Evidence map›Paper›PMID 40577659›Full record

ArticleThe international journal of neuropsychopharmacology2025

Predicting antidepressant responsiveness in major depressive disorder patients via electroencephalography gamma-band dynamic functional connectivity in response to salient auditory stimuli.

Kang-Min Choi, Taegyeong Lee, Seung-Hwan Lee, Chang-Hwan Im

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Article in The international journal of neuropsychopharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kang-Min ChoiDepartment of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea.ORCID 0000-0003-1968-3641
Taegyeong LeeDepartment of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Seung-Hwan LeeClinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Gyeonggi-do 10380, Republic of Korea.ORCID 0000-0003-0305-3709
Chang-Hwan ImDepartment of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea.ORCID 0000-0003-3795-3318

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeterogeneous 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.

methodsBiosignals 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.

resultsUsing 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.

conclusionsWe demonstrated that gamma-band dFC responses to salient stimuli could serve as potential biomarkers for antidepressant responsiveness prediction in patients with MDD.

Indexed as

Antidepressive AgentsAuditory PerceptionConnectomeGamma RhythmMajor Depressive DisorderAcoustic StimulationAdultElectroencephalographyFemaleHumansMachine LearningMaleMiddle AgedAntidepressive Agentsantidepressant responsivenessdynamic functional connectivityelectroencephalographymajor depressive disordermismatch negativity

Identifiers

PMID40577659
PMCPMC12309374

What Socratic holds

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.