Original Research ARTICLE
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: RESULTS FROM MULTIVARIATE ANALYSIS
- 1Department of Psychiatry and Medical Psychology, Plovdiv Medical University, Bulgaria
- 2Lausanne University Hospital (CHUV), Switzerland
- 3Plovdiv Medical University, Bulgaria
The objective of the study is to construct a bottom-up unsupervised machine learning approach, where the brain signatures identified by three principle components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional MRI.
We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principle component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.
Keywords: Validation, Psychopathology, machine learning, functional MRI, Classifi cation
Received: 23 Jul 2019;
Accepted: 04 Nov 2019.
Copyright: © 2019 Stoyanov, Kherif, Kandilarova and Paunova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Drozdstoy S. Stoyanov, Plovdiv Medical University, Department of Psychiatry and Medical Psychology, Plovdiv, 4002, Bulgaria, email@example.com