{"doi":"10.1093/ijnp/pyaf042","title":"Predicting antidepressant responsiveness in major depressive disorder patients via electroencephalography gamma-band dynamic functional connectivity in response to salient auditory stimuli","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>Heterogeneous pathophysiological characteristics in patients with major depressive disorder (MDD) lead to individually differentiated sensitivities to antidepressants. Based on the hypothesis that gamma-band dynamic fluctuations in cortical functional connectivity (FC) in response to salient stimuli are linked to pathophysiological characteristics, we conducted a classification analysis for antidepressant responsiveness prediction.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>Biosignals and psychological measures were acquired from 47 patients with MDD prior to treatment. After 8 weeks of antidepressant therapy, patients were divided into non-remitted MDD (nrMDD; aged 42.55 ± 11.52 years; n = 20) and remitted MDD (rMDD; aged 47.22 ± 11.59 years; n = 27) groups based on their depressive symptom reduction. Electroencephalography (EEG) signals were acquired during the duration-variant auditory mismatch negativity paradigm. From the deviant condition, gamma-band weighted phase-lag index-based dynamic fluctuations were evaluated using a template generated from 21 demography-matched healthy control (aged 43.81 ± 14.10 years) data.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>Using these dynamic functional connectivity (dFC) features, a machine learning-based classification analysis was performed for nrMDD and rMDD. Using leave-one-out cross-validation, the linear discriminant analysis classifier achieved the best accuracy (82.98%) for classifying nrMDD and rMDD. Further simple effect analyses identified three core dFC features for nrMDD: (i) relatively intact time-dependent FC between the left frontal and right temporal regions; (ii) disrupted right frontoparietal FC; and (iii) disrupted left fronto-temporal FC. These dFC features commonly exhibit transient hyperconnections in patients with nrMDD.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>We demonstrated that gamma-band dFC responses to salient stimuli could serve as potential biomarkers for antidepressant responsiveness prediction in patients with MDD.</jats:p>\n                  </jats:sec>","journal":"International Journal of Neuropsychopharmacology","year":2025,"id":671011,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":2,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1752897,"name":"Taegyeong Lee","orcid":null,"position":1,"is_corresponding":false},{"id":1549743,"name":"Seung-Hwan Lee","orcid":"0000-0003-0305-3709","position":2,"is_corresponding":false},{"id":1752898,"name":"Chang-Hwan Im","orcid":"0000-0003-3795-3318","position":3,"is_corresponding":false},{"id":1470069,"name":"Kang‐Min Choi","orcid":"0000-0003-1968-3641","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Predicting antidepressant responsiveness in major depressive disorder patients via electroencephalography gamma-band dynamic functional connectivity in response to salient auditory stimuli","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>Heterogeneous pathophysiological characteristics in patients with major depressive disorder (MDD) lead to individually differentiated sensitivities to antidepressants. Based on the hypothesis that gamma-band dynamic fluctuations in cortical functional connectivity (FC) in response to salient stimuli are linked to pathophysiological characteristics, we conducted a classification analysis for antidepressant responsiveness prediction.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>Biosignals and psychological measures were acquired from 47 patients with MDD prior to treatment. After 8 weeks of antidepressant therapy, patients were divided into non-remitted MDD (nrMDD; aged 42.55 ± 11.52 years; n = 20) and remitted MDD (rMDD; aged 47.22 ± 11.59 years; n = 27) groups based on their depressive symptom reduction. Electroencephalography (EEG) signals were acquired during the duration-variant auditory mismatch negativity paradigm. From the deviant condition, gamma-band weighted phase-lag index-based dynamic fluctuations were evaluated using a template generated from 21 demography-matched healthy control (aged 43.81 ± 14.10 years) data.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>Using these dynamic functional connectivity (dFC) features, a machine learning-based classification analysis was performed for nrMDD and rMDD. Using leave-one-out cross-validation, the linear discriminant analysis classifier achieved the best accuracy (82.98%) for classifying nrMDD and rMDD. Further simple effect analyses identified three core dFC features for nrMDD: (i) relatively intact time-dependent FC between the left frontal and right temporal regions; (ii) disrupted right frontoparietal FC; and (iii) disrupted left fronto-temporal FC. These dFC features commonly exhibit transient hyperconnections in patients with nrMDD.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>We demonstrated that gamma-band dFC responses to salient stimuli could serve as potential biomarkers for antidepressant responsiveness prediction in patients with MDD.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40577659","pmcid":null,"openalex_id":null,"authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://academic.oup.com/ijnp/article-pdf/28/7/pyaf042/63526750/pyaf042.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/ijnp/advance-article-pdf/doi/10.1093/ijnp/pyaf042/63526750/pyaf042.pdf","host_type":"publisher"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12309374","host_type":"repository"}],"fields_of_study":["Humans","Major Depressive Disorder","Adult","Gamma Rhythm","Female","Middle Aged","Male","Antidepressive Agents","Electroencephalography","Connectome","Acoustic Stimulation","Machine Learning","Auditory Perception"],"mesh_terms":["Humans","Major Depressive Disorder","Adult","Gamma Rhythm","Female","Middle Aged","Male","Antidepressive Agents","Electroencephalography","Connectome","Acoustic Stimulation","Machine Learning","Auditory Perception"],"keywords":["antidepressant responsiveness","dynamic functional connectivity","electroencephalography","major depressive disorder","mismatch negativity"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-16T00:59:26.820032Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}