Comparing energy levels in brain regions of interest in ADHD subjects

Abstract

Energy Landscape analysis is a statistical method of examining networks in the brain. In this study we apply this technique to analyze and compare resting state fMRI brain scan data from brain Regions of Interest (ROIs) in subjects with Attention Deficit Hyperactivity Disorder (ADHD) to the same ROI activity in a control group of similar genders and ages. 192 temporal data samples were extracted from the fMRI data and using masks 90 spatial AAL ROIs were isolated for each of the fifty subjects. These (90 x 50) ROI time data files were then combined into one file per subject, and then concatenated into two groups: ADHD and Control. The two concatenated files were then normalized, binarized, and processed with the Energy Landscape Analysis Toolbox (ELAT) by Takahiro Ezaki. The ELAT analysis produced Activation Maps, Disconnectivity Graphs, and Local Minima figures showing the comparative energy levels of the ROIs grouped into nine separate sub-networks. These results were then compared between groups to obtain candidate biomarkers that could be used to distinguish the ADHD subjects from the Control group. Our results point to significant differences in the energy levels of brain states between the two groups in the Salience (SAL), Frontoparietal (FPN), and Attention (ATN) sub-networks. These areas of the brain are associated with language, self-awareness, spatial attention, emotions, and social behavior.