Computer-aided diagnostics of schizophrenia: Comparison of different feature extraction methods

Abstract

Receiving an early diagnosis of schizophrenia is a crucial step towards its treatment. However, in current thinking, the diagnosis is based on time-consuming criteria, burdened with subjectivity. Hence, objective and more reliable therapeutic tests are desirable for the clinical practice of Psychiatry. Since schizophrenia is characterized by progressive brain volume changes during the course of the disease, many studies have recently turned attention to machine learning and brain morphometric techniques serving as tools for computer-aided diagnosis of schizophrenia based on neuroimaging data. In our study, the methodology is applied to distinguish between 52 first-episode schizophrenia patients and 52 healthy volunteers on the basis of T1-weighted magnetic resonance images of their brains preprocessed by the means of voxel-based and deformation-based morphometry. The proposed classification schemes vary in the feature extraction and selection steps. Namely, Mann-Whitney testing is implemented as a simple univariate approach playing the role of a comparator to multivariate methods such as inter-subject PCA, the K-SVD algorithm, and pattern-based morphometry. The highest classification accuracy, 70%, is reached with the pattern-based morphometry technique. The study points out the difference between univariate and multivariate approaches towards neuroimaging data. Additionally, the contrast between feature extraction capabilities of voxel-based and deformation-based morphometry is demonstrated.