High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

• Published in: NeuroImage (Orlando, Fla.) Vol. 102; no. 1; pp. 35 - 48
• Main Authors: Plis, Sergey M., Sui, Jing, Lane, Terran, Roy, Sushmita, Clark, Vincent P., Potluru, Vamsi K., Huster, Rene J., Michael, Andrew, Sponheim, Scott R., Weisend, Michael P., Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.11.2014; Elsevier Limited
• Subjects: Adult; Brain - physiopathology; Female; fMRI; Heuristic; High-order interactions; Humans; Linear algebra; Male; Methods; Multi-task data; Nerve Net - physiology; Nonparametric estimators; Random variables; Schizophrenia - physiopathology; Task Performance and Analysis; High-order interactions; Multi-task data; Nonparametric estimators; fMRI
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.07.041

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pairwise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. •New approach to identify high order links among multiple imaging modalities•Evidence of important relationships not detected by existing analysis methods•Multimodal relationships highlight important changes in schizophrenia