Children and youths are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is an increase in functional connectivity, yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition. Data from 32 healthy adolescents (mean age =16 years) and 21 concussed adolescents (mean age = 15 years) within 1 week of injury were included in the study. Five minutes of resting state data EEG were collected while participants sat quietly with their eyes closed. We applied the information flow rate to measure the transfer of information between the EEG time series of each individual at different source locations, and therefore between different brain regions. Based on the ensemble means of the magnitude of normalized information flow rate, our analysis shows that the dominant nexus of information flow in healthy adolescents is primarily left lateralized and anterior-centric, characterized by strong bidirectional information exchange between the frontal regions, and between the frontal and the central/temporal regions. In contrast, adolescents with concussion show distinct differences in information flow marked by a more left-right symmetrical, albeit still primarily anterior-centric, pattern of connections, diminished activity along the central-parietal midline axis, and the emergence of inter-hemispheric connections between the left and right frontal and the left and right temporal regions of the brain. We also find that the statistical distribution of the normalized information flow rates in each group (control and concussed) is significantly different. This paper is the first to describe the characteristics of the source space information flow and the effective connectivity patterns between brain regions in healthy adolescents in juxtaposition with the altered spatial pattern of information flow in adolescents with concussion, statistically Hristopulos et al. Disrupted Information Flow in Concussed Adolescentsquantifying the differences in the distribution of the information flow rate between the two populations. We hypothesize that the observed changes in information flow in the concussed group indicate functional reorganization of resting state networks in response to brain injury.

Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

Resting-state directed brain1 connectivity patterns in adolescents 2 from source-reconstructed EEG 3 signals based on information flow 4 rate 5Abstract Quantifying the brain's effective connectivity offers a unique window onto the causal 15 architecture coupling the different regions of the brain. Here, we advocate a new, data-driven 16 measure of directed (or effective) brain connectivity based on the recently developed information 17 flow rate coefficient. The concept of the information flow rate is founded in the theory of stochastic 18 dynamical systems and its derivation is based on first principles; unlike various commonly used 19 linear and nonlinear correlations and empirical directional coefficients, the information flow rate 20 can measure causal relations between time series with minimal assumptions. We apply the 21 information flow rate to electroencephalography (EEG) signals in adolescent males to map out the 22 directed, causal, spatial interactions between brain regions during resting-state conditions. To our 23 knowledge, this is the first study of effective connectivity in the adolescent brain. Our analysis 24 reveals that adolescents show a pattern of information flow that is strongly left lateralized, and 25 consists of short and medium ranged bidirectional interactions across the frontal-central-temporal 26 regions. These results suggest an intermediate state of brain maturation in adolescence. The brain is a complex entity comprising widely distributed but highly interconnected regions, the 30 dynamic interplay of which is essential for brain function. Establishing how activity is coordinated 31 across these regions to give rise to organized (higher order) brain functions ranks as one of the key 32 challenges in neuroscience. Various measures of brain connectivity are in use for this purpose as 33 discussed in (Friston34 Cohen, 2014) and references therein. Structural measures are based on confirmed anatomical 35 connections between brain regions. Functional measures involve dynamically changing, linear 36 or nonlinear, non-directional coefficients of statistical dependence (e.g., correlation, covariance, 37 phase-locking values, coherence) that may appear between structurally unconnected regions. Effec-38 tive brain connectivity measures capture directionally dependent interactions between different 39 brain regions and aim to identify causal mechanisms in neural processing. In the following, we 40 use the terms "effective" and "'directed" connectivity interchangeably. We refer readers to Sakkalis 41 (2011) and Bastos and Schoffelen (2015) for recent reviews of functional and effective connectiv-42 ity measures in the brain. Herein, we investigate effective connectivity patterns as revealed by 43 electroencephalography (EEG) recordings (Van de Ville et al., 2010) of scalp electromagnetic fields 44 following source-space reconstruction. 45 The multichannel EEG signals, which are thought to reflect activity in the underlying brain 46 regions, offer a convenient window into the temporal dyn...

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