On the time-course of functional connectivity: theory of a dynamic progression of concussion effects.

The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.

Neurophysiological markers of cognitive deficits and recovery in concussed adolescents.

OBJECTIVE: The present study sought to determine: 1) whether concussed adolescents exhibited deficits in neurocognitive functioning as reflected by neurophysiological alterations; 2) if neurophysiological alterations could be linked to supplementary data such as the number of previous concussions and days since injury; and 3) if deficits in psychological health and behavioural tests increased during diagnosis duration. METHODS: Twenty-six concussed adolescents were compared to twenty-eight healthy controls with no prior concussions. Self-report inventories evaluated depressive and concussive symptomatology, while behavioral tests evaluated cognitive ability qualitatively. To assess neurophysiological markers of cognitive function, two separate auditory oddball tasks were employed: 1) an active oddball task measuring executive control and attention as reflected by the N2b and P300, respectively; and 2) a passive oddball task assessing the early, automatic pre-conscious awareness processes as reflected by the MMN. RESULTS: Concussed adolescents displayed delayed N2b and attenuated P300 responses relative to controls; showed elevated levels of depressive and concussive symptomatology; scored average-to- low-average in behavioral tests; and exhibited N2b response latencies that correlated with number of days since injury. CONCLUSION: These findings demonstrate that concussed adolescents exhibit clear deficiencies in neurocognitive function, and that N2b response latency may be a marker of concussion recovery.

Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment.

Concussion has been shown to leave the afflicted with significant cognitive and neurobehavioural deficits. The persistence of these deficits and their link to neurophysiological indices of cognition, as measured by event-related potentials (ERP) using electroencephalography (EEG), remains restricted to population level analyses that limit their utility in the clinical setting. In the present paper, a convolutional neural network is extended to capitalize on characteristics specific to EEG/ERP data in order to assess for post-concussive effects. An aggregated measure of single-trial performance was able to classify accurately (85%) between 26 acutely to post-acutely concussed participants and 28 healthy controls in a stratified 10-fold cross-validation design. Additionally, the model was evaluated in a longitudinal subsample of the concussed group to indicate a dissociation between the progression of EEG/ERP and that of self-reported inventories. Concordant with a number of previous studies, symptomatology was found to be uncorrelated to EEG/ERP results as assessed with the proposed models. Our results form a first-step towards the clinical integration of neurophysiological results in concussion management and motivate a multi-site validation study for a concussion assessment tool in acute and post-acute cases.

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes.

There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are persistent neurophysiological markers of past concussions. However, as such evidence is limited to group-level analyzes, the extent to which they enable concussion detection at the individual-level is unclear. One promising avenue of research is the use of machine learning to create quantitative predictive models that can detect prior concussions in individuals. In this paper, we translate the recent group-level findings from ERP studies of concussed individuals into a machine learning framework for performing single-subject prediction of past concussion. We found that a combination of statistics of single-subject ERPs and wavelet features yielded a classification accuracy of 81% with a sensitivity of 82% and a specificity of 80%, improving on current practice. Notably, the model was able to detect concussion effects in individuals who sustained their last injury as much as 30 years earlier. However, failure to detect past concussions in a subset of individuals suggests that the clear effects found in group-level analyses may not provide us with a full picture of the neurophysiological effects of concussion.

Disruption of function: Neurophysiological markers of cognitive deficits in retired football players.

OBJECTIVE: Recent studies demonstrate that sports-related concussions can have negative consequences on long-term brain health. The goal of the present study was to determine whether retired Canadian Football League (CFL) athletes with a history of concussions exhibit alterations in neurocognitive functioning, along with changes in physical, social, and psychological health. METHODS: Our study compared nineteen retired CFL athletes' concussion histories to eighteen healthy age-matched controls with no history of concussion. Self-report inventories were used to assess depression, memory, attention, and general health. Neurophysiological markers of cognitive function were evaluated with event-related brain potentials (ERPs) as measured in two protocols: (1) A Mismatch Negativity (MMN) protocol for assessing the automatic early attentional brain mechanism; and, (2) a P300 auditory oddball task for assessing consciously controlled attention. RESULTS: Relative to controls, CFL players exhibited: response delays and reduced amplitudes in neurophysiological responses; overall decreases in cognitive function; and poorer scores on self-reports of physical, social, and psychological health; reflecting problems in all three categories. CONCLUSION: Our findings demonstrate that multiple concussions sustained over several years can lead to altered cognitive and psychosocial function. SIGNIFICANCE: Neurophysiological markers of conscious and pre-conscious attention provide an objective assessment for evaluating long-term cognitive consequences of concussion.