Local activity features for computer aided diagnosis of schizophrenia on resting-state fMRI

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions, such as Schizophrenia. The machine learning approach followed in this paper consists in performing feature extraction and subsequent classification experiments. Feature extraction methods that preserve spatial information allow to recover the anatomical localization of the voxels that provide discriminant information. Such locations may be further studied to assess their biological meaning as biomarkers for the disease. The power of this approach lies in the predictive accuracy of the classifier, so that features leading to higher accuracy results are assumed to have greater value as biomarkers. In this paper we apply this approach to brain local activity measures computed over rs-fMRI data from Schizophrenia patients and healthy control subjects obtained from a publicly available database (COBRE), which allows for the confirmation or falsification of our results. The extensive experimental work provides evidence that local activity measures, such as Regional Homogeneity (ReHo), may be useful for the intended purposes.