28: Advanced 3D-CNN and ICA-Guided Model for Bipolar Disorder Diagnosis via Resting-State fMRI.

Music City Center 

Introduction: Bipolar Disorder (BD), previously known as manic-depressive illness, is a persistent mental health condition marked by intense mood fluctuations. These mood episodes range from emotional highs (mania or hypomania) to lows (depression), significantly affecting an individual's energy, activity levels, sleep patterns, behavior, and cognitive clarity. Methods: This study utilizes both resting-state functional MRI (rs-fMRI) and microscopic imaging techniques to capture detailed structural and functional insights into brain tissue samples. Microscopy was employed to analyze cellular structures in key brain regions, providing additional context for the rs-fMRI analysis. Independent Component Analysis (ICA) was applied to rs-fMRI scans from 45 healthy controls (HCs) and 45 BD subjects to identify significant features. The top features from five selected components were then used as inputs for a three-dimensional convolutional neural network (3D-CNN) model aimed at BD diagnosis. Results: Out of 90x5=450 independent components, the model was trained on 70% (315 components) and tested on the remaining 30% (135 components). Evaluation metrics confirmed high performance in disting

Bipolar disorder

rsfMRI

Independent Component Analysis (ICA)

Deep learning

Health risks 

Mental Health Statistics Section