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

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

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.

Therapeutic Use and Chronic Abuse of CNS Stimulants and Anabolic Drugs

The available evidence suggests that affective disorders, such as depression and anxiety, increase risk for accelerated cognitive decline and late-life dementia in aging individuals. Behavioral neuropsychology studies also showed that cognitive decline is a central feature of aging impacting the quality of life. Motor deficits are common after traumatic brain injuries and stroke, affect subjective well-being, and are linked with reduced quality of life. Currently, restorative therapies that target the brain directly to restore cognitive and motor tasks in aging and disease are available. However, the very same drugs used for therapeutic purposes are employed by athletes as stimulants either to increase performance for fame and financial rewards or as recreational drugs. Unfortunately, most of these drugs have severe side effects and pose a serious threat to the health of athletes. The use of performance-enhancing drugs by children and teenagers has increased tremendously due to the decrease in the age of players in competitive sports and the availability of various stimulants in many forms and shapes. Thus, doping may cause serious health-threatening conditions including, infertility, subdural hematomas, liver and kidney dysfunction, peripheral edema, cardiac hypertrophy, myocardial ischemia, thrombosis, and cardiovascular disease. In this review, we focus on the impact of doping on psychopathological disorders, cognition, and depression. Occasionally, we also refer to chronic use of therapeutic drugs to increase physical performance and highlight the underlying mechanisms. We conclude that raising awareness on the health risks of doping in sport for all shall promote an increased awareness for healthy lifestyles across all generations.

Sex-related differences in visuomotor skill recovery following concussion in working-aged adults

BACKGROUND

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" tasks is affected in concussion. Here, we investigate the relationship between visuomotor skill performance, concussion history, and sex during the course of a post-concussion management program.

METHODS

Fifteen acutely concussed working-aged adults, 11 adults with a history of concussion, and 17 healthy controls all completed a recovery program over the course of 4 weeks. Prior to, mid-way, and following the program, all participants were tested on their visuomotor skills.

RESULTS

We observed an overall change in visuomotor behaviour in all groups, as participants completed the tasks faster and more accurately. Specifically, we observed significant visuomotor skill improvement between the first and final sessions in participants with a concussion history compared to no-concussion-history controls. Notably, we observed a stronger recovery of these skills in females.

CONCLUSIONS

Our findings indicate that (1) concussion impairs visuomotor skill performance, (2) the performance of complex, rule-based tasks showed improvement over the course of a recovery program, and (3) stronger recovery in females suggests sex-related differences in the brain networks controlling skilled performance, and the effect of injury on these networks.

Visuomotor adaptation learning not affected by repeated sport-related concussion

Sports-related concussions (SRC) have been associated with emotional, cognitive, and affective symptoms including a negative impact on motor-based learning. However, no study has assessed the impact of SRC on cerebellar-based motor learning. Cerebellar-based motor learning was assessed in three different groups of athletes with different SRC history: athletes with no history of SRC: athletes in the acute stage of SRC (within two weeks of injury), and athletes in the chronic stage of SRC (over one year after injury). We used a visuomotor adaptation task (VAT) to measure both explicit strategy-based learning and implicit error-based learning. We found that there was no difference in cerebellar dependent motor learning in SRC and non-SRC athletes. These findings suggest that the cerebellum may be more resilient to damage from SRCs than the motor cortex.

Return to Cycling Following Brain Injury: A Proposed Multidisciplinary Approach

Cycling is an important form of exercise, recreation, and transportation. Following traumatic brain injury, the benefits of cycling for health, fitness, and community mobility must be considered alongside potential risk for recurrent injury. In addition to medical concerns and exercise tolerance, key domains include motor function, attention, and visuospatial and executive function, which have previously been explored with regard to driving. Cycling skill is a combination of cognitive and motor function, and can be trained with appropriate education and intervention. We discuss the relationship of brain injury rehabilitation to specific features of cycling, including case studies.

Sabotage Detection Using DL Models on EEG Data From a Cognitive-Motor Integration Task

Objective clinical tools, including cognitive-motor integration (CMI) tasks, have the potential to improve concussion rehabilitation by helping to determine whether or not a concussion has occurred. In order to be useful, however, an individual must put forth their best effort. In this study, we have proposed a novel method to detect the difference in cortical activity between best effort (no-sabotage) and willful under-performance (sabotage) using a deep learning (DL) approach on the electroencephalogram (EEG) signals. The EEG signals from a wearable four-channel headband were acquired during a CMI task. Each participant completed sabotage and no-sabotage conditions in random order. A multi-channel convolutional neural network with long short term memory (CNN-LSTM) model with self-attention has been used to perform the time-series classification into sabotage and no-sabotage, by transforming the time-series into two-dimensional (2D) image-based scalogram representations. This approach allows the inspection of frequency-based, and temporal features of EEG, and the use of a multi-channel model facilitates in capturing correlation and causality between different EEG channels. By treating the 2D scalogram as an image, we show that the trained CNN-LSTM classifier based on automated visual analysis can achieve high levels of discrimination and an overall accuracy of 98.71% in case of intra-subject classification, as well as low false-positive rates. The average intra-subject accuracy obtained was 92.8%, and the average inter-subject accuracy was 86.15%. These results indicate that our proposed model performed well on the data of all subjects. We also compare the scalogram-based results with the results that we obtained by using raw time-series, showing that scalogram-based gave better performance. Our method can be applied in clinical applications such as baseline testing, assessing the current state of injury and recovery tracking and industrial applications like monitoring performance deterioration in workplaces.

Characterization of Cognitive-Motor Function in Women Who Have Experienced Intimate Partner Violence-Related Brain Injury

Intimate partner violence (IPV) affects at least one in three women worldwide, and up to 92% report symptoms consistent with brain injury (BI). Although a handful of studies have examined different aspects of brain structure and function in this population, none has characterized potential deficits in cognitive-motor function. This knowledge gap was addressed in the current study by having participants who had experienced IPV complete the bimanual Object Hit & Avoid (OHA) task in a Kinesiological Instrument for Normal and Altered Reaching Movement (KINARM) End-Point Laboratory. BI load, post-traumatic stress disorder (PTSD), anxiety, depression, substance use, and history of abuse were also assessed. A stepwise multiple regression was undertaken to explore the relationship between BI load and task performance while accounting for comorbid psychopathologies. Results demonstrated that BI load accounted for a significant amount of variability in the number of targets hit and the average hand speed. PTSD, anxiety, and depression also contributed significantly to the variability in these measures as well as to the number and proportion of distractor hits, and the object processing rate. Taken together, these findings suggest that IPV-related BI, as well as comorbid PTSD, anxiety, and depression, disrupt the processing required to quickly and accurately hit targets while avoiding distractors. This pattern of results reflects the complex interaction between the physical injuries induced by the episodes of IPV and the resulting impacts that these experiences have on mental health.

Potential Mechanisms of Acute Standing Balance Deficits After Concussions and Subconcussive Head Impacts: A Review

Standing balance deficits are prevalent after concussions and have also been reported after subconcussive head impacts. However, the mechanisms underlying such deficits are not fully understood. The objective of this review is to consolidate evidence linking head impact biomechanics to standing balance deficits. Mechanical energy transferred to the head during impacts may deform neural and sensory components involved in the control of standing balance. From our review of acute balance-related changes, concussions frequently resulted in increased magnitude but reduced complexity of postural sway, while subconcussive studies showed inconsistent outcomes. Although vestibular and visual symptoms are common, potential injury to these sensors and their neural pathways are often neglected in biomechanics analyses. While current evidence implies a link between tissue deformations in deep brain regions including the brainstem and common post-concussion balance-related deficits, this link has not been adequately investigated. Key limitations in current studies include inadequate balance sampling duration, varying test time points, and lack of head impact biomechanics measurements. Future investigations should also employ targeted quantitative methods to probe the sensorimotor and neural components underlying balance control. A deeper understanding of the specific injury mechanisms will inform diagnosis and management of balance deficits after concussions and subconcussive head impact exposure.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.