Obsessive-compulsive disorder (OCD) presents itself as a highly debilitating disorder. The disorder has common associations to the prefrontal cortex and the glutamate receptor known Metabotropic Glutamate Receptor 5 (mGluR5). This receptor has been observed to demonstrate higher levels of signaling from positron emission tomography scans measured by its distribution volume ratios. Though, studies are unable to verify the involvement of mGluR5. Computational modeling methods were used as a means of validation for previous hypotheses involving mGluR5. The inadequacies in relation to the causal factor of OCD were answered by utilizing T1 resting-state magnetic resonance imaging (TRS-MRI) scans of patients suffering from schizophrenia, major depressive disorder, and obsessive-compulsive disorder. Because comorbid cases often occur within these disorders, cross comparative abilities become necessary to find distinctive characteristics. After unique structures of tissues found in OCD TRS-MRI scans were identified, a gene expression analysis was conducted based on scan data output. Two-dimensional convolutional neural networks alongside ResNet50 and MobileNet models were constructed and evaluated for efficiency. Activation heatmaps of TRS-MRI scans were outputted, allowing for transcriptomics analysis. Though, a lack of ability of prediction of OCD cases prevented gene expression analysis. Across all models, there was an 88.75% validation accuracy for MDD, and 82.08% validation accuracy for SZD under the framework of ResNet50 as well as novel computation. OCD yielded an accuracy rate of ~54.4%. These results provided further evidence for the p factor theorem regarding mental disorders. Future work involves the application of transfer learning to bolster accuracy rates.