{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T18:01:56Z","timestamp":1793728916675,"version":"4.6.1"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIMH (National Institute of Mental Health, Bethesda, MD, USA)","award":["R03MH096321"],"award-info":[{"award-number":["R03MH096321"]}],"id":[{"id":"https:\/\/ror.org\/04xeg9z08","id-type":"ROR","asserted-by":"crossref"}]},{"name":"NIMH (National Institute of Mental Health, Bethesda, MD, USA)","award":["K23MH087770"],"award-info":[{"award-number":["K23MH087770"]}],"id":[{"id":"https:\/\/ror.org\/04xeg9z08","id-type":"ROR","asserted-by":"crossref"}]},{"name":"NIMH (National Institute of Mental Health, Bethesda, MD, USA)","award":["00007218"],"award-info":[{"award-number":["00007218"]}],"id":[{"id":"https:\/\/ror.org\/04xeg9z08","id-type":"ROR","asserted-by":"crossref"}]},{"name":"multiple agencies","award":["R03MH096321"],"award-info":[{"award-number":["R03MH096321"]}]},{"name":"multiple agencies","award":["K23MH087770"],"award-info":[{"award-number":["K23MH087770"]}]},{"name":"multiple agencies","award":["00007218"],"award-info":[{"award-number":["00007218"]}]},{"name":"U.S. Army Medical Research and Development Command (MRDC)","award":["R03MH096321"],"award-info":[{"award-number":["R03MH096321"]}],"id":[{"id":"https:\/\/ror.org\/03cd02q50","id-type":"ROR","asserted-by":"crossref"}]},{"name":"U.S. Army Medical Research and Development Command (MRDC)","award":["K23MH087770"],"award-info":[{"award-number":["K23MH087770"]}],"id":[{"id":"https:\/\/ror.org\/03cd02q50","id-type":"ROR","asserted-by":"crossref"}]},{"name":"U.S. Army Medical Research and Development Command (MRDC)","award":["00007218"],"award-info":[{"award-number":["00007218"]}],"id":[{"id":"https:\/\/ror.org\/03cd02q50","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.<\/jats:p>","DOI":"10.3390\/brainsci14050456","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T08:02:22Z","timestamp":1714723342000},"page":"456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Nguyen","family":"Huynh","sequence":"first","affiliation":[{"name":"Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Indiana University Bloomington, Bloomington, IN 47405, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueen","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengbin","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Long","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7653-7643","authenticated-orcid":false,"given":"Mirza Tanzim","family":"Sami","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullateef","family":"Almudaifer","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, University of Alabama at Birmingham, Birmingham, AL 35294, USA"},{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 41477, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3039-5328","authenticated-orcid":false,"given":"Haiquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, California State University, Sacramento, CA 95819, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael N.","family":"Dretsch","sequence":"additional","affiliation":[{"name":"Walter Reed Army Institute of Research-West, Joint Base Lewis-McChord, WA 98433, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6695-4777","authenticated-orcid":false,"given":"Thomas S.","family":"Denney","sequence":"additional","affiliation":[{"name":"Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849, USA"},{"name":"Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA"},{"name":"Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA"},{"name":"Center for Neuroscience, Auburn University, Auburn, AL 36849, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rangaprakash","family":"Deshpande","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7471-5357","authenticated-orcid":false,"given":"Gopikrishna","family":"Deshpande","sequence":"additional","affiliation":[{"name":"Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849, USA"},{"name":"Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA"},{"name":"Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA"},{"name":"Center for Neuroscience, Auburn University, Auburn, AL 36849, USA"},{"name":"Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore 560030, India"},{"name":"Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad 502285, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"702","DOI":"10.3389\/fnhum.2013.00702","article-title":"Discriminative analysis of non-linear brain connectivity in schizophrenia: An fmri study","volume":"7","author":"Su","year":"2013","journal-title":"Front. 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