Classification of Schizophrenia and Alzheimer's Disease using Resting-State Functional Network Connectivity

Abstract

Neuroimaging studies in Alzheimer's disease (AD) and schizophrenia (SZ) have compared AD or SZ subjects against control (CN) subjects. However, it is also of interest and more critical to identify potential biomarkers by comparing these disorders, which can share some overlap, to each other directly. In this study, we investigated similarities and differences in resting-state functional network connectivity (rs-FNC) between 162 AD + late mild cognitive impairment (LMCI) and 181 SZ subjects from two well-known datasets - Alzheimer's Disease Neuroimaging Initiative (ADNI) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP). We applied standard machine learning algorithms on confounder-controlled FNC features to distinguish groups of subjects, achieving an accuracy of 89% in classifying AD+LMCI vs. SZ subjects and an accuracy of 68% in a three-way classification of AD+LMCI, SZ, and CN subjects. Our results indicate that support vector machine (SVM) with an RBF kernel outperforms linear SVM and other machine learning methods, including random forest (RF), logistic regression (LR), and k-nearest neighbor (KNN). Furthermore, we conducted experiments for monitoring the potential impact of biases and showed that our trained models perform reasonably in a dataset-agnostic way. Finally, our findings highlight cerebellum and cognitive control networks as notable domains in common and unique FNC changes in AD and SZ disorders.