Introduction:The ability to perform visually-guided motor tasks requires the transformation of visual information into programmed motor outputs. When the guiding visual information does not align spatially with the motor output, the brain processes rules to integrate somatosensory information into an appropriate motor response. Performance on such rule-based, "cognitive-motor integration" (CMI) tasks has been shown to be affected by sex, age, and in several neurologic conditions. The present study sought to (1) expand on these findings by examining whether such performance differences are related to levels of sex steroid hormones, and (2) characterize the relationship between hormone levels and any structural differences in brain regions responsible for complex motor control. Methods:Thirty-six healthy individuals (18 females) underwent MRI scanning to acquire anatomical brain images. They performed two touchscreen-based eye-hand coordination tasks, including a standard direct interaction task and one which involved CMI; target location and motor action were dissociated in the CMI task. Saliva samples collected on the day of testing were used to determine estrogen, progesterone, and testosterone levels. Results:Multiple regression analyses revealed age to be a small but significant predictors of performance in a CMI condition with visual feedback reversal. We found that after accounting for this age effect, testosterone was a significant predictor of CMI performance in this group. We also observed that the relationship between testosterone levels and complex performance was related to grey matter thickness and volume in visuomotor control regions. Conclusion:These data suggest that underlying brain networks controlling simultaneous thought and action may differ as a function of sex steroid hormone concentrations, and that small performance declines emerge in the working-age years.

Many skills necessary to perform activities of daily living require individuals to think and move at the same time; otherwise known as cognitive-motor integration (CMI). An upper extremity CMI task has shown how CMI performance changes with age, neurotrauma, and sport experience; however, the majority of movements required for activities of daily living extend beyond the upper extremity. Therefore, the purpose of this pilot study was to compare a full-body balance-related CMI task with the validated upper extremity task. Twenty-nine young healthy adults [24.3 ± 5.1 years (SD); 12 females] completed 2 CMI tasks to assess upper extremity CMI and full-body CMI. In general, both CMI tasks varied in difficulty, ranging from congruent interactions with targets, to incongruent interactions which included visual feedback reversal (requiring increased CMI). Performance in both tasks were quantified using reaction time (RT), movement time (MT), and normalized path length (nPL). An interaction effect of task and condition was found for MT [F (1,28) = 9.344, p = 0.005] and nPL [F (1,28) = 12.766, p = 0.001], with larger increases across conditions in the full-body task compared to the upper extremity task. For the upper extremity task, sex predicted RT, where males had quicker RTs than females (unstandardized B = -78.968, p = 0.038). For the full-body task, MT and nPL were predicted by age and sport experience, respectively; where younger age resulted in faster MTs (unstandardized B = 235.546, p = 0.009), and more sport experience led to less variable nPLs (unstandardized B = -3.802, p = 0.005). Lastly, the full-body task found that sport experience was moderated by sex (unstandardized B = 203.650, p = 0.014), where only females saw decreases in MT with increasing sport experience. The full-body CMI task provides a more comprehensive analysis of sensory, motor, and cognitive contributions to coordination tasks. An isolated upper extremity task may be limited in its ability to extract meaningful information that could contribute to difficulties in performing activities of daily living. Future work could utilize this task in clinical populations with the potential to uncover differences that might not be apparent in standard assessment protocols.

IntroductionIn everyday life we interact with our environment in an indirect way, where there is a mapping between the viewed goal of our action and the required movement (e.g., using a computer mouse). Such tasks require cognitive- motor integration (CMI), where rules dictate the relationship between perception and action. Previous research with primarily young adult male athletes has demonstrated that the underlying movement and cognitive control networks that rely on intact frontal, parietal, and subcortical brain region connectivity may be compromised following concussion, resulting in an impaired ability to engage in complex movements. Here we investigate whether such relationships also exist in working-aged adults with persistent post-concussion symptoms (PPCS).MethodsTwenty-two individuals (17 females) performed two visuomotor tasks: one requiring direct (standard) interaction with visual targets, and one comprising a plane-change and feedback reversal (non-standard interaction) between viewed target and required hand motion (CMI). PPCS and dizziness were related to brain network function via resting state functional connectivity (RSFC) in six networks, structural integrity via cortical thickness in CMI-related brain regions, and white matter tract integrity via diffusion tensor imaging.ResultsWe observed that lower cortical thickness in the inferior and superior parietal cortices were associated with dizziness and impaired non-standard visuomotor performance, respectively. Furthermore, increased PPCS severity was associated with hyperconnectivity within the visual, sensorimotor control, frontoparietal control, and dorsal attention networks, while hyperconnectivity within the salience ventral attention network was associated with better non-standard visuomotor performance. Lastly, we found that lower white matter tract integrity in several long associative, projection, and commissural tracts were associated with poor cognitive-motor integration performance, PPCS severity, and dizziness.DiscussionThese preliminary findings characterize the impact of PPCS on structure and function underlying impaired visuomotor performance.

# Aims Previous studies have consistently shown a decline in cognitive-motor integration (CMI) performance in those either with a history of concussion, less sport experience or of older age. The present study sought to characterize CMI performance of individuals as a function of these factors combined. Hypothesis: relative to those with one concussion, those with multiple concussions would experience significantly greater neuropathological effects on the brain networks required for standard and rule-based visuomotor performance, resulting in impaired motor performance. Study design: Individual cross-sectional study. Level of evidence: Level 3. # Materials & Methods Two hundred and twenty-three asymptomatic individuals with a concussion history participated in this study. They performed two touchscreen-based eye--hand coordination tasks, including a standard direct interaction task and one which involved CMI; target location and motor action were dissociated in the CMI task. # Results A significant percentage of standard and CMI variance was explained only by age and sport experience in our sample of younger, mainly select-level athletes. # Conclusion These findings may suggest that motor developmental stage, which corresponds to age, and sport experience provide brain network resilience that can compensate for concussion-related performance declines. Clinical relevance: These data provide evidence around the importance of accounting for sport experience and developmental age when evaluating return to play metrics in youth and young adults.

In this work, we introduce a novel approach using cognitive-motor integration (CMI) metrics and concussion history to predict NHL participation. Specifically, we leverage data from the BrDi Test—a validated neuroscience-based assessment involving visuomotor tracing tasks under both standard and non-standard conditions. Using data from the BrDi Test—a neuroscience-based cognitive-motor integration task this research explores the link between mild traumatic brain injury (mTBI), or concussion, and NHL participation. We emphasize both model accuracy and interpretability to support human scouting decisions by comparing explainable machine learning (XAI) models with traditional black-box approaches. We frame the problem as a binary classification task and evaluate a range of machine learning models. Despite the limitations of a small, class-imbalanced dataset, a class-weighted Decision Tree achieved the best average generalization performance, offering strong interpretability alongside competitive performance. Our contributions include the first application of BrDi data to NHL prediction, a comparative analysis of XAI and black-box models for talent evaluation, and a modular ML pipeline adaptable to small-sample, high-dimensional datasets. The results support the potential value of cognitive metrics in augmenting traditional scouting decisions and provide a foundation for future research as more data becomes available.

BACKGROUND/AIMS:Concussion has been a topic of concern in Ontario, Canada, and elsewhere, and new research and guidelines are emerging. The association between material deprivation and emergency department (ED) visits for concussions is not well established, and many studies have focused on organised sports which may not be equally accessible. The objective of this study was to examine the association between material deprivation, age, sex and ED visits for concussions in children and youth (0-19). METHODS:This study used administrative data from ICES in Ontario, Canada. All ED visits for children and adolescents with International Classification of Disease version 10 S060 are included. The denominator was the number of children residing in Ontario. Incidence rate per 100 000 and 95% CIs were calculated. RESULTS:The ED visit rate per 100 000 children and adolescents varied by year, material deprivation, age and sex. Rates among children with the greatest material deprivation (quintile 5) were 36.7 in 2010 and 43.3 in 2020, while the corresponding rates in the lowest quintile were 62.6 and 61.8. The ED visit rate was increasing prior to the pandemic in 2020. CONCLUSIONS:Children in a lower material deprivation quintile consistently visited EDs for concussion more frequently than children from higher quintiles. Children in less deprived areas may be more able to participate in organised sports and more aware of concussion policies such as Rowan's Law which requires medical care following a suspected sport-related concussion. Resources related to awareness and identification of concussions should be considered for all children and youth.

Cognitive-motor integration (CMI) tasks require simultaneous mental and physical effort, engaging multiple brain regions and networks. These tasks are widely used to study the effects of brain injuries, cognitive impairments, and neurological challenges. With the rapid increase in the use of sensors for brain signal analysis and the proliferation of IoT technologies, there is a growing need for a synergistic framework that integrates machine learning (ML) with IoT for cognitive health studies. However, research exploring ML-based cognitive or mental health assessments in conjunction with visuomotor tasks within an IoT pipeline has been limited. This study addresses that gap by proposing a novel IoT architecture and deep learning classifier for real-time electroencephalography (EEG) data collection for cognitive load assessment. The novelty of our work lies in original real time collection of EEG data using wearable device during CMI tasks under two conditions: low-cognitive and high-cognitive load scenarios. We propose a binary classification for detecting these two cognitive states by transforming the EEG time series data into multi-channel scalograms which are analyzed with attention-based deep learning models, achieving 96.1% intra-subject classification accuracy and 86.3% inter-subject accuracy with transfer learning. The visual representation of EEG time series as scalogram images highlights and captures the regional trends associated with different cognitive states over time which might be less discernible in raw time series data. These images aids in identifying specific signatures from the textured patterns extracted by the deep learning (DL) models in improving cognitive load classification. Our framework integrates a scalable sensor-gateway-cloud infrastructure with an automated, microservices-based machine learning pipeline, enabling real-time cognitive load monitoring. This methodology advances wearable EEG-based classification, providing a cost-effective and user-friendly solution with applications in education, workplace performance, healthcare, and sports. Unlike datasets typically sourced from external repositories, this experimentally captured data offers valuable insights into cognitive load during motor tasks. The recorded EEG signals reflect neural activity associated with cognitive changes, providing a critical resource for analyzing mental workload. Our methodology can help an individual identify the optimal level of mental workload and hence enhance one’s learning performance. By bridging IoT data management with AI-driven cognitive health analysis, this study sets the stage for optimizing performance in diverse environments.

INTRODUCTION:There is an established interplay between gait and attention allocation. Attention during walking is important to reduce instability, process environmental stimuli, and perform simultaneous tasks. It is of critical importance to consider how attention modulation during dual-tasking influences prefrontal cortex (PFC) activity and gait characteristics. However, paradigms probing this relationship are often limited in realism and must balance mobility challenges with practicality. This protocol introduces a novel methodology combining functional near-infrared spectroscopy (fNIRS) and augmented reality (AR) in a complex gait dual-task to validate the use of virtual obstacles to probe for a cortical indicator of altered attention during distracted walking. MATERIALS AND METHODS:This methodological development study investigated 11 healthy adults (mean age 50.9 ± 5.8 years, 5 female) in an obstacle navigation cognitive-motor dual-task combining fNIRS and AR during navigation of realistic AR-projected 3D virtual obstacles and physical obstacles. The distraction task involved a 5-word recall from a mimicked phone call. Participants performed six experimental tasks: walking; walking + distraction; walking + obstacles (both physical and virtual); and walking + obstacles (both physical and virtual) + distraction. RESULTS:Intraclass correlations ranged from 0.563 to 0.886 for oxyhemoglobin (O2Hb) ratios and gait velocity between virtual and physical obstacles, demonstrating moderate-to-good consistency between methods. Proportional bias in the Bland-Altman plots was observed for O2Hb. Participants also demonstrated task-dependent modulation of gait and PFC activity in response to dual task conditions in both tasks. CONCLUSIONS:This combination of technologies elicited task-dependent modulation in PFC activity and gait behaviours in healthy adults, confirming the efficacy of AR-projected obstacles in a cognitive-motor dual-task paradigm. Based on these outcomes, it is likely that this experimental approach will be useful in probing cortical activity changes associated with dual-tasking to inform the relationship between mobility and cognition and characterize behavioural and neural markers of functional mobility.

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis. Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model. These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

# Background We investigated whether everyday situations that trigger post-concussion symptoms (i.e., dynamic visual scenes), induce vection (illusory self-motion) and/or affect postural stability. # Materials & Methods Concussed and control participants were moved through a virtual grocery store, and rated their vection intensity. Postural sway during visual motion was measured. Baseline tests assessed concussion symptoms and sensory functioning, including visual dependence. # Results Vection ratings were higher in concussed individuals than controls, and were predicted by faster visual speeds. Vection and visual speed also predicted postural sway in the concussion group. Visual dependence was positively associated with vection intensity and all postural measures. # Conclusion These findings provide valuable insights for the development of future symptom-screening tools and rehabilitation strategies.

ObjectiveTo examine the association between socio-economic status and Emergency Department (ED) visits for concussions in children and youth in Ontario, Canada.DesignLongitudinal population-based study using administrative data from all ED visits.SettingAll ED visits in Ontario, Canada.ParticipantsChildren and youth residing in Ontario.Interventions (or Assessment of Risk Factors)The rate per 100000 children was calculated from 2008 to 2015. Socio-economic status was defined by a marginalization index and grouped into quintiles from the highest to the lowest. Comparisons were made over the 7-year period and by quintile.Outcome MeasuresICD-10 diagnosis of concussion.Main ResultsThere were 5,889 concussions reported at an emergency department in 2008, and 14,906 in 2015. The rate among the lowest socioeconomic class quintile was 5.23 per 100000 person years in 2008, and 7.12 for the highest socioeconomic class quintile, and 8.64 and 11.07 respectively in 2015.ConclusionsRates of ED visits for concussions increased among children over time. However, children in higher income quintiles consistently visited EDs for concussion more than children from lower quintiles. This may be due to the increased opportunity wealthier children have to engage in contact sports such as hockey and football or may reflect differences in the likelihood of seeking care. Policies related to awareness and identification of concussions need to be considered for all children and may need to be improved for those in poorer areas.

Post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID, and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are debilitating post-viral conditions with many symptomatic overlaps, including exercise intolerance and autonomic dysfunction. Both conditions are growing in prevalence, and effective safe treatment strategies must be investigated. We hypothesized that inspiratory muscle training (IMT) could be used in PASC and mild to moderate ME/CFS to mitigate symptoms, improve exercise capacity, and improve autonomic function. We recruited healthy controls (n=12; 10 women), people with PASC (n=9; 8 women), and people with mild to moderate ME/CFS (n=12; 10 women) to complete 8 weeks of IMT. This project was registered as a clinical trial (NCT05196529) with clinicaltrials.gov. After completion of IMT, all groups experienced improvements in inspiratory muscle pressure (p<0.001), 6-minute walk distance (p=0.002), resting heart rate (p=0.037), heart rate variability (p<0.05), and symptoms related to sleep (p=0.009). In the ME/CFS group only, after completion of IMT, there were additional improvements with regard to vascular function (p=0.001), secretomotor function (p=0.023), the total weighted score (p=0.005) of the COMPASS 31 autonomic questionnaire, and symptoms related to pain (p=0.016). We found that after 8 weeks of IMT, people with PASC and/or ME/CFS could see some overall improvements in their autonomic function and symptomology.

Objective To determine if concussion history affects sex-related differences in athletes' abilities to perform visual reaction time, oculomotor, and cognitive tasks. Design Prospective design. Setting Canadian University. Participants Pre-season baseline data was collected from 133 varsity athletes (79 no concussion history: 36 females, 43 males; 54 history of concussion: 28 females, 26 males). Exclusions included previous visual epileptic seizures, strabismus, and colour blindness. Interventions (or Assessment of Risk Factors) Athletes used a VR-goggle/eye-tracking system (Saccade Analytics, Inc.) to look at targets and perform a Stroop task while seated. Outcome Measures Oculomotor accuracy (saccades), oculomotor reaction time (anti-saccades), and cognitive performance (Stroop total errors). Main Results We observed a significant difference in our cognitive task outcome measure between males and females with a history of concussion whereupon females performed (p=0.015). Further, females with a history of concussion (p=0.037) which may relate to greater concussion exposure for higher performing athletes. There was no significant difference between the sexes, with and without concussion history for the visual reaction and oculomotor tasks. Conclusions This study demonstrates that asymptomatic athletes with and without a previous history of concussion do not show a significant performance difference on visual reaction and oculomotor tasks. Cognitive performance, however, shows sex-related differences which may be due to the nature of the task (males tended to execute the test faster, making more errors). This research provides an example of objective visuomotor and cognitive testing that can be used to further explore sex-related differences in concussion injury effects.

Objective Develop an on-field cognitive-motor integration task for assessing post-concussion return readiness. Hypothesis: athletes without a history (Hx) of concussion perform faster and incur fewer errors in the different task conditions versus athletes with a Hx. Design Prospective study. Setting Canadian university. Participants 180 asymptomatic university athletes from 14 teams; 77 athletes (48 females, 29 males) with a prior Hx of concussions & 103 athletes (51 females, 52 males) without. Interventions (or Assessment of Risk Factors) Athletes ran 26m while responding to four visual directional cues from an examiner (directing athletes to jump, drop, cut right, cut left) under two different conditions. Athletes were randomly assigned to complete either a simple condition 1: athlete response direction matched the visual cue direction, or a more cognitively demanding condition 2: response direction was opposite to the visual cue. Outcome Measures Dependent variables: number of incorrect responses and total time for completion of each condition. A visuomotor learning factor was calculated based on the order that the tasks were assigned and a cognitive load factor was calculated based on the task condition. Main Results Learning effect (p=0.03): Athletes with a Hx had reduced time savings benefit from having done the basic task already. Sex/Concussion-History interaction effect (p=0.003): Males with a Hx performed on average 669ms slower on overall task performance while females with a Hx were on average 303ms faster, suggesting a better visuomotor motor skill recovery in females. Conclusions The HurtSHynes test is a useful, fast assessment of an athlete's multi-domain skill performance to assist in guiding RTS decisions.