Major Depressive Disorder
- 3.1 Introduction 47
- 3.2 Importance of Studying MDD 48
- 3.3 Problem Statement 50
- 3.4 Software/Hardware 50
- 3.5 Experiment Design and Protocol 52
- 3.5.1 Target Population 52
- 3.5.2 Inclusion Criteria 53
- 3.5.3 Exclusion Criteria 53
- 3.5.4 Clinical Questionnaires 53
- 3.5.5 Sample Size Computation 53
- 3.5.6 Participant Recruitment 54
- 3.5.7 Ethics Approval 54
- 3.6 Experimental Tasks and Procedure 55
- 3.6.1 Procedure/Methodology 55
- 3.7 Data Description 58
- 3.8 Relevant Papers 58
Treatment management for major depressive disorder (MDD) has been challenging and has been associated with low treatment efficacy. Antidepressants, selective serotonin reuptake inhibitors (SSRIs), are considered to be the first line of treatment for MDD. There are more than 20 different medicines for SSRIs that are available commercially. However, the challenge lies in the appropriate selection of an antidepressant for the MDD patient. According to the existing clinical practice, the selection is based on subjective assessment with less scientific evidence. There is no guarantee that the selected antidepressant would work for the patient. To improve upon this situation, we have proposed utilization of elec- troencephalographic (EEG) data from the patients at baseline. EEG has the capability to capture the changes occurring inside the brain due to
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Designing EEG Experiments for Studying the Brain.
depression and anxiety.1 However, extracting useful information from raw EEG requires computational techniques to analyze and infer such changes.2,3 As a result, the evidence about the treatment outcome from a certain antidepressant such as SSRIs is generated, which is helpful for the psychiatrist as a second opinion. Selection of appropriate antidepressants at pretreatment stage would sufficiently reduce the multiple trials and would improve the treatment efficacy associated with MDD. In this chapter, details about this project are provided, such as sample size calculation, details about experiment design, data acquisition, and study participants.