{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang T"],"funding":["Jiangsu Provincial Key Research and Development Program","National Natural Science Foundation of China"],"pagination":["264-274"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10924577"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["69(4)"],"pubmed_abstract":["<h4>Objective</h4>This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and <i>P11</i> gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD).<h4>Methods</h4>A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the <i>P11</i> gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the a"],"journal":["Canadian journal of psychiatry. Revue canadienne de psychiatrie"],"pubmed_title":["Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and &lt;i&gt;P11&lt;/i&gt; Gene DNA Methylation: Prediction de l'efficacite precoce d'un antidepresseur chez des patients souffrant du trouble depressif majeur d'apres les caracteristiques multidimensionnelles de la methylation de l'ADN du gene P11 et de la IRMf-rs."],"pmcid":["PMC10924577"],"funding_grant_id":["BE2019748","81901375","82271570","81971277"],"pubmed_authors":["Yin Y","Hou Z","Li J","Li L","Gao C","Liu X","Jiang W","Wang T","Chen S","Kong Y","Yue Y","Xu Z","Yuan Y"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and &lt;i&gt;P11&lt;/i&gt; Gene DNA Methylation: Prediction de l'efficacite precoce d'un antidepresseur chez des patients souffrant du trouble depressif majeur d'apres les caracteristiques multidimensionnelles de la methylation de l'ADN du gene P11 et de la IRMf-rs.","description":"<h4>Objective</h4>This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and <i>P11</i> gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD).<h4>Methods</h4>A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the <i>P11</i> gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the a","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2025-04-04T18:53:41.738Z","creation":"2025-04-04T18:53:41.738Z"},"accession":"S-EPMC10924577","cross_references":{"pubmed":["37920958"],"doi":["10.1177/07067437231210787"]}}