CNN-based Neuroimaging Analysis for Mapping Neurotransmitter Dysregulation in Anxiety Disorders

This research examines the use of Convolutional Neural Networks (CNNs) in analyzing neuroimaging data to identify neurotransmitter dysregulation in anxiety disorders. Neurotransmitter abnormalities significantly contribute to anxiety disorders, but identifying these imbalances by imaging techniques is challenging. A CNN-based model was created using functional neuroimaging data (e.g., fMRI, PET scans) to detect patterns of neurotransmitter dysregulation. The model was trained on a dataset of neuroimaging scans from individuals with anxiety disorders, using preprocessing processes such as spatial smoothing and normalization. Various CNN models were evaluated, with a conventional CNN chosen for its optimal mix of accuracy and computing economy. The CNN model accuracy of 94.7% detects neurotransmitter dysregulation from normal brain activity. CNN showed the capacity to generalize to novel patient populations with varied demographic and genetic profiles. The proposed model presents a significant tool for enhancing diagnostic accuracy and directing personalized treatment approaches in anxiety disorders.