There is still a great deal that is unknown about various depressive conditions, though it is a very common affliction and cause of disability throughout the world. Not only do the underlying mechanisms of various types of depression remain uncertain, but the mystery of how different treatment options work and who will respond to them also persists. The aim of this study was to identify potential non-invasive biomarkers, to predict responsiveness to electroconvulsive therapy. Two hypotheses were investigated in this study. The first was that patient improvement from baseline on the neurocognitive, computer based pattern separation task prior to the third ECT treatment will correlate with a clinical antidepressant response. The second was that increased prefrontal slowing relative to baseline will correlate with a decrease in depressive symptoms. As a first step to validate this approach, a healthy control group performed both the pattern separation and EEG tasks once per week over the course of three weeks. Patient participants completed both tasks before their first ECT treatment, prior to their third treatment, and prior to their last treatment. A spectral analysis of EEG data was then conducted. Results indicated good test-retest reliability for the pattern separation task and EEG measurements across all three trials in the healthy control group. Results from patient data are inconclusive, but indicates that there is a change from baseline to subsequent trials for at least the EEG measurements. However, a larger sample size is needed to determine this. The limited results from this small patient sample suggest that these measurements may have clinical value in refining ECT treatment, and merit further study.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/23748 |
| Date | 12 July 2017 |
| Creators | Davis, Kathryn |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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