Dynamic Functional Network Analysis in Mild Traumatic Brain Injury

Abstract

Mild traumatic brain injury (mTBI) is one of the most common neurological disorders for which a subset of patients develops persistent postconcussive symptoms. Previous studies discovered abnormalities and disruptions in the brain functional networks of mTBI patients principally using static functional connectivity measures which assume that neural communication across the brain is static during resting state conditions. In this study, we examine the differences in dynamic neural communication between mTBI and control participants through the application of a combination of dynamic functional analysis and graph theoretic algorithms. Resting state functional magnetic resonance imaging data was obtained on 47 mTBI patients at the acute stage of injury and 30 demographically matched healthy control participants. Results show unique alterations in both the static and dynamic functional connectivity at the acute stage in mTBI patients who suffer persistent symptoms (≥6 months after injury). In addition, mTBI patients with postconcussion syndrome demonstrated a unique allocation of time in various brain states compared to both control participants and mTBI patients with favorable outcomes. These findings suggest that global damage to the overall communication across the brain in the acute stage may contribute to chronic mTBI symptoms. Dynamic functional analysis is a powerful tool that provides insights into the brain states and the innovative analysis methodology utilized may hold the potential to delineate patients predisposed to poor outcomes upon early presentation following injury.

Keywords: dynamic functional connectivity; graph theory; mild traumatic brain injury; postconcussive syndrome.

FIG. 1.

Diagram outlining the analysis pipeline. canICA, canonical independent component analysis.

FIG. 2.

Visual representation of the component derived by canICA divided into seven functional sub-networks. They are labeled according to their anatomical positions: visual, auditory, cognitive control, sensorimotor, sub-cortical, cerebellum, and default mode network.

FIG. 3.

BIC Scores for K-means Clustering. BIC scores with various numbers of clusters, k. The k with the lowest BIC is most preferable. BIC, Bayesian Information Criterion.

FIG. 4.

Average static measurements of global network properties for the HC (n = 30), PCS+ (n = 24), and PCS− (n = 23) sub-groups. (A) Static average clustering coefficient (CC), (B) Static average shortest path (SP), (C) Static average weight of the minimum spanning trees (MST). *p < 0.05 based on post-hoc t-tests. HC, healthy controls; PCS, postconcussion syndrome; PCS+, patients with PCS; PCS−, patients without PCS; SE, standard error.

FIG. 5.

Average dynamic measurements of global network properties for the HC (n = 30), PCS+ (n = 24), and PCS− (n = 23) sub-groups. (A) Static average clustering coefficient (CC), (B) Static average shortest path (SP), (C) Static average weight of the minimum spanning trees (MST). *p < 0.05 based on post-hoc t-tests. SE, standard error.

FIG. 6.

Results of the state analysis. (A) Percent of time that the HC (n = 30), mTBI PCS+ (n = 24), and mTBI PCS− (n = 23) spend in each of the five states. Characteristics of each state are shown in Table 4. (B) Average connectivity matrices for each of the five states. A, Auditory network; C, cerebellar network; CC, cognitive control network, D, default mode network; mTBI, mild traumatic brain injury, SC, sub-cortical network, SM, sensory motor network, V, visual network.