Multisensory integration is required for a number of daily living tasks where the inability to accurately identify simultaneity and temporality of multisensory events results in errors in judgment leading to poor decision-making and dangerous behavior. Previously, our lab discovered that older adults exhibited impaired timing of audiovisual events, particularly when making temporal order judgments (TOJs). Simultaneity judgments (SJs), however, were preserved across the lifespan. Here, we investigate the difference between the TOJ and SJ tasks in younger and older adults to assess neural processing differences between these two tasks and across the lifespan. Event-related potentials (ERPs) were studied to determine between-task and between-age differences. Results revealed task specific differences in perceiving simultaneity and temporal order, suggesting that each task may be subserved via different neural mechanisms. Here, auditory N1 and visual P1 ERP amplitudes confirmed that unisensory processing of audiovisual stimuli did not differ between the two tasks within both younger and older groups, indicating that performance differences between tasks arise either from multisensory integration or higher-level decision-making. Compared to younger adults, older adults showed a sustained higher auditory N1 ERP amplitude response across SOAs, suggestive of broader response properties from an extended temporal binding window. Our work provides compelling evidence that different neural mechanisms subserve the SJ and TOJ tasks and that simultaneity and temporal order perception are coded differently and change with age.

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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Bimodal interactions between relevant visual and tactile inputs can facilitate attentional modulation at early stages in somatosensory cortices to achieve goal-oriented behaviors. However, the specific contribution of each sensory system during attentional processing and, importantly, how these interact with the required behavioral motor goals remains unclear. Here we used electroencephalography and event-related potentials (ERPs) to test the hypothesis that activity from modality-specific somatosensory cortical regions would be enhanced with task-relevant bimodal (visual-tactile) stimuli and that the degree of modulation would depend on the difficulty of the associated sensory-motor task demands. Tactile stimuli were discrete vibrations to the index finger and visual stimuli were horizontal bars on a computer screen, both with random amplitudes. Streams of unimodal (tactile) and crossmodal (visual and tactile) stimuli were randomly presented and participants were instructed to attend to one type of stimulus (unimodal or crossmodal) and responses involved either an indication of the presence of an attended stimulus (detect), or the integration and summation of two stimulus amplitudes using a pressure-sensitive ball (grade). Force-amplitude associations were learned in a training session, and no feedback was provided during the task. ERPs were time-locked to tactile stimuli and extracted for early modality-specific components (P50, P100, N140). The P50 was enhanced with bimodal (visual-tactile) stimuli that were attended to. This was maximal when the motor requirements involved integration of the two stimuli in the grade task and when the visual stimulus occurred before (100 ms) the tactile stimulus. These results suggest that visual information relevant for movement modulates somatosensory processing as early as the primary somatosensory cortex (S1) and that the motor behavioral context influences this likely through interaction of top-down attentional and motor preparatory systems with more bottom-up crossmodal influences.

Modulating cortical excitability based on a stimulus' relevance to the task at hand is a component of sensory gating, and serves to protect higher cortical centers from being overwhelmed with irrelevant information (McIlroy et al., 2003; Kumar et al., 2005; Wasaka et al., 2005). This study examined relevancy-based modulation of cortical excitability, and corresponding behavioral responses, in the face of distracting stimuli in participants with and without a history of concussion (mean age 22 ± 3 SD years; most recent concussion 39.1 ± 30 SD months). Participants were required to make a scaled motor response to the amplitudes of visual and tactile stimuli presented individually or concurrently. Task relevance was manipulated, and stimuli were occasionally presented with irrelevant distractors. Electroencephalography (EEG) and task accuracy data were collected from participants with and without a history of concussion. The somatosensory-evoked N70 event-related potential (ERP) was significantly modulated by task relevance in the control group but not in those with a history of concussion, and there was a significantly greater cost to task accuracy in the concussion history group when relevant stimuli were presented with an irrelevant distractor. This study demonstrated that relevancybased modulation of electrophysiological responses and behavioral correlates of sensory gating differ in people with and without a history of concussion, even after patients were symptom-free and considered recovered from their injuries.

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.

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