Major depressive disorder (MDD) is a debilitating disease characterized by depressed mood, diminished interests, impaired cognitive function and vegetative symptoms, such as disturbed sleep or appetite. MDD affects one in six adults in their lifetime. To date, decisions regarding specific treatment protocols for MDD are based on clinical experience and risk factors with limited data on outcome prediction. In addition, since it takes 8 weeks to assess if a treatment is successful, the long and often unsuccessful search for an effective antidepressant is accompanied by significant decrease in patients' quality of life, an increased risk of suicidal action, and decreased chance of response and remission with each attempt. This has led to examination of various markers (e.g., neuroimaging, electrophysiological, genetic and behavioral) in an attempt to predict the response to various forms of treatments, including pharmacotherapies and TMS (Transcranial Magnetic Stimulation) for depression. Elminda had developed a novel, non-invasive imaging EEG-based technology, Brain Network Analytics (BNA), for visualization and quantification of specific brain functions. The rationale of the study is to develop a reliable marker for MDD treatment outcome based on the BNA.
Elminda was granted a specific funding from the European Union in the framework of the Horizon 2020 program (Grant No: 808677 \& 859051) to perform this study.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
NONE
Enrollment
390
Opti-Me algorithm provides the likelihood of response to the 3 treatment options: SSRIs, SNRIs or TMS in MDD patients.
Beer Ya'aqov
Be’er Ya‘aqov, Israel
RECRUITINGUniversity of Zurich Hospital
Zurich, Switzerland
RECRUITINGThe response to treatment based on the Montgomery-Asberg Depression Rating Scale (MADRS) defined as a decline of at least 50% in the MADRS score from Baseline (BL) to the end of treatment (EOT) period which will be defined per treatment type.
Time frame: up to 8 weeks
Accuracy of the model will be assessed by the specificity and sensitivity of the model developed based on Cohort 1(GR1) and implemented in Cohort 2 Arm A (GR2A).
Time frame: end of study, estimated 2 years
Comparison of the proportion of responders in Cohort 2 Arm A (GR2A) (assignment to treatment with the guidance of the Opti-Me algorithm) vs Cohort 1 (GR1) + Cohort 2 Arm B (GR2B) (random allocation to treatment).
Time frame: up to 8 weeks
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