Longitudinal Changes in Resting State Connectivity and White Matter Integrity in Adolescents With Sports-Related Concussion

Murdaugh, Donna L.; King, Tricia Z.; Sun, Binjian; Jones, Richard A.; Ono, Kim E; Reisner, Andrew; Burns, Thomas G.

doi:10.1017/s1355617718000413

Cited by 45 publications

(49 citation statements)

References 54 publications

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“…We propose that widespread injury can reduce rule-based skilled performance through a failure to communicate between brain networks. Cognitive brain network alterations have been observed following concussion (13)(14)(15)(16)(17). To assess the communication between brain areas for movement control, our group developed a touch screen based functional assessment tool using a visuomotor task that incorporates two forms of non-standard mapping: explicit rule integration (strategic control), and implicit spatial vision-to-hand-motion realignment (sensorimotor recalibration).…”

Section: A Failure To Communicate: the Effect Of Mild Brain Injury Onmentioning

confidence: 99%

The Effects of Mild Traumatic Brain Injury on Cognitive-Motor Integration for Skilled Performance

et al. 2020

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Adults exposed to blast and blunt impact often experience mild traumatic brain injury, affecting neural functions related to sensory, cognitive, and motor function. In this perspective article, we will review the effects of impact and blast exposure on functional performance that requires the integration of these sensory, cognitive, and motor control systems. We describe cognitive-motor integration and how it relates to successfully navigating skilled activities crucial for work, duty, sport, and even daily life. We review our research on the behavioral effects of traumatic impact and blast exposure on cognitive-motor integration in both younger and older adults, and the neural networks that are involved in these types of skills. Overall, we have observed impairments in rule-based skilled performance as a function of both physical impact and blast exposure. The extent of these impairments depended on the age at injury and the sex of the individual. It appears, however, that cognitive-motor integration deficits can be mitigated by the level of skill expertise of the affected individual, suggesting that such experience imparts resiliency in the brain networks that underly the control of complex visuomotor performance. Finally, we discuss the next steps needed to comprehensively understand the impact of trauma and blast exposure on functional movement control.

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Section: A Failure To Communicate: the Effect Of Mild Brain Injury Onmentioning

confidence: 99%

The Effects of Mild Traumatic Brain Injury on Cognitive-Motor Integration for Skilled Performance

et al. 2020

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“…Several resting state fMRI studies of concussed athletes showed reduced functional connectivity in several neural networks in the acute and chronic periods following mTBI [93,96]. Murdaugh et al interestingly found resting state fMRI hyper connectivity in posterior brain regions (left cerebellum and precuneus, and right middle occipital gyrus, p = 0.047, <0.0001, 0.001, respectively) and hypoconnectivity in anterior brain regions (right inferior frontal gyrus (IFG), middle frontal gyrus (MFG), middle temporal gyrus (MTG), and posterior cingulate, p = 0.005, 0.05, 0.013, 0.01, respectively) in high school football players with SRC compared to those without [97]. More recent reviews of resting state fMRI in SRC have shown that changes in functional connectivity have the potential to predict SRC recovery profiles and clinical outcomes.…”

Section: Future Directionsmentioning

confidence: 99%

Sport-Related Concussion: Evaluation, Treatment, and Future Directions

et al. 2019

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Sport-related concussion (SRC) is a highly prevalent injury predominantly affecting millions of youth through high school athletes every year. In recent years, SRC has received a significant amount of attention due to potential for long-term neurologic sequelae. However, the acute symptoms and possibility of prolonged recovery account for the vast majority of morbidity from SRC. Modifying factors have been identified and may allow for improved prediction of a protracted course. Potential novel modifying factors may include genetic determinants of recovery, as well as radiographic biomarkers, which represent burgeoning subfields in SRC research. Helmet design and understanding the biomechanical stressors on the brain that lead to concussion also represent active areas of research. This narrative review provides a general synopsis of SRC, including relevant definitions, current treatment paradigms, and modifying factors for recovery, in addition to novel areas of research and future directions for SRC research.

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“…Numerous groups have sought to leverage neuroimaging data to not only detect mTBIs reliably and objectively, but also track brain recovery. At the group-level, structural changes in the integrity of white matter in adolescents has been observed using diffusion tensor imaging (DTI) [20,[27][28][29][30][31][32]. Similarly, studies of youths during "resting state" reveal significant mTBI-induced alterations in the functional organization of the brain with respect to their healthy counterparts, with key features being (i) a shift in spectral profile toward higher frequencies in the frontal brain regions, [33] (ii) an increase in functional connectivity (hyperconnectivity) [17,34,35], and (iii) disrupted information flow patterns [24], again particularly in the frontal regions.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data

Thanjavur

Babul

Foran

et al. 2020

Preprint

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ABSTRACTConcussion 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 recovering individuals are more prone to suffer additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, concussion management faces two significant challenges: there are no objective, clinically accepted, brain-based approaches for determining (i) whether an athlete has suffered a concussion, and (ii) when the athlete has recovered. Diagnosis is based on clinical testing and self-reporting of symptoms and their severity. Self-reporting is highly subjective and symptoms only indirectly reflect the underlying brain injury. Here, we introduce a deep learning Long Short Term Memory (LSTM)-based recurrent neural network that is able to distinguish between healthy and acute post-concussed adolescent athletes using only a short (i.e. 90 seconds long) sample of resting state EEG data as input. The athletes were neither required to perform a specific task nor subjected to a stimulus during data collection, and the acquired EEG data was neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and tested on data from 27 male, adolescent athletes with sports related concussion, bench marked against 35 healthy, adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of >90% and its ensemble-median Area Under the Curve (AUC) corresponds to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state EEG data. It represents a key step towards the development of an easy-to-use, brain-based, automatic classification of concussion at an individual level.

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Longitudinal Changes in Resting State Connectivity and White Matter Integrity in Adolescents With Sports-Related Concussion

Cited by 45 publications

References 54 publications

The Effects of Mild Traumatic Brain Injury on Cognitive-Motor Integration for Skilled Performance

The Effects of Mild Traumatic Brain Injury on Cognitive-Motor Integration for Skilled Performance

Sport-Related Concussion: Evaluation, Treatment, and Future Directions

Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data

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