Brain signatures identified by bottom-up unsupervised machine learning: three principal 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 magnetic resonance imaging (fMRI). We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal 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.

{"references": ["Kherif F, Poline JB, Flandin G, Benali H, Simon O, Dehaene S, et al. Multivariate model specification for fMRI data. Neuroimage. 2002;16(4):1068-83."]}