An Electrophysiological Biomarker That May Predict Treatment Response to ECT

Overview

Journal J ECT

Date 2018 Dec 12

PMID 30531398

Citations 5

Authors

Katherine W Scangos

Richard D Weiner

Edward C Coffey

Andrew D Krystal

Affiliations

Soon will be listed here.

Abstract

Objective: Electroconvulsive therapy (ECT) is the most effective treatment for major depression but also carries risk of cognitive side effects. The ability to predict whether treatment will be effective before initiation of treatment could significantly improve quality of care, reduce suffering, and diminish costs. We sought to carry out a comprehensive and definitive study of the relationship between the background electroencephalography (EEG) and therapeutic response to ECT.

Methods: Twenty-one channel resting EEG was collected pre-ECT and 2 to 3 days after ECT course from 2 separate data sets, one to develop an EEG model of therapeutic response (n = 30) and a second to test this model (n = 40). A 3-way principal components analysis was applied and coherence and spectral amplitude across 6 frequency bands were examined. The primary outcome measure was the Montgomery-Asberg Rating Scale (MADRS).

Results: Four patterns of amplitude and coherence along with baseline MADRS score accounted for 85% of the variance in posttreatment course MADRS score in study 1 (R = 0.85, F = 11.7, P < 0.0002) and 53% of the variance in MADRS score in study 2 (R = 0.53, F = 5.5, P < 0.003). Greater pre-ECT course anterior delta coherence accounted for the majority of variance in therapeutic response (study 1: R = 0.44, P = 0.01; study 2: R = 0.16, P = 0.008).

Conclusions: These results suggest a putative electrophysiological biomarker that can predict therapeutic response before a course of ECT. Greater baseline anterior delta coherence is significantly associated with a better subsequent therapeutic response and could be indicative of intact circuitry allowing for improved seizure propagation.

Citing Articles

[Effect of electroconvulsive therapy on brain functional network in major depressive disorder].

Tian S, Xu G, Yang X, Paul B, Alan W Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023; 40(3):426-433.

PMID: 37380380 PMC: 10307598. DOI: 10.7507/1001-5515.202201032.

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology.

Scangos K, Khambhati A, Daly P, Owen L, Manning J, Ambrose J Front Hum Neurosci. 2021; 15:746499.

PMID: 34744662 PMC: 8566975. DOI: 10.3389/fnhum.2021.746499.

Using EEG to Predict Clinical Response to Electroconvulsive Therapy in Patients With Major Depression: A Comprehensive Review.

Simon L, Blay M, Galvao F, Brunelin J Front Psychiatry. 2021; 12:643710.

PMID: 34248695 PMC: 8264052. DOI: 10.3389/fpsyt.2021.643710.

Changes in seizure duration during acute course electroconvulsive therapy.

Luccarelli J, McCoy Jr T, Seiner S, Henry M Brain Stimul. 2021; 14(4):941-946.

PMID: 34119670 PMC: 8316429. DOI: 10.1016/j.brs.2021.05.016.

Maintenance ECT is associated with sustained improvement in depression symptoms without adverse cognitive effects in a retrospective cohort of 100 patients each receiving 50 or more ECT treatments.

Luccarelli J, McCoy Jr T, Seiner S, Henry M J Affect Disord. 2020; 271:109-114.

PMID: 32479305 PMC: 7289157. DOI: 10.1016/j.jad.2020.03.152.

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