{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T12:07:14Z","timestamp":1792757234203,"version":"4.5.0"},"reference-count":47,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"unspecified","delay-in-days":36,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Psychol. Med."],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n\t  <jats:sec id=\"S0033291724003477_sec1\">\n\t    <jats:title>Background<\/jats:title>\n\t    <jats:p>Recent neuroimaging studies have demonstrated that the heterogeneous antidepressant responsiveness in patients with major depressive disorder (MDD) is associated with diverse resting-state functional brain network (rsFBN) topology; however, only limited studies have explored the rsFBN using electroencephalography (EEG). In this study, we aimed to identify EEG-derived rsFBN-based biomarkers to predict pharmacotherapeutic responsiveness.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S0033291724003477_sec2\" sec-type=\"methods\">\n\t    <jats:title>Methods<\/jats:title>\n\t    <jats:p>The resting-state EEG signals were acquired for demography-matched three groups: 98 patients with treatment-refractory MDD (trMDD), 269 those with good-responding MDD (grMDD), and 131 healthy controls (HCs). The source-level rsFBN was constructed using 31 sources as nodes and beta-band power envelope correlation (PEC) as edges. The degree centrality (DC) and clustering coefficients (CCs) were calculated for various sparsity levels. Network-based statistic and one-way analysis of variance models were employed for comparing PECs and network indices, respectively. The multiple comparisons were controlled by the false discovery rate.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S0033291724003477_sec3\" sec-type=\"results\">\n\t    <jats:title>Results<\/jats:title>\n\t    <jats:p>Patients with trMDD were characterized by the altered dorsal attention network and salience network. Specifically, they exhibited hypoconnection between eye fields and right parietal regions (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.0088), decreased DC in the right supramarginal gyrus (<jats:italic>q<\/jats:italic>\u00a0=\u00a00.0057), and decreased CC in the reward circuit (<jats:italic>q<\/jats:italic>s\u00a0&lt;\u00a00.05). On the other hand, both MDD groups shared increased DC but decreased CC in the posterior cingulate cortex.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S0033291724003477_sec4\" sec-type=\"conclusions\">\n\t    <jats:title>Conclusions<\/jats:title>\n\t    <jats:p>We confirmed that network topology was more severely deteriorated in patients with trMDD, particularly for the attention-regulatory networks. Our findings suggested that the altered rsFBN topologies could serve as potential pathologically interpretable biomarkers for predicting antidepressant responsiveness.<\/jats:p>\n\t  <\/jats:sec>","DOI":"10.1017\/s0033291724003477","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T03:39:57Z","timestamp":1738813197000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":11,"title":["Association between the functional brain network and antidepressant responsiveness in patients with major depressive disorders: a resting-state EEG 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