Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
David A.A. Baranger,
Yaroslav O. Halchenko,
Skye Satz,
Rachel Ragozzino,
Satish Iyengar,
Holly A. Swartz,
Anna Manelis
Affiliations
David A.A. Baranger
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA; Corresponding authors.
Yaroslav O. Halchenko
Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
Skye Satz
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
Rachel Ragozzino
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
Satish Iyengar
Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
Holly A. Swartz
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
Anna Manelis
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA; Corresponding authors.
The T1w/T2w ratio is a novel magnetic resonance imaging (MRI) measure that is thought to be sensitive to cortical myelin. Using this novel measure requires developing novel pipelines for the data quality assurance, data analysis, and validation of the findings in order to apply the T1w/T2w ratio for classification of disorders associated with the changes in the myelin levels. In this article, we provide a detailed description of such a pipeline as well as the reference to the scripts used in our recent report that applied the T1w/T2w ratio and machine learning to classify individuals with depressive disorders from healthy controls.
