Patel, Meenal J
(2014)
Multimodal MR Prediction Models for Late-Life Depression and Treatment Response.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Currently, depression diagnosis relies primarily on behavioral symptoms and signs, instead of underlying brain characteristics, and treatment is guided by trial and error instead of individual suitability associated with underlying brain characteristics. Also, previous brain-imaging studies attempting to resolve this issue have traditionally focused on mid-life depression using a single imaging modality and region-based approach, which may not fully explain the complexity of the underlying brain characteristics; especially for late-life depression. We aimed to evaluate and compare underlying brain characteristics of late-life depression diagnosis and treatment response by estimating accurate prediction models using multi-modal magnetic resonance imaging and non-imaging measures. Based on our finding, late-life depression diagnosis and treatment response predictors involve measures from different imaging modalities, which are indicative of differences in underlying brain characteristics.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
16 June 2014 |
| Date Type: |
Publication |
| Defense Date: |
25 March 2014 |
| Approval Date: |
16 June 2014 |
| Submission Date: |
7 April 2014 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
198 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Bioengineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
magnetic resonance imaging, machine learning, late-life depression |
| Date Deposited: |
16 Jun 2014 18:34 |
| Last Modified: |
19 Dec 2016 14:41 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/21094 |
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