Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

Copyright © 2023. Published by Elsevier B.V..

BACKGROUND: Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.

METHODS: Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.

RESULTS: After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).

LIMITATIONS: Relatively small sample size and not a double-blind design.

CONCLUSIONS: The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:340

Enthalten in:

Journal of affective disorders - 340(2023) vom: 01. Nov., Seite 751-757

Sprache:

Englisch

Beteiligte Personen:

Tian, Shui [VerfasserIn]
Wang, Qiang [VerfasserIn]
Zhang, Siqi [VerfasserIn]
Chen, Zhilu [VerfasserIn]
Dai, Zhongpeng [VerfasserIn]
Zhang, Wei [VerfasserIn]
Yao, Zhijian [VerfasserIn]
Lu, Qing [VerfasserIn]

Links:

Volltext

Themen:

Antidepressive Agents
Early response
Journal Article
Large-scale functional connectivity
Magnetoencephalography (MEG)
Major depressive disorder (MDD)
Neural activity
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 13.09.2023

Date Revised 13.09.2023

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.jad.2023.08.096

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM360965458