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Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was \u226550% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the\n                      <jats:italic toggle=\"yes\">P11<\/jats:italic>\n                      gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Na\u00efve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain,\n                      <jats:italic toggle=\"yes\">P11<\/jats:italic>\n                      gene DNA methylation and clinical\/demographic features after screening.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The SVM model based on ALFF, ReHo, FC,\n                      <jats:italic toggle=\"yes\">P11<\/jats:italic>\n                      methylation, and clinical\/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multidimensional data features combining rs-fMRI, DNA methylation, and clinical\/demographic features can predict the early antidepressant efficacy in MDD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/07067437231210787","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T04:27:36Z","timestamp":1698985656000},"page":"264-274","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and\n                    <i>P11<\/i>\n                    Gene DNA Methylation: Pr\u00e9diction de l\u2019efficacit\u00e9 pr\u00e9coce d\u2019un antid\u00e9presseur chez des patients souffrant du trouble d\u00e9pressif majeur d\u2019apr\u00e8s les caract\u00e9ristiques multidimensionnelles de la m\u00e9thylation de l\u2019ADN du g\u00e8ne P11 et de la IRMf-rs"],"prefix":"10.1177","volume":"69","author":[{"given":"Tianyu","family":"Wang","sequence":"first","affiliation":[{"name":"Southeast University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenjie","family":"Gao","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxing","family":"Li","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Sleep Medicine, The Fourth People's Hospital of Lianyungang, Lianyungang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Yue","sequence":"additional","affiliation":[{"name":"Southeast 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