Clinical Trials Logo

Clinical Trial 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.


Clinical Trial Description

Due to the immense disease burden of major depression and unsatisfactory response to standard pharmacological and psychological treatments, the need for treatment alternatives is evident. Electroconvulsive therapy (ECT) remains to be the most efficacious treatment known for treatment-resistant depression. However, although many studies show response rates above 70%, ECT can be considered vastly underused. Reasons contributing to this phenomenon may include stigma, regulatory restrictions, limited medical training, safety and side-effect concerns, or reluctance among professionals to recommend ECT. Most of these reasons have already been refuted or put into perspective by psychological and neuroscientific studies (e.g. ECT causes brain lesions) and most cognitive deficits related to the ECT course seem to fade after several weeks of discontinuation. Still, in terms of the tolerability, memory disturbances remain the most problematic effect of ECT. Besides subjective reports from patients after a course of ECT, experimental studies have also found evidence of episodic and autobiographical memory impiarment attributable to ECT. The origins of these effects are still largely unknown and remain a goal for further research. It has now been shown that structural T1 weighted MR-images can be used to predict the response to a course of ECT via machine learning. Therefore, this study aims to use machine learning to predict the occurrence of episodic and specifically autobiographical memory deficits arising within a course of electroconvulsive therapy based on MR-images collected within one week before the first ECT treatment from severely depressed patients. Additionally, the neurophysiological correlates of the cognitive effects modulated by a course of ECT will be investigated longitudinally through the use of structural, resting-state and diffusion tensor images. The study consists of 60 ECT receiving inpatients suffering from major unipolar or bipolar depression. If successful, this line of research should lead to a better tolerability of ECT by aiding in the complex decision making process involved in prescribing ECT as well as the parameter setting within a treatment course (e.g. uni- vs. bilateral). ;


Study Design


Related Conditions & MeSH terms


NCT number NCT03490149
Study type Observational
Source University Hospital, Bonn
Contact Maximilian Kiebs, M.Sc.
Phone 0049228287
Email m.kiebs@ukbonn.de
Status Recruiting
Phase
Start date January 2, 2018
Completion date December 1, 2022

See also
  Status Clinical Trial Phase
Active, not recruiting NCT05777044 - The Effect of Hatha Yoga on Mental Health N/A
Recruiting NCT04680611 - Severe Asthma, MepolizumaB and Affect: SAMBA Study
Recruiting NCT04977232 - Adjunctive Game Intervention for Anhedonia in MDD Patients N/A
Recruiting NCT04043052 - Mobile Technologies and Post-stroke Depression N/A
Completed NCT04512768 - Treating Comorbid Insomnia in Transdiagnostic Internet-Delivered Cognitive Behaviour Therapy N/A
Recruiting NCT03207828 - Testing Interventions for Patients With Fibromyalgia and Depression N/A
Completed NCT04617015 - Defining and Treating Depression-related Asthma Early Phase 1
Recruiting NCT06011681 - The Rapid Diagnosis of MCI and Depression in Patients Ages 60 and Over
Completed NCT04476446 - An Expanded Access Protocol for Esketamine Treatment in Participants With Treatment Resistant Depression (TRD) Who do Not Have Other Treatment Alternatives Phase 3
Recruiting NCT02783430 - Evaluation of the Initial Prescription of Ketamine and Milnacipran in Depression in Patients With a Progressive Disease Phase 2/Phase 3
Recruiting NCT05563805 - Exploring Virtual Reality Adventure Training Exergaming N/A
Completed NCT04598165 - Mobile WACh NEO: Mobile Solutions for Neonatal Health and Maternal Support N/A
Completed NCT03457714 - Guided Internet Delivered Cognitive-Behaviour Therapy for Persons With Spinal Cord Injury: A Feasibility Trial
Recruiting NCT05956912 - Implementing Group Metacognitive Therapy in Cardiac Rehabilitation Services (PATHWAY-Beacons)
Completed NCT05588622 - Meru Health Program for Cancer Patients With Depression and Anxiety N/A
Recruiting NCT05234476 - Behavioral Activation Plus Savoring for University Students N/A
Active, not recruiting NCT05006976 - A Naturalistic Trial of Nudging Clinicians in the Norwegian Sickness Absence Clinic. The NSAC Nudge Study N/A
Enrolling by invitation NCT03276585 - Night in Japan Home Sleep Monitoring Study
Completed NCT03167372 - Pilot Comparison of N-of-1 Trials of Light Therapy N/A
Terminated NCT03275571 - HIV, Computerized Depression Therapy & Cognition N/A