CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
Description
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.
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http://data.europa.eu/88u/dataset/oai-zenodo-org-3497072 |
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http://data.europa.eu/88u/dataset/oai-zenodo-org-3497072 |
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http://data.europa.eu/88u/dataset/oai-zenodo-org-3497072 |
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http://data.europa.eu/88u/dataset/oai-zenodo-org-3497072 |
Tags
- mental-disorders
- fmri
- multivariate-linear-model
- paranoid-depressive-psychiatry
- machine-learning