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Simultaneously, it models population relationships by constructing an inter-subject similarity graph to enhance the model\u2019s representational capacity. Additionally, this study incorporates interpretability analysis techniques to deeply dissect the model\u2019s decision-making mechanism and identify key brain regions critical for classification. Results demonstrate that the proposed model achieves superior performance in PD classification tasks and exhibits good generalization ability. More importantly, by interpreting the model\u2019s decisions, key brain regions associated with PD discrimination were successfully identified. This study provides an effective computational framework for PD identification and offers new insights into understanding its pathological mechanisms.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae5dd3","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T22:47:53Z","timestamp":1775861273000},"page":"035002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A graph deep learning method for diagnosis of Parkinson\u2019s disease using brain functional connectivity features"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6434-4377","authenticated-orcid":true,"given":"Meili","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"bpexae5dd3bib1","doi-asserted-by":"publisher","first-page":"147635","DOI":"10.1109\/ACCESS.2020.3016062","type":"journal-article","article-title":"Early detection of Parkinson\u2019s disease using deep learning and machine learning","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"bpexae5dd3bib2","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","type":"journal-article","article-title":"Challenges in the diagnosis of Parkinson\u2019s disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"bpexae5dd3bib3","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2023.103396","type":"journal-article","article-title":"Improving fMRI in Parkinson\u2019s disease by accounting for brain region-specific activity patterns","volume":"38","author":"Torrecuso","year":"2023","journal-title":"Neuroimage Clin."},{"key":"bpexae5dd3bib4","doi-asserted-by":"publisher","DOI":"10.1155\/padi\/6870215","type":"journal-article","article-title":"Hot topics and frontiers of resting-state fMRI in Parkinson\u2019s disease: research trends and paradigm shifts from a bibliometric perspective","volume":"7","author":"Jin","year":"2025","journal-title":"Parkinsons Dis."},{"key":"bpexae5dd3bib5","doi-asserted-by":"publisher","first-page":"657","DOI":"10.3389\/fnins.2019.00657","type":"journal-article","article-title":"Resting state dynamic functional connectivity in neurodegenerative conditions: a review of magnetic resonance imaging findings","volume":"13","author":"Filippi","year":"2019","journal-title":"Front. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-12-17","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-04-10","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-04-23","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}