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Neural Eng."],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Alzheimer\u2019s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).\n                    <jats:italic>Approach<\/jats:italic>\n                    . Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.\n                    <jats:italic>Main results<\/jats:italic>\n                    . The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.\n                    <jats:italic>Significance<\/jats:italic>\n                    . Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Shuning-Han\/FC-based-GCN\" xlink:type=\"simple\">https:\/\/github.com\/Shuning-Han\/FC-based-GCN<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ad1e22","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T17:45:17Z","timestamp":1705081517000},"page":"016013","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Early prediction of dementia using fMRI data with a graph convolutional network approach"],"prefix":"10.1088","volume":"21","author":[{"given":"Shuning","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6531-0769","authenticated-orcid":true,"given":"Zhe","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanhao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-6095","authenticated-orcid":true,"given":"Cesar F","family":"Caiafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6534-1979","authenticated-orcid":true,"given":"Jordi","family":"Sol\u00e9-Casals","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"jnead1e22bib1","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.3390\/molecules25245789","article-title":"Comprehensive review on Alzheimer\u2019s disease: causes and treatment","volume":"25","author":"Breijyeh","year":"2020","journal-title":"Molecules"},{"key":"jnead1e22bib2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2021.113320","article-title":"Alzheimer\u2019s disease and its treatment by different approaches: a review","volume":"216","author":"Srivastava","year":"2021","journal-title":"Eur. 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