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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Mood Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1469645
This article is part of the Research Topic Treatment Resistant Depression (TRD): epidemiology, clinic, burden and treatment View all 18 articles

Prediction of Pharmacological Treatment Efficacy Using Electroencephalography-based Salience Network in Patients with Major Depressive Disorder

Provisionally accepted
Kang-Min Choi Kang-Min Choi 1Taegyeong Lee Taegyeong Lee 1Chang-Hwan Im Chang-Hwan Im 1Seung-Hwan Lee Seung-Hwan Lee 2*
  • 1 Hanyang University, Seoul, Republic of Korea
  • 2 Department of Psychiatry, Inje University, Ilsan-Paik Hospital, Goyang, Republic of Korea

The final, formatted version of the article will be published soon.

    Recent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions. Thirty-one drug-naïve patients with MDD (aged 46.61 ± 10.05, female 28) and twentyone healthy controls (aged 43.86 ± 14.14, female 19) participated in the study. After 8 weeks of pharmacological treatment, the patients were divided into non-remitted MDD (nrMDD, n = 14) and remitted-MDD (rMDD, n = 17) groups. EEG data under three conditions (resting-state, standard, and deviant) were analyzed. The SN was constructed with three cortical regions as nodes and weighted phase-lag index as edges, across alpha, low-beta, high-beta, and gamma bands. A repeated measures analysis of the variance model was used to examine the group-by-condition interaction. Machine learning-based classification analyses were also conducted between the nrMDD and rMDD groups. A notable group-by-condition interaction was observed in the high-beta band between nrMDD and rMDD. Specifically, patients with nrMDD exhibited hypoconnectivity between the dorsal anterior cingulate cortex and right insula (p = 0.030). The classification analysis yielded a maximum classification accuracy of 80.65%. Our study suggests that abnormal condition-dependent changes in the SN could serve as potential predictors of pharmacological treatment efficacy in patients with MDD.

    Keywords: Electroencephalography, Major Depressive Disorder, salience network, Prediction of antidepressant responsiveness, Condition-dependent functional network

    Received: 24 Jul 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Choi, Lee, Im and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Seung-Hwan Lee, Department of Psychiatry, Inje University, Ilsan-Paik Hospital, Goyang, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.