Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electroconvulsive Therapy

Electroconvulsive Therapy

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Shared and specific associations of amygdala nuclei volumes with PTSD symptom domains and childhood trauma: An ENIGMA-PGC PTSD mega-analysis.

Molecular psychiatry·2026

Same author

Disrupted intrathalamic and thalamocortical structural covariance networks in posttraumatic stress disorder.

Network neuroscience (Cambridge, Mass.)·2026

Same author

Whole-brain mapping of TMS-induced discomfort with large-scale concurrent TMS-fMRI.

bioRxiv : the preprint server for biology·2026

Same author

Multimodal evidence for hippocampal engagement and modulation by functional connectivity-guided parietal TMS.

Nature communications·2026

Same author

Childhood Maltreatment and Deviations From Normative Brain Structure: A Mega-Analysis of 3711 Individuals From the ENIGMA Major Depressive Disorder and ENIGMA Posttraumatic Stress Disorder Working Groups.

Biological psychiatry·2026

Same author

Lower Gyrification of the Ventromedial Prefrontal Cortex in Posttraumatic Stress Disorder: An ENIGMA-PTSD Study.

Biological psychiatry global open science·2026

Same journal

Predicting Chemotherapy Response from Staging Laparoscopy Images.

medRxiv : the preprint server for health sciences·2026

Same journal

Development and External Validation of a Machine Learning Model for 10-Year Ischemic Stroke Risk Prediction in Diverse Populations.

medRxiv : the preprint server for health sciences·2026

Same journal

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer's Disease Neuroimaging Initiative.

medRxiv : the preprint server for health sciences·2026

Same journal

Genetic and maternal environmental contributions to estimated fetal weight at 20 weeks gestation compared with birthweight.

medRxiv : the preprint server for health sciences·2026

Same journal

Better immediate declarative memory is associated with forgetting during locomotor adaptation in chronic stroke and in older adults.

medRxiv : the preprint server for health sciences·2026

Same journal

An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 27, 2025

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

Published on: August 11, 2015

Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response.

Xiaoyu Tong¹, Hua Xie^2,3, Wei Wu⁴

¹Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.

Medrxiv : the Preprint Server for Health Sciences

|June 9, 2023

Summary

This summary is machine-generated.

This study used normative modeling of brain activity to predict how well patients with major depressive disorder (MDD) respond to antidepressants. The approach successfully identified individuals likely to benefit from treatment, paving the way for personalized psychiatry.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Related Experiment Videos

Last Updated: Jul 27, 2025

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

Published on: August 11, 2015

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Area of Science:

Neuroscience
Psychiatry
Computational Biology

Background:

Major depressive disorder (MDD) treatments show limited efficacy due to unknown mechanisms and patient variability.
Personalized psychiatry requires predicting individual treatment responses.
Normative modeling quantifies deviations from healthy brain function.

Approach:

Developed a normative model using resting-state electroencephalography (EEG) connectivity data from healthy controls across three cohorts.
Characterized individual MDD patient deviations from normative data.
Trained sparse predictive models to forecast treatment outcomes based on these deviations.

Key Points:

Successfully predicted treatment outcomes for sertraline and placebo groups (r = 0.43 and r = 0.33, respectively).
The framework distinguished between subclinical and diagnostic variability.
Identified key resting-state EEG connectivity signatures associated with antidepressant treatment response.

Conclusions:

The normative modeling framework offers a generalizable approach for predicting MDD treatment response.
Advances neurobiological understanding of antidepressant action pathways.
Enables more targeted and effective personalized treatments for MDD.