Diminished neural network dynamics after moderate and severe traumatic brain injury.

Nicholas Gilbert,Frank G Hillary,Vincent D Calhoun,Einat Brenner,Rachel A Bernier,Sarah M Rajtmajer,Emily Grossner,Naoki Masuda

doi:10.1371/journal.pone.0197419

Abstract

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network “states” that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.

Highlights

One approach to understand the consequences of traumatic brain injury (TBI) on brain functioning has been the use of functional MRI methods, with more than 25 years since the first fMRI studies [1,2]
While the current data demonstrated the within subject reliability of network dynamics and the sample size here is comparable to prior graph theory analysis examining static networks after moderate and severe TBI [4,6,7,68], the sample size for this study does preclude direct examination of the reliability of these findings with respect to the groups
Replication of the current findings is needed in a separate group of individuals with moderate and severe TBI with focus on the primary findings: 1) network dynamic loss, 2) occurrence of rare states not evident in the healthy adults in this sample

Summary

Introduction

One approach to understand the consequences of traumatic brain injury (TBI) on brain functioning has been the use of functional MRI (fMRI) methods, with more than 25 years since the first fMRI studies [1,2]. Since these early seminal studies, the change in the landscape of this literature is reflected in the development of novel methods and much recent work has turned the focus toward understanding interactive nodes of large-scale neural networks. Enhanced connectivity in the SN has been demonstrated over the course of recovery from injury [8], and connectivity between the anterior insula and ACC within the SN have been linked to attentional capture and cognitive outcome in TBI [12,13]

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