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Repeated subconcussive blows to the head during sports or other contact activities may have a cumulative and long lasting effect on cognitive functioning. Unobtrusive measurement and tracking of cognitive functioning is needed to enable preventative interventions for people at elevated risk of concussive injury. The focus of the present study is to investigate the potential for using passive measurements of fine motor movements (smooth pursuit eye tracking and read speech) and resting state brain activity (measured using fMRI) to complement existing diagnostic tools, such as the Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), that are used for this purpose. Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Hypotheses were that (1) measures of complexity of fine motor coordination and of resting state brain activity are predictive of cognitive functioning measured by the ImPACT test, and (2) within-subject changes in these measures over the course of a sports season are predictive of changes in ImPACT scores. The first principal component of the six ImPACT composite scores was used as a latent factor that represents cognitive functioning. This latent factor was positively correlated with four of the ImPACT composites: verbal memory, visual memory, visual motor speed and reaction speed. Strong correlations, ranging between r = 0.26 and r = 0.49, were found between this latent factor and complexity features derived from each sensor modality. Based on a regression model, the complexity features were combined across sensor modalities and used to predict the latent factor on out-of-sample subjects. The predictions correlated with the true latent factor with r = 0.71. Within-subject changes over time were predicted with r = 0.34. These results indicate the potential to predict

There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential for oculomotor features to complement existing diagnostic tools, such as measurements of Optic Nerve Sheath Diameter (ONSD) and Immediate Post-concussion Assessment and Cognitive Testing (ImPACT). Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Given the high risk of repetitive head impacts associated with both soccer and football, our hypotheses were that (1) ONSD and ImPACT scores would worsen through the season and (2) oculomotor features would effectively capture both neurophysiological changes reflected by ONSD and neuro-functional status assessed via ImPACT. Oculomotor features were used as input to Linear Mixed-Effects Regression models to predict ONSD and ImPACT scores as outcomes. Prediction accuracy was evaluated to identify explicit relationships between eye movements, ONSD, and ImPACT scores. Significant Pearson correlations were observed between predicted and actual outcomes for ONSD (Raw = 0.70; Normalized = 0.45) and for ImPACT (Raw = 0.86; Normalized = 0.71), demonstrating the capability of oculomotor features to capture neurological changes detected by both ONSD and ImPACT. The most predictive features were found to relate to motor control and visual-motor processing. In future work, oculomotor models, linking neural structures to oculomotor function, can be built to gain extended mechanistic insights into neurophysiological changes observed through seasons of participation in contact sports., Department of the Army (Contract FA8702-15-D-0001)

Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance.

Many individuals struggle to understand speech in listening scenarios that includereverberation and background noise. An individual’s ability to understand speech arisesfrom a combination of peripheral auditory function, central auditory function, and generalcognitive abilities. The interaction of these factors complicates the prescription oftreatment or therapy to improve hearing function. Damage tothe auditory peripherycan be studied in animals; however, this method alone is not enough to understandthe impact of hearing loss on speech perception. Computational auditory models bridgethe gap between animal studies and human speech perception.Perturbations to themodeled auditory systems can permit mechanism-based investigations into observedhuman behavior. In this study, we propose a computational model that accounts forthe complex interactions between different hearing damagemechanisms and simulateshuman speech-in-noise perception. The model performs a digit classification task asa human would, with only acoustic sound pressure as input. Thus, we can use themodel’s performance as a proxy for human performance. This two-stage model consistsof a biophysical cochlear-nerve spike generator followed by a deep neural network(DNN) classifier. We hypothesize that sudden damage to the periphery affects speechperception and that central nervous system adaptation overtime may compensatefor peripheral hearing damage. Our model achieved human-like performance acrosssignal-to-noise ratios (SNRs) under normal-hearing (NH) cochlear settings, achieving50% digit recognition accuracy at−20.7 dB SNR. Results were comparable to eightNH participants on the same task who achieved 50% behavioralperformance at−22dB SNR. We also simulated medial olivocochlear reflex (MOCR)and auditory nervefiber (ANF) loss, which worsened digit-recognition accuracy at lower SNRs comparedto higher SNRs. Our simulated performance following ANF loss is consistent withthe hypothesis that cochlear synaptopathy im, United States. Department of Defense. Research and Engineering (Air Force Contract No. FA8702-15-D-0001), National Institutes of Health (U.S.) (T32 Trainee Grant No. 5T32DC000038-27), National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant DGE1745303)

Goal: We propose a speech modeling and signal-processing framework to detect and track COVID-19 through asymptomatic and symptomatic stages. Methods: The approach is based on complexity of neuromotor coordination across speech subsystems involved in respiration, phonation and articulation, motivated by the distinct nature of COVID-19 involving lower (i.e., bronchial tubes, diaphragm, lower trachea) versus upper (i.e., laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation [1], as well as by the growing evidence of the virus' neurological manifestations [2]—[5]. Preliminary results: An exploratory study with audio interviews of five subjects provides Cohen's d effect sizes between pre-COVID-19 (pre-exposure) from post-COVID-19 (after positive diagnosis but asymptomatic) using: coordination of respiration (as measured through acoustic waveform amplitude) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. Conclusions: While there is a strong subject-dependence, the group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. Validation is needed with larger more controlled datasets and to address confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings.