Diminished neural network dynamics after moderate and severe traumatic brain injury

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.

Dynamic Functional Network Analysis in Mild Traumatic Brain Injury

“…The fact that PCS+ patients have two major states, state 2 and state 4, may contribute to the fact that PCS+ patients demonstrate reduced functional dynamics. Our findings are further supported by recent work demonstrating that network state occupancy rates for semi-acute mTBI patients differ from controls (Vergara et al, 2017b) as well as fewer state transitions in a chronic severe TBI population compared to controls (Gilbert et al, 2018). However, the authors provide evidence that the results were dependent on the specific preprocessing pipelines used suggesting a potential limitation to the findings outlined in this analysis.…”

“…While the majority of groups that investigated functional connectivity following mTBI have used static functional connectivity analysis, recently, groups have started to consider how dynamic changes in functional connectivity may be altered following mTBI (Mayer et al, 2015;Vergara et al, 2017a;Vergara et al, 2017b;Vergara et al, 2018) as well as moderate and severe TBI (Gilbert et al, 2018). In spite of differences in methodology, these groups have presented variable levels of success in using dynamic functional connectivity measures, either globally or within individual sub-networks, to both characterize and discriminate TBI populations from control groups.…”

Identification of Imaging and Clinical Markers Predicting Chronic Sleep Disturbances After Traumatic Brain Injury in Adults

“…These secondary neural injuries are associated with biomechanical, structural and functional changes in the damaged brain regions and corresponding homologous regions structurally or functionally connected with them [37,38]. Subsequently, they cause the inefficiency of neural networks and imbalance of neurotransmitter systems regulating affective and cognitive behaviors [39,40], that seem to contribute in the occurrence of sleep disorders [41,42].…”

Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia

“…• Functional integration is inherently multi-scale; that is, it coordinates neurons, micro-columns, and macroscopic regions (which form functional networks) and also organizes large-scale network interactions. Ongoing network interactions reflect higher-order functional organization in the brain, which is related to one's character and cognitive performance (Vidaurre et al, 2019) and is modulated in various disorders (Du et al, 2018;Díezcirarda et al, 2018;Gilbert et al, 2018;Rashid et al, 2018;de Lacy and Calhoun, 2019;Espinoza et al, 2019;Fu et al, 2019). So far, a good number of IPS studies have relied on (anatomical) atlases (Ponce-Alvarez et al, 2015;Cabral et al, 2017;Lord et al, 2019) or have investigated few functional networks (Chang and Glover, 2010); some have not been concerned with the recurrence of IPS modes (Pedersen et al, 2018;Zhang et al, 2019) or their recurrence characterizations have not been purely phase-based 2 (Yaesoubi et al, 2015(Yaesoubi et al, , 2017.…”