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Electroconvulsive Therapy

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Electroconvulsive therapy (ECT), or shock therapy, remains a critical biomedical intervention for severe, treatment-resistant depression. While its origins can be traced back to Hippocrates' observations that malaria-induced convulsions alleviated mental illness, modern ECT has evolved significantly from its earlier, more primitive applications. First introduced in 1938 by Ugo Cerletti and his colleagues, ECT involves inducing controlled seizures using electrical currents. In its early...

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Related Experiment Video

Updated: Jul 16, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Cingulate dynamics track depression recovery with deep brain stimulation.

Sankaraleengam Alagapan¹, Ki Sueng Choi^2,3,4, Stephen Heisig²

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

|September 21, 2023

Summary

This summary is machine-generated.

Deep brain stimulation (DBS) for treatment-resistant depression (TRD) shows promise. New brain biomarkers from subcallosal cingulate (SCC) recordings help personalize treatment and predict recovery, improving patient outcomes.

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Last Updated: Jul 16, 2025

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Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

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Published on: October 4, 2016

Area of Science:

Neuroscience
Biomarkers
Artificial Intelligence

Background:

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) offers long-term relief for treatment-resistant depression (TRD).
Current treatment lacks objective biomarkers, relying on subjective reporting and trial-and-error adjustments.
Individual recovery from TRD is unpredictable, necessitating personalized therapeutic strategies.

Purpose of the Study:

To develop and validate objective brain-based biomarkers for guiding SCC DBS in TRD.
To identify predictors of variable recovery trajectories in TRD patients undergoing DBS.
To correlate electrophysiological and behavioral markers with clinical states in TRD.

Main Methods:

Implemented a novel device for electrophysiology recording during SCC DBS in 10 TRD participants.
Utilized explainable artificial intelligence (AI) to analyze SCC local field potentials (LFPs) for clinical state detection.
Employed data-driven video analysis to detect objective facial expression changes.

Main Results:

90% of participants achieved clinical response and 70% remission at 24 weeks.
An AI-derived LFP biomarker accurately reflected individual patient recovery states, distinct from stimulation effects.
Preoperative white matter integrity and functional connectivity predicted recovery variability, mirrored by facial expression changes.

Conclusions:

Objective biomarkers derived from SCC LFPs can personalize SCC DBS management for TRD.
Multifaceted (functional, anatomical, behavioral) features of TRD pathology influence treatment variability.
Further research is warranted to understand the causes of treatment variability in depression.