  Schizophrenia and bipolar disorder are debilitating psychiatric illnesses
that can be challenging to diagnose accurately. The similarities between the
diseases make it difficult to differentiate between them using traditional
diagnostic tools. Recently, resting-state functional magnetic resonance imaging
(rsfMRI) has emerged as a promising tool for the diagnosis of psychiatric
disorders. This paper presents several methods for differentiating
schizophrenia and bipolar disorder based on features extracted from rsfMRI
data. The system that achieved the best results, uses 1D Convolutional Neural
Networks to analyze patterns of Intrinsic Connectivity time courses obtained
from rsfMRI and potentially identify biomarkers that distinguish between the
two disorders. We evaluate the system's performance on a large dataset of
patients with schizophrenia and bipolar disorder and demonstrate that the
system achieves a 0.7078 Area Under Curve (AUC) score in differentiating
patients with these disorders. Our results suggest that rsfMRI-based
classification systems have great potential for improving the accuracy of
psychiatric diagnoses and may ultimately lead to more effective treatments for
patients with this disorder.
