prediction of antidepressant effects of electroconvulsive therapy

Conditions: Depressive Disorder, Major

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.


Eligibility criteria:
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

Keywords: electroconvulsive therapy neuroimaging response prediction biomarker