Comparative analysis of building Pearson and Canonical correlation functional connectivity matrices for classification tasks in neuroimaging

Abstract Machine learning (ML) methodologies offer significant potential for addressing the intricate challenges inherent in the analysis of neuroimaging data within the realm of neurological research. Nonetheless, the effective application of these techniques is markedly contingent upon the particular task and dataset under examination, and the absence of standardized methodologies poses impediments to cross-study result comparisons. This study contributes substantively to the collective endeavor by conducting a comprehensive evaluation and comparative analysis of ML models in the context of predicting schizophrenia and autism spectrum disorder (ASD) utilizing distinct functional Magnetic Resonance Imaging (fMRI) datasets. In this research, we introduce Canonical Correlation Analysis (CCA) as an innovative modality to augment the classification of these multifaceted neurological conditions. By elucidating the efficacy of CCA in ameliorating classification accuracy within the framework of Support Vector Machines (SVM), our study endeavors to propel the domain of neuroimaging and deepen our understanding of these intricate neurological disorders.