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Prediction of the Cognitive Effects of Electroconvulsive Therapy Via Machine Learning and Neuroimaging

NCT03490149 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 180

Last updated 2022-05-25

No results posted yet for this study

Summary

The study aims to use machine learning to predict the occurrence of episodic and autobiographical memory deficits as well as treatment response following a course of electroconvulsive therapy. Additionally, the neurophysiological correlates of the cognitive effects after a course of ECT will be investigated.

Therefore, structural, resting-state and diffusion tensor images will be collected within one week before the first and after the last ECT treatment from severely depressed patients. Standard measures of cognitive function and specifically episodic as well as autobiographical memory will also be collected longitudinally and used for prediction. The study consists of 60 ECT receiving inpatients suffering from major unipolar or bipolar depression, 60 medication-only controls and 60 healthy controls.

Conditions

Interventions

DEVICE

Electroconvulsive Therapy

Series of electroconvulsive therapy for major depressive disorder

DRUG

Medication - Treatment as usual

Medication only sample - Treatment as usual

Sponsors & Collaborators

  • Maximilian Kiebs, M.Sc. - University Hospital Bonn (Department of Medical Psychology)

    collaborator UNKNOWN

  • University Hospital, Bonn

    lead OTHER

Principal Investigators

  • Rene Hurlemann, Prof. · University Hospital, Bonn

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-01-02
Primary Completion
2021-12-01
Completion
2022-12-01

Countries

  • Germany

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03490149 on ClinicalTrials.gov