# Naturalistic Monitoring of Resting State Functional Connectivity in Electroconvulsive Therapy

> **NIH NIH R21** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2020 · $404,865

## Abstract

Project Summary/Abstract
Despite its efficacy in severe depression, electroconvulsive therapy (ECT) is a medically
intensive procedure with medical risk associated with general anesthesia and additional
risk of transient and persistent/permanent cognitive difficulties. Although ECT is the most
effective known somatic treatment for major depression, its response rate is estimated to
be 50-70% in the general psychiatric practice, and a system to predict response would
be of significant medical value. Many predictors of response to ECT have been
proposed, but to date, have manifested only weak potential to discriminate which
depressed patients are likely to benefit from ECT. Although the mechanisms by which
ECT exerts its therapeutic effects in major depression are unknown, the functional
neural circuitry (FC) underlying major depression and its treatment with ECT have
become increasingly better defined. This represents an unexplored opportunity to
develop a FC biomarker upon which to base recommendation of ECT and monitor the
need for relapse prevention post ECT as well. To our knowledge, this is the first attempt
to address this need for a predictive biomarker using functional connectivity.
In the current R21 proposal, we propose to study whether a predictive model of brain
functional connectivity patterns determined by fMRI, and trained on a pilot sample of
depressed patients undergoing ECT can predict response to ECT and monitor relapse
after response to ECT to a degree that is clinically useful in a larger sample.
This is a prospective observation of depressed inpatients scheduled to undergo ECT.
Participants will be examined with rsfMRI before the treatment course and once it is
complete. We will test predictive analytic techniques developed in a prior pilot rsfMRI to
see whether they discriminate responders from non-responders in this larger sample. In
the follow-up period, we will use the same techniques to explore whether they can
identify those patients at risk for relapse. The ultimate goal is to develop an effective
biomarker to predict which patients could benefit from ECT as well as to monitor
potential relapse and the need for further intervention.

## Key facts

- **NIH application ID:** 9957640
- **Project number:** 1R21MH122832-01
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** JOAN PRUDIC
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $404,865
- **Award type:** 1
- **Project period:** 2020-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9957640

## Citation

> US National Institutes of Health, RePORTER application 9957640, Naturalistic Monitoring of Resting State Functional Connectivity in Electroconvulsive Therapy (1R21MH122832-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9957640. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*