Personalized connectome-based modeling in patients with semi-acute phase TBI: relationship to acute neuroimaging and 6-month follow-up

Abstract

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the micro-scale a neurometabolic cascade alters neurotransmission, while on the macro-scale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of forty-four adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks post-injury, and with follow-up behavioral outcome assessed six months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semi-acute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6-month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.

Significance Statement

The variability of clinical outcomes following mild to moderate traumatic brain injury (TBI) is underscored by complex pathophysiological mechanisms that take effect across spatial scales. We used the neuroinformatics platform, The Virtual Brain, to model individualized brain activity and make inferences across these spatial scales. Specifically, this approach allowed us to link macroscopic brain dynamics with mesoscopic biophysical parameters, distinguishing semi-acute mild to moderate TBI patients from comparison participants and predicting the long-term recovery of these patients. Our results demonstrate the sensitivity of our large-scale brain model to pathophysiological changes following TBI and illustrates how computational modeling may be used to advance understanding of chronic TBI outcome.