Schizophrenia patients differentiation based on MR vascular perfusion and volumetric imaging

Candecomp/Parafac Decomposition (CPD) has emerged as a framework for modeling N-way arrays (higher-order matrices). CPD is naturally well suited for the analysis of data sets comprised of observations of a function of multiple discrete indices. In this study we evaluate the prospects of using CPD for modeling MRI brain properties (i.e. brain volume and gray-level) for schizophrenia diagnosis. Taking into account that 3D imaging data consists of millions of pixels per patient, the diagnosis of a schizophrenia patient based on pixel analysis constitutes a methodological challenge (e.g. multiple comparison problem). We show that the CPD could potentially be used as a dimensionality redaction method and as a discriminator between schizophrenia patients and match control, using the gradient of pre- and post Gd-T1-weighted MRI data, which is strongly correlated with cerebral blood perfusion. Our approach was tested on 68 MRI scans: 40 first-episode schizophrenia patients and 28 matched controls. The CPD subject’s scores exhibit statistically significant result (P < 0.001). In the context of diagnosing schizophrenia with MRI, the results suggest that the CPD could potentially be used to discriminate between schizophrenia patients and matched control. In addition, the CPD model suggests for brain regions that might exhibit abnormalities in schizophrenia patients for future research.