Functional outcome is tied to dynamic brain states after mild to moderate traumatic brain injury

Abstract

The current study set out to investigate the dynamic functional connectome in relation to long-term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting-state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 healthy subjects. Patients underwent a neuropsychological assessment at 3 months postinjury. Outcome was measured using the Glasgow Outcome Scale Extended (GOS-E) at 6 months postinjury. The 57 patients who completed the GOS-E were classified as recovered completely (GOS-E = 8; n = 37) or incompletely (GOS-E < 8; n = 20). Neuropsychological test scores were similar for all groups. Patients with incomplete recovery spent less time in a segregated brain state compared to recovered patients during the second visit. Also, these patients moved less frequently from one meta-state to another as compared to healthy controls and recovered patients. Furthermore, incomplete recovery was associated with disruptions in cyclic state transition patterns, called attractors, during both visits. This study demonstrates that poor long-term functional recovery is associated with alterations in dynamics between brain networks, which becomes more marked as a function of time. These results could be related to psychological processes rather than injury-effects, which is an interesting area for further work.Another natural progression of the current study is to examine whether these dynamic measures can be used to monitor treatment effects. K E Y W O R D S brain dynamics, fMRI, functional connectivity, mild traumatic brain injury, networks, outcome, recovery

The chronnectome as a model for Charcot’s ‘dynamic lesion’ in functional movement disorders

“…However, human minds are not static but rather constantly switch between thoughts and cognitive states, and naturally, the connectivity patterns between brain regions fluctuate accordingly ( Hutchison et al, 2013 , Allen et al, 2014 , Calhoun et al, 2014 ). These fluctuations in functional connectivity are referred to as dynamic functional connectivity (dFC), and the entire network of dynamic connections throughout the human brain as the human ‘chronnectome’ ( Chang and Glover, 2010 , Calhoun et al, 2014 , van der Horn et al, 2019 ).…”

“…Put simply, these patterns can be thought of as prototypical functional connectivity patterns that subjects tend to return to across time ( Calhoun et al, 2014 ). Similar to other neurologic diseases, such as traumatic brain injury and stroke, it is plausible that changes in the dynamics of brain states are related to the etiology of FMD ( Bonkhoff et al, 2019 , van der Horn et al, 2019 ). Furthermore, it has been shown that in some cases different brain states transition between one another via cyclic patterns of connectivity increases and decreases ( van der Horn et al, 2019 ).…”

“…Similar to other neurologic diseases, such as traumatic brain injury and stroke, it is plausible that changes in the dynamics of brain states are related to the etiology of FMD ( Bonkhoff et al, 2019 , van der Horn et al, 2019 ). Furthermore, it has been shown that in some cases different brain states transition between one another via cyclic patterns of connectivity increases and decreases ( van der Horn et al, 2019 ). These cyclic patterns of brain transitions are called attractors , which can be disrupted due to brain disorders, such as traumatic brain injury ( van der Horn et al, 2019 , Vergara et al, 2019b ).…”

“…Furthermore, it has been shown that in some cases different brain states transition between one another via cyclic patterns of connectivity increases and decreases ( van der Horn et al, 2019 ). These cyclic patterns of brain transitions are called attractors , which can be disrupted due to brain disorders, such as traumatic brain injury ( van der Horn et al, 2019 , Vergara et al, 2019b ). The primary aim of our exploratory resting-state fMRI study is to investigate whether the properties and behavior of dynamic brain states differ between FMD patients and healthy controls (HC).…”

Functional motor disorders: mechanism, prognosis and treatment

“…After PCA, group ICA was performed using the FastICA algorithm. The statistical reliability of independent components was assessed using the ICASSO method that validates the independent components via clustering the results of multiple ICA runs(Himberg, Hyvarinen, & Esposito, 2004); using this method, the component estimation was repeated 20 times. After group ICA, the Group information guided ICA (GIG-ICA) algorithm (Du et al, 2016; Du & Fan, 2013) in GIFT was performed to generate produce subject-specific images, enabling a comparison of both the time course and the spatial maps to evaluate between-group differences.…”