Prediction of Antidepressant Effects of Electroconvulsive Therapy

Depressive Disorder, Major Clinical Trial

Official title:

Prediction of Antidepressant Effects of Electroconvulsive Therapy

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05630469
• Other study ID #: KLI 1098-B
• Status: Recruiting
• Start date: December 1, 2022
• Est. completion date: November 30, 2025

Study information

• Verified date: March 2023
• Source: Medical University of Vienna
• Contact: Pia Baldinger-Melich, MD, PhD, PD, Assoc.Prof.
• Phone: +43 1 40400 35350
• Email: pia.baldinger-melich[@]meduniwien.ac.at
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Despite its successful use for more than 80 years, the mechanisms of action of electroconvulsive therapy (ECT) are still not fully understood. ECT has been shown to be accompanied by changes in regional brain volumes and connectivity measures, as well as biochemical alterations. However, how these changes relate to ECT response remains to be further elucidated; up to now, there are no objective markers for the targeted use of ECT in clinical practice. Methods: Study design: longitudinal mono-centre study with duration of 36 months. Subjects: 30 depressed patients (aged 18-65 years) eligible for ECT. Measurements: subjects will undergo 2 3-Tesla MRI scans (one before and one after a course of ECT), including structural MRI, resting-state functional MRI, task-based functional MRI and MR spectroscopy. Blood, CSF sampling and clinical assessments will be performed once before and once after the ECT course. ECT: Each patient will be treated in a min. of 8 bitemporal ECT sessions (~4 weeks). Data analysis: Longitudinal changes in brain imaging parameters and laboratory measures (before/after ECT) will be assessed using repeated-measures analysis of covariance. Machine learning with random forests will be employed to identify a pattern of pretreatment imaging, biochemical (serum and CSF) and clinical parameters that are best qualified to predict response to ECT as defined by a reduction of ≥50% of baseline HAMD17. Hypotheses: 1. ECT will be accompanied by changes in brain morphology, functional connectivity, neuronal activation in response to cognitive and reward-related stimuli and neurochemical signals in the brain. 2. ECT leads to changes in blood- and CSF-based markers of neuronal plasticity, neurodegeneration and inflammation, as well as genetic/epigenetic markers. 3. Predictive markers of ECT response can be established based on the relationships between imaging, neurochemical and clinical markers and treatment response. Innovation: This study would be the first to combine multimodal MRI measures with the assessment of biomarkers in the CSF in the context of ECT. The implementation of the proposed trial represents an important step towards a better understanding of the powerful antidepressant properties of ECT. By relating treatment effects and potentially underlying biological mechanisms on numerous complementary levels, the study might help to identify biomarkers that distinguish patients who are likely to benefit from ECT from non-responders. Ultimately, results of the study might be useful in order to establish an individualized medical indication for ECT.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 30
• Est. completion date: November 30, 2025
• Est. primary completion date: June 30, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 65 Years

Eligibility

Inclusion Criteria:

• male or female inpatients (18-65 years old)
• ICD-10 diagnosis of severe unipolar depression (F32.2, F32.3, F33.2, F33.3)
• HAMD17 = 23
• physical health
• ability to understand and willingness to sign the written informed consent document
• negative drug screening, negative urine pregnancy test in women
• antidepressant and antipsychotic medication in steady state for at least 10 days prior inclusion
• anesthesiological approval for ECT

Excusion criteria:

• severe somatic or neurological disease
• current comorbid psychiatric disorder, including bipolar depression (according to DSM-5)
• current or past history of schizophrenia or schizoaffective disorder
• current use of alcohol, drugs of abuse, or any medication in a manner which is indicative of chronic abuse or who meet DSM-5 criteria for alcohol or other substance dependence
• clinically relevant abnormalities on a general physical examination and routine laboratory screening
• pregnancy, breast feeding
• contraindications to lumbar puncture (increased intracranial volume, anticoagulant medication, uncorrected bleeding diathesis, coagulopathy, congenital spine abnormality, recent spinal trauma/surgery, skin infection at puncture site)
• any implant or stainless steel graft contraindicated for MRI

Related Conditions & MeSH terms

• Depressive Disorder
• Depressive Disorder, Major

Intervention

Other:
• Electroconvulsive therapy
repetitive induction of a generalized seizure under controlled conditions (muscle relaxation and sedation)

Locations

Country	Name	City	State
Austria	Department of Psychiatry and Psychotherapy, Medical University of Vienna	Vienna

Sponsors (1)

Lead Sponsor	Collaborator
Medical University of Vienna

Country where clinical trial is conducted

Austria, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	imaging parameter - sMRI	cortical thickness	3 years
Primary	imaging parameters - DTI	mean diffusivity	3 years
Primary	imaging parameters - fMRI	BOLD activity (task-based fMRI for cognition and anhedonia)	3 years
Primary	imaging parameters - rsfMRI	BOLD activity (rsfMRI)	3 years
Primary	imaging parameters - MRS	Glx, NAA and GABA concentrations (MRS)	3 years
Primary	biochemical parameters - neurodegeneration	concentrations of markers of neurodegeneration serum and CSF	3 years
Primary	biochemical parameters - inflammation	concentrations of markers of neuroinflammation serum and CSF	3 years
Primary	biochemical parameters - neurplasticity	concentrations of markers of neuroplasticity serum and CSF	3 years
Primary	response prediction	machine learning - how do outcome 1 and 2 relate to response to ECT	3 years