/abstracts/search?q=histology" title=" histology"> histology</a> </p> <a href="https://publications.waset.org/abstracts/9408/effect-of-methanolic-extract-of-punica-granatum-l-fruit-rind-on-kidney-liver-marker-enzymes-electrolytes-and-their-histology-in-normal-healthy-rats" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9408.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">397</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">1258</span> Magnetic Resonance Imaging in Children with Brain Tumors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20R.%20Ashrapov">J. R. Ashrapov</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20A.%20Alihodzhaeva"> G. A. Alihodzhaeva</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20E.%20Abdullaev"> D. E. Abdullaev</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20R.%20Kadirbekov"> N. R. Kadirbekov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosis of brain tumors is one of the challenges, as several central nervous system diseases run the same symptoms. Modern diagnostic techniques such as CT, MRI helps to significantly improve the surgery in the operating period, after surgery, after allowing time to identify postoperative complications in neurosurgery. Purpose: To study the MRI characteristics and localization of brain tumors in children and to detect the postoperative complications in the postoperative period. Materials and methods: A retrospective study of treatment of 62 children with brain tumors in age from 2 to 5 years was performed. Results of the review: MRI scan of the brain of the 62 patients 52 (83.8%) case revealed a brain tumor. Distribution on MRI of brain tumors found in 15 (24.1%) - glioblastomas, 21 (33.8%) - astrocytomas, 7 (11.2%) - medulloblastomas, 9 (14.5%) - a tumor origin (craniopharyngiomas, chordoma of the skull base). MRI revealed the following characteristic features: an additional sign of the heterogeneous MRI signal of hyper and hypointensive T1 and T2 modes with a different perifocal swelling degree with involvement in the process of brain vessels. The main objectives of postoperative MRI study are the identification of early or late postoperative complications, evaluation of radical surgery, the identification of the extended-growing tumor that (in terms of 3-4 weeks). MRI performed in the following cases: 1. Suspicion of a hematoma (3 days or more) 2. Suspicion continued tumor growth (in terms of 3-4 weeks). Conclusions: Magnetic resonance tomography is a highly informative method of diagnostics of brain tumors in children. MRI also helps to determine the effectiveness and tactics of treatment and the follow up in the postoperative period. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title="brain tumors">brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment" title=" treatment"> treatment</a> </p> <a href="https://publications.waset.org/abstracts/116321/magnetic-resonance-imaging-in-children-with-brain-tumors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116321.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">145</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">1257</span> Brain-Motor Disablement: Using Virtual Reality-Based Therapeutic Simulations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vince%20Macri">Vince Macri</a>, <a href="https://publications.waset.org/abstracts/search?q=Jakub%20Petioky"> Jakub Petioky</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Zilber"> Paul Zilber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Virtual-reality-based technology, i.e. video-game-like simulations (collectively, VRSims) are used in therapy for a variety of medical conditions. The purpose of this paper is to contribute to a discussion on criteria for selecting VRSims to augment treatment of survivors of acquired brain injury. Specifically, for treatments to improve or restore brain motor function in upper extremities affected by paresis or paralysis. Six uses of virtual reality are reviewed video games for entertainment, training simulations, unassisted or device-assisted movements of affected or unaffected extremities displayed in virtual environments and virtual anatomical interactivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acquired%20brain%20injury" title="acquired brain injury">acquired brain injury</a>, <a href="https://publications.waset.org/abstracts/search?q=brain-motor%20function" title=" brain-motor function"> brain-motor function</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20anatomical%20interactivity" title=" virtual anatomical interactivity"> virtual anatomical interactivity</a>, <a href="https://publications.waset.org/abstracts/search?q=therapeutic%20simulations" title=" therapeutic simulations "> therapeutic simulations </a> </p> <a href="https://publications.waset.org/abstracts/29311/brain-motor-disablement-using-virtual-reality-based-therapeutic-simulations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29311.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">588</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">1256</span> Clinical Trial of VEUPLEXᵀᴹ TBI Assay to Help Diagnose Traumatic Brain Injury by Quantifying Glial Fibrillary Acidic Protein and Ubiquitin Carboxy-Terminal Hydrolase L1 in the Serum of Patients Suspected of Mild TBI by Fluorescence Immunoassay</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moon%20Jung%20Kim">Moon Jung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Guil%20Rhim"> Guil Rhim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The clinical sensitivity of the “VEUPLEXTM TBI assay”, a clinical trial medical device, in mild traumatic brain injury was 28.6% (95% CI, 19.7%-37.5%), and the clinical specificity was 94.0% (95% CI, 89.3%). -98.7%). In addition, when the results analyzed by marker were put together, the sensitivity was higher when interpreting the two tests together than the two tests, UCHL1 and GFAP alone. Additionally, when sensitivity and specificity were analyzed based on CT results for the mild traumatic brain injury patient group, the clinical sensitivity for 2 CT-positive cases was 50.0% (95% CI: 1.3%-98.7%), and 19 CT-negative cases. The clinical specificity for cases was 68.4% (95% CI: 43.5% - 87.4%). Since the low clinical sensitivity for the two CT-positive cases was not statistically significant due to the small number of samples analyzed, it was judged necessary to secure and analyze more samples in the future. Regarding the clinical specificity analysis results for 19 CT-negative cases, there were a large number of patients who were actually clinically diagnosed with mild traumatic brain injury but actually received a CT-negative result, and about 31.6% of them showed abnormal results on VEUPLEXTM TBI assay. Although traumatic brain injury could not be detected in 31.6% of the CT scans, the possibility of actually suffering a mild brain injury could not be ruled out, so it was judged that this could be confirmed through follow-up observation of the patient. In addition, among patients with mild traumatic brain injury, CT examinations were not performed in many cases because the symptoms were very mild, but among these patients, about 25% or more showed abnormal results in the VEUPLEXTM TBI assay. In fact, no damage is observed with the naked eye immediately after traumatic brain injury, and traumatic brain injury is not observed even on CT. But in some cases, brain hemorrhage may occur (delayed cerebral hemorrhage) after a certain period of time, so the patients who did show abnormal results on VEUPLEXTM TBI assay should be followed up for the delayed cerebral hemorrhage. In conclusion, it was judged that it was difficult to judge mild traumatic brain injury with the VEUPLEXTM TBI assay only through clinical findings without CT results, that is, based on the GCS value. Even in the case of CT, it does not detect all mild traumatic brain injury, so it is difficult to necessarily judge that there is no traumatic brain injury, even if there is no evidence of traumatic brain injury in CT. And in the long term, more patients should be included to evaluate the usefulness of the VEUPLEXTM TBI assay in the detection of microscopic traumatic brain injuries without using CT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20injury" title="brain injury">brain injury</a>, <a href="https://publications.waset.org/abstracts/search?q=traumatic%20brain%20injury" title=" traumatic brain injury"> traumatic brain injury</a>, <a href="https://publications.waset.org/abstracts/search?q=GFAP" title=" GFAP"> GFAP</a>, <a href="https://publications.waset.org/abstracts/search?q=UCHL1" title=" UCHL1"> UCHL1</a> </p> <a href="https://publications.waset.org/abstracts/166823/clinical-trial-of-veuplex-tbi-assay-to-help-diagnose-traumatic-brain-injury-by-quantifying-glial-fibrillary-acidic-protein-and-ubiquitin-carboxy-terminal-hydrolase-l1-in-the-serum-of-patients-suspected-of-mild-tbi-by-fluorescence-immunoassay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166823.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">99</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">1255</span> Estimation of Endogenous Brain Noise from Brain Response to Flickering Visual Stimulation Magnetoencephalography Visual Perception Speed</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20N.%20Pisarchik">Alexander N. Pisarchik</a>, <a href="https://publications.waset.org/abstracts/search?q=Parth%20Chholak"> Parth Chholak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrinsic brain noise was estimated via magneto-encephalograms (MEG) recorded during perception of flickering visual stimuli with frequencies of 6.67 and 8.57 Hz. First, we measured the mean phase difference between the flicker signal and steady-state event-related field (SSERF) in the occipital area where the brain response at the flicker frequencies and their harmonics appeared in the power spectrum. Then, we calculated the probability distribution of the phase fluctuations in the regions of frequency locking and computed its kurtosis. Since kurtosis is a measure of the distribution’s sharpness, we suppose that inverse kurtosis is related to intrinsic brain noise. In our experiments, the kurtosis value varied among subjects from K = 3 to K = 5 for 6.67 Hz and from 2.6 to 4 for 8.57 Hz. The majority of subjects demonstrated leptokurtic kurtosis (K < 3), i.e., the distribution tails approached zero more slowly than Gaussian. In addition, we found a strong correlation between kurtosis and brain complexity measured as the correlation dimension, so that the MEGs of subjects with higher kurtosis exhibited lower complexity. The obtained results are discussed in the framework of nonlinear dynamics and complex network theories. Specifically, in a network of coupled oscillators, phase synchronization is mainly determined by two antagonistic factors, noise, and the coupling strength. While noise worsens phase synchronization, the coupling improves it. If we assume that each neuron and each synapse contribute to brain noise, the larger neuronal network should have stronger noise, and therefore phase synchronization should be worse, that results in smaller kurtosis. The described method for brain noise estimation can be useful for diagnostics of some brain pathologies associated with abnormal brain noise. <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=flickering" title=" flickering"> flickering</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetoencephalography" title=" magnetoencephalography"> magnetoencephalography</a>, <a href="https://publications.waset.org/abstracts/search?q=MEG" title=" MEG"> MEG</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20perception" title=" visual perception"> visual perception</a>, <a href="https://publications.waset.org/abstracts/search?q=perception%20time" title=" perception time"> perception time</a> </p> <a href="https://publications.waset.org/abstracts/104073/estimation-of-endogenous-brain-noise-from-brain-response-to-flickering-visual-stimulation-magnetoencephalography-visual-perception-speed" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104073.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">148</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=brain%20histology&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=brain%20histology&page=3">3</a></li> <li class="page-item"><a class="page-link" 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