{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T10:19:41Z","timestamp":1768213181331,"version":"3.49.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> Modular organization reflects the activity patterns of our brain. Different disease states may lead to different activity patterns and clinical features. Early onset depression (EOD) and late onset depression (LOD) share the same clinical symptoms, but have different treatment strategies and prognosis. Thus, explored the modular organization of EOD and LOD might help us understand their pathogenesis.<\/jats:p><jats:p><jats:bold>Method:<\/jats:bold> The study included 82 patients with late life depression (EOD 40, LOD 42) and 90 healthy controls. We evaluated the memory, executive function and processing speed and performed resting-stage functional MRI for all participants. We constructed a functional network based on Granger causality analysis and carried out modularity, normalized mutual information (NMI), Phi coefficient, within module degree z-score, and participation coefficient analyses for all the participants.<\/jats:p><jats:p><jats:bold>Result:<\/jats:bold> The Granger function network analysis suggested that the functional modularity was different among the three groups (<jats:italic>P<\/jats:italic><jats:sub>auc<\/jats:sub> = 0.0300), and NMI analysis confirmed that the partition of EOD was different from that of LOD (<jats:italic>P<\/jats:italic><jats:sub>auc<\/jats:sub> = 0.0190). Rh.10d.ROI (polar frontal cortex) and Rh.IPS1.ROI (dorsal stream visual cortex) were shown to be the potential specific nodes in the modular assignment according to the Phi coefficient (<jats:italic>P<\/jats:italic> = 0.0002, <jats:italic>P<\/jats:italic><jats:sub>fdr<\/jats:sub> = 0.0744 &amp;amp; <jats:italic>P<\/jats:italic> = 0.0004, <jats:italic>P<\/jats:italic><jats:sub>fdr<\/jats:sub> = 0.0744).<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> This study reveal that the functional modularity and partition were different between EOD and LOD in Granger function network. These findings support the hypothesis that different pathological changes might exist in EOD and LOD.<\/jats:p>","DOI":"10.3389\/fnagi.2021.625175","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T10:22:24Z","timestamp":1612866144000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Different Modular Organization Between Early Onset and Late Onset Depression: A Study Base on Granger Causality Analysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Naikeng","family":"Mai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuping","family":"Ning","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3889","DOI":"10.1016\/j.neuroimage.2011.11.035","article-title":"The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia","volume":"59","author":"Alexander-Bloch","year":"2011","journal-title":"NeuroImage"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e6626","DOI":"10.1371\/journal.pone.0006626","article-title":"Endogenous human brain dynamics recover slowly following cognitive effort","volume":"4","author":"Barnes","year":"2009","journal-title":"PLoS ONE"},{"key":"B3","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate - a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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