Cross-Validation of Functional MRI and Paranoid-Depressive Scale: Results From Multivariate Analysis - Stoyanov, Drozdstoy; Kandilarova, Sevdalina; Paunova, Rositsa; Barranco Garcia, Javier; Latypova, Adeliya; Kherif, Ferath (Frontiers in Psychiatry, 2019)
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.