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• W2016557887 abstract "Hemodynamic changes in the brain have been reported in major psychosis in respect to healthy controls, and could unveil the basis of structural brain modifications happening in patients. The study of first episode psychosis is of particular interest because the confounding role of chronicity and medication can be excluded. The aim of this work is to automatically discriminate first episode psychosis patients and normal controls on the basis of brain perfusion employing a support vector machine (SVM) classifier. 35 normal controls and 35 first episode psychosis underwent dynamic susceptibility contrast magnetic resonance imaging, and cerebral blood flow and volume, along with mean transit time were obtained. We investigated their behavior in the whole brain and in selected regions of interest, in particular the left and right frontal, parietal, temporal and occipital lobes, insula, caudate and cerebellum. The distribution of values of perfusion indexes were used as features in a support vector machine classifier. Mean values of blood flow and volume were slightly lower in patients, and the difference reached statistical significance in the right caudate, left and right frontal lobes, and in left cerebellum. Linear SVM reached an accuracy of 83% in the classification of patients and normal controls, with the highest accuracy associated with the right frontal lobe and left parietal lobe. In conclusion, we found evidence that brain perfusion could be used as a potential marker to classify patients with psychosis, who show reduced blood flow and volume in respect to normal controls." @default.
• W2016557887 created "2016-06-24" @default.
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• W2016557887 date "2015-06-01" @default.
• W2016557887 modified "2023-09-26" @default.
• W2016557887 title "The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis" @default.
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• W2016557887 doi "https://doi.org/10.1016/j.schres.2015.03.017" @default.
• W2016557887 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25888338" @default.
• W2016557887 hasPublicationYear "2015" @default.
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