  Obsessive-compulsive disorder (OCD) presents itself as a highly debilitating
disorder. The disorder has common associations with the prefrontal cortex and
the glutamate receptor known as 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
in mice. Despite this evidence, studies are unable to fully verify the
involvement of mGluR5 as more empirical data is needed. 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. 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 to predict 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 around 54.4%. These results provided further evidence for the p-factor
theory regarding mental disorders. Future work involves the application of
alternate transfer learning networks than those used in this paper to bolster
accuracy rates.
