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This is partly because of differences in the atlas and delineation techniques used to measure FC\u2010related deficits within\/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC\u2010based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo\u2010Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, <jats:italic>n<\/jats:italic>\u2009=\u2009340), autism spectrum disorder (ASD, <jats:italic>n<\/jats:italic>\u2009=\u2009513), schizophrenia (SZ, <jats:italic>n<\/jats:italic>\u2009=\u2009200), schizoaffective disorder (SAD, <jats:italic>n<\/jats:italic>\u2009=\u2009142), bipolar disorder (BP, <jats:italic>n<\/jats:italic>\u2009=\u2009172), and major depression disorder (MDD, <jats:italic>n<\/jats:italic>\u2009=\u2009282). Our cross\u2010atlas\/disorder analyses demonstrated that frontal\u2010related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal\u2010related FCs may serve as potential common and robust neuro\u2010abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI\u2010based FC analyses, this study suggests the use of functional templates at larger granularity.<\/jats:p>","DOI":"10.1002\/hbm.70206","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T09:40:52Z","timestamp":1743759652000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders"],"prefix":"10.1002","volume":"46","author":[{"given":"Xiaoya","family":"Wu","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics  Nanjing China"},{"name":"The Key Laboratory of Brain\u2010Machine Intelligence Technology, Ministry of Education Nanjing University of Aeronautics and Astronautics  Nanjing 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