Energy landscape analysis of fMRI data from Schizophrenic and healthy subjects

Abstract

Brain connectivity biomarkers are powerful tools for not only identifying neuropsychiatric disorders in patients but also validating treatment effectiveness. In this work, we used energy landscape techniques to analyze resting state fMRI data collected from 107 healthy control (HC) and 86 Schizophrenia patients (SZ). Activity patterns and disconnectivity graphs were obtained from 264 ROIs and 180-second fMRI time course of each subject. Statistics of individual and subgroups’ inter-network and intra-network connections of Auditory Network (AUD), Attention Network (ATN), Default – Mode Network (DMN), Frontoparietal Network (FPN), Salience Network (SAN), Sensorimotor Network (SSM), and Visual Network (VIS) were analyzed. For inter-network results we found that the DMN and ATN of SZ are strongly coupled. But for HC, a stable brain states that the ATN, SAN, and FPN are coupled as a group and anti-correlated with the other coupled group of DMN, SSM, VIS, and AUD. For intra-networks we found that in FPN, controls have more flexibility to allow the Inferior Frontal Gyrus independently working together with the Superior Temporal Gyrus. In FPN we found that regions that process language and regions that process motor and planning can sometimes be decoupled in SZ. In SMN, some controls can accomplish a brain state to separate voluntary and autopilot activities. In VIS, controls have the ability to separate lower-level visual processing from working memory, motor planning, and guided coordination, whereas patients mixed some of them together, suggesting lack of self-awareness and self-constraint.