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Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD)
- Source :
- PLoS ONE, PLoS ONE, Vol 10, Iss 7, p e0132958 (2015)
- Publication Year :
- 2015
- Publisher :
- Public Library of Science (PLoS), 2015.
-
Abstract
- The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD) diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of 'treatment resistance' in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S) clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD.
- Subjects :
- Male
medicine.medical_specialty
Support Vector Machine
lcsh:Medicine
Grey matter
Depressive Disorder, Treatment-Resistant
Physical medicine and rehabilitation
Neuroimaging
Region of interest
Humans
Medicine
Gray Matter
Medical diagnosis
lcsh:Science
Psychiatry
Depression (differential diagnoses)
Multidisciplinary
medicine.diagnostic_test
business.industry
lcsh:R
Magnetic resonance imaging
Voxel-based morphometry
Middle Aged
Magnetic Resonance Imaging
medicine.anatomical_structure
Case-Control Studies
lcsh:Q
Female
business
Insula
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....df9e1081cb68b5e01e4a965e41ec43d9