Endogenous orexin and hyperacute autonomic responses after resuscitation in a preclinical model of cardiac arrest.

Objectives: The study of autonomic responses to cardiac arrest (CA) resuscitation deserves attention due to the impact of autonomic function on survival and arousal. Orexins are known to modulate autonomic function, but the role of endogenous orexin in hyperacute recovery of autonomic function post-resuscitation is not well understood. We hypothesized that endogenous orexin facilitates hyperacute cardiovascular sympathetic activity post-resuscitation, and this response could be attenuated by suvorexant, a dual orexin receptor antagonist. Methods: A well-established 7-min asphyxial CA rat model was studied. Heart rate (HR) and blood pressure were monitored from baseline to 90-min post-resuscitation. Autonomic function was evaluated by spectral analysis of HR variability, whereby the ratio of low- and high-frequency components (LF/HF ratio) represents the balance between sympathetic/parasympathetic activities. Plasma orexin-A levels and orexin receptors immunoreactivity in the rostral ventrolateral medulla (RVLM), the key central region for regulating sympathetic output, were measured post-resuscitation. Neurological outcome was assessed via neurologic-deficit score at 4-h post-resuscitation. Key results: A significant increase in HR was found over 25-40 min post-resuscitation (p < 0.01 vs. baseline), which was attenuated by suvorexant significantly (p < 0.05). Increased HR (from 15-to 25-min post-resuscitation) was correlated with better neurological outcomes (rs = 0.827, p = 0.005). There was no evident increase in mean arterial pressure over 25-40 min post-resuscitation, while systolic pressure was reduced greatly by suvorexant (p < 0.05). The LF/HF ratio was higher in animals with favorable outcomes than in animals injected with suvorexant over 30-40 min post-resuscitation (p < 0.05). Plasma orexin-A levels elevated at 15-min and peaked at 30-min post-resuscitation (p < 0.01 vs. baseline). Activated orexin receptors-immunoreactive neurons were found co-stained with tyrosine hydroxylase-immunopositive cells in the RVLM at 2-h post-resuscitation. Conclusion: Together, increased HR and elevated LF/HF ratio indicative of sympathetic arousal during a critical window (25-40 min) post-resuscitation are observed in animals with favorable outcomes. The orexin system appears to facilitate this hyperacute autonomic response post-CA.

EEG reactivity in neurologic prognostication in post-cardiac arrest patients: A narrative review.

Electroencephalographic reactivity (EEG-R) is a promising early predictor of arousal in comatose patients after cardiac arrest. Despite recent guidelines advocating for the integration of EEG-R into the multimodal prognostication model, EEG-R testing methods remain heterogeneous across studies. While efforts towards standardization have been made to reduce interrater variability by the development of quantitative approaches and machine learning models, future validation studies are needed to increase clinical applicability. Furthermore, the specific neurophysiological mechanisms and neuroanatomical correlates underlying EEG-R are not fully understood. In this narrative review, we explore the value and possible mechanisms of EEG-R, focusing on post-cardiac arrest comatose patients. We aim to discuss the current standard of knowledge and future directions, as well as elucidate possible implications for patient care and research.

High frequency oscillations may improve somatosensory evoked potential detection of good outcomes in disorders of consciousness secondary to acute neurologic injury.

BACKGROUND: Somatosensory evoked potentials (SEPs) are highly specific predictors of poor prognosis in hypoxic-ischemic coma when cortical responses (N20s) are absent. However, bilateral N20 presence is nonspecific for good outcomes. High-frequency oscillations (HFOs) in the SEP waveform predict neurologic recovery in animals, but clinical applications are poorly understood. We sought to develop a clinical measure of HFOs to potentially improve detection of good outcomes in coma. MATERIALS AND METHODS: We collected SEP waveform data from all comatose inpatients (GCS<=8) who underwent neurologic prognostication from 2020 to 2022 at Johns Hopkins Hospital. We developed a novel measure - HFO evoked to spontaneous ratios (HFO-ESRs) - and applied this to those patients with bilaterally present N20s using both standard univariate classification and cubic kernal vector machine (SVM) models to predict the last documented in-hospital Glasgow Coma Scale (GCS) prior to discharge or death. RESULTS: Of 58 total patients, 34 (58.6%) had bilaterally present N20s. Of these, 14 had final GCS>=9, and 20 had final GCS<=8. Mean age was 52 (+/- 17) years, 20.1% female. Etiologies of coma were primarily global hypoxic-ischemic brain injury (79.4%), intracranial hemorrhage (11.8%), and traumatic brain injury (2.9%). In univariate classification, the addition of averaged HFO-ESRs to bilaterally present N20s predicted final GCS>=9 with 68% specificity. The SVM model further improved specificity to 85%. CONCLUSIONS: In this pilot investigation, we developed a novel clinical measure of SEP HFOs. Incorporation of this measure may improve the specificity of the SEP to predict in-hospital GCS outcomes in coma, but requires further validation in specific neurologic injuries and with longitudinal outcomes.

Driving Fatigue Detection Based on Hybrid Electroencephalography and Eye Tracking.

EEG-based unimodal method has demonstrated substantial success in the detection of driving fatigue. Nonetheless, the data from a single modality might be not sufficient to optimize fatigue detection due to incomplete information. To address this limitation and enhance the performance of driving fatigue detection, a novel multimodal architecture combining electroencephalography (EEG) and eye tracking data was proposed in this study. Specifically, EEG and eye tracking data were separately input into encoders, generating two one-dimensional (1D) features. Subsequently, these 1D features were fed into a cross-modal predictive alignment module to improve fusion efficiency and two 1D attention modules to enhance feature representation. Furthermore, the fused features were recognized by a linear classifier. To evaluate the effectiveness of the proposed multimodal method, comprehensive validation tasks were conducted, including intra-session, cross-session, and cross-subject evaluations. In the intra-session task, the proposed architecture achieves an exceptional average accuracy of 99.93%. Moreover, in the cross-session task, our method results in an average accuracy of 88.67%, surpassing the performance of EEG-only approach by 8.52%, eye tracking-only method by 5.92%, multimodal deep canonical correlation analysis (DCCA) technique by 0.42%, and multimodal deep generalized canonical correlation analysis (DGCCA) approach by 0.84%. Similarly, in the cross-subject task, the proposed approach achieves an average accuracy of 78.19%, outperforming EEG-only method by 5.87%, eye tracking-only approach by 4.21%, DCCA method by 0.55%, and DGCCA approach by 0.44%. The experimental results conclusively illustrate the superior effectiveness of the proposed method compared to both single modality approaches and canonical correlation analysis-based multimodal methods. Overall, this study provides a new and effective strategy for driving fatigue detection.

Wavelet Transforms Significantly Sparsify and Compress Tactile Interactions.

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform-1D, 2D, or 3D-would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics.

Sensory integration for neuroprostheses: from functional benefits to neural correlates.

In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation-elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.

Focused Ultrasound Neuromodulation of Human In Vitro Neural Cultures in Multi-Well Microelectrode Arrays.

The neuromodulatory effects of focused ultrasound (FUS) have been demonstrated in animal models, and FUS has been used successfully to treat movement and psychiatric disorders in humans. However, despite the success of FUS, the mechanism underlying its effects on neurons remains poorly understood, making treatment optimization by tuning FUS parameters difficult. To address this gap in knowledge, we studied human neurons in vitro using neurons cultured from human-induced pluripotent stem cells (HiPSCs). Using HiPSCs allows for the study of human-specific neuronal behaviors in both physiologic and pathologic states. This report presents a protocol for using a high-throughput system that enables the monitoring and quantification of the neuromodulatory effects of FUS on HiPSC neurons. By varying the FUS parameters and manipulating the HiPSC neurons through pharmaceutical and genetic modifications, researchers can evaluate the neural responses and elucidate the neuro-modulatory effects of FUS on HiPSC neurons. This research could have significant implications for the development of safe and effective FUS-based therapies for a range of neurological and psychiatric disorders.

Volume loss during muscle reinnervation surgery is correlated with reduced CMAP amplitude but not reduced force output in a rat hindlimb model.

Introduction: Muscle reinnervation (MR) surgery offers rehabilitative benefits to amputees by taking severely damaged nerves and providing them with new denervated muscle targets (DMTs). However, the influence of physical changes to muscle tissue during MR surgery on long-term functional outcomes remains understudied. Methods: Our rat hindlimb model of MR surgery utilizes vascularized, directly neurotized DMTs made from the lateral gastrocnemius (LG), which we employed to assess the impact of muscle tissue size on reinnervation outcomes, specifically pairing the DMT with the transected peroneal nerve. We conducted MR surgery with both DMTs at full volume and DMTs with partial volume loss of 500 mg at the time of surgery (n = 6 per group) and measured functional outcomes after 100 days of reinnervation. Compound motor action potentials (CMAPs) and isometric tetanic force production was recorded from reinnervated DMTs and compared to contralateral naïve LG muscles as positive controls. Results: Reinnervated DMTs consistently exhibited lower mass than positive controls, while DMTs with partial volume loss showed no significant mass reduction compared to full volume DMTs (p = 0.872). CMAP amplitudes were lower on average in reinnervated DMTs, but a broad linear correlation also exists between muscle mass and maximum CMAP amplitude irrespective of surgical group (R2 = 0.495). Surprisingly, neither MR group, with or without volume loss, demonstrated decreased force compared to positive controls. The average force output of reinnervated DMTs, as a fraction of the contralateral LG's force output, approached 100% for both MR groups, a notable deviation from the 9.6% (±6.3%) force output observed in our negative control group at 7 days post-surgery. Tissue histology analysis revealed few significant differences except for a marked decrease in average muscle fiber area of reinnervated DMTs with volume loss compared to positive controls (p = 0.001). Discussion: The results from our rat model of MR suggests that tissue electrophysiology (CMAPs) and kinesiology (force production) may recover on different time scales, with volumetric muscle loss at the time of MR surgery not significantly reducing functional outcome measurements for the DMTs after 100 days of reinnervation.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology.

BACKGROUND: Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS: This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS: Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Tethered spinal cord tension assessed via ultrasound elastography in computational and intraoperative human studies.

BACKGROUND: Tension in the spinal cord is a trademark of tethered cord syndrome. Unfortunately, existing tests cannot quantify tension across the bulk of the cord, making the diagnostic evaluation of stretch ambiguous. A potential non-destructive metric for spinal cord tension is ultrasound-derived shear wave velocity (SWV). The velocity is sensitive to tissue elasticity and boundary conditions including strain. We use the term Ultrasound Tensography to describe the acoustic evaluation of tension with SWV. METHODS: Our solution Tethered cord Assessment with Ultrasound Tensography (TAUT) was utilized in three sub-studies: finite element simulations, a cadaveric benchtop validation, and a neurosurgical case series. The simulation computed SWV for given tensile forces. The cadaveric model with induced tension validated the SWV-tension relationship. Lastly, SWV was measured intraoperatively in patients diagnosed with tethered cords who underwent treatment (spinal column shortening). The surgery alleviates tension by decreasing the vertebral column length. RESULTS: Here we observe a strong linear relationship between tension and squared SWV across the preclinical sub-studies. Higher tension induces faster shear waves in the simulation (R2 = 0.984) and cadaveric (R2 = 0.951) models. The SWV decreases in all neurosurgical procedures (p < 0.001). Moreover, TAUT has a c-statistic of 0.962 (0.92-1.00), detecting all tethered cords. CONCLUSIONS: This study presents a physical, clinical metric of spinal cord tension. Strong agreement among computational, cadaveric, and clinical studies demonstrates the utility of ultrasound-induced SWV for quantitative intraoperative feedback. This technology is positioned to enhance tethered cord diagnosis, treatment, and postoperative monitoring as it differentiates stretched from healthy cords.

Tethered spinal cord syndrome occurs when surrounding tissue attaches to and causes stretching across the spinal cord. People with a tethered cord can experience weakness, pain, and loss of bladder control. Although increased tension in the spinal cord is known to cause these symptoms, evaluating the amount of stretching remains challenging. We investigated the ability of an ultrasound imaging approach to measure spinal cord tension. We studied our method in a computer simulation, a benchtop validation model, and in six people with tethered cords during surgery that they were undergoing to reduce tension. In each phase, the approach could detect differences between stretched spinal cords and spinal cords in a healthy state. Our method could potentially be used in the future to improve the care of people with a tethered cord.

Non-contrast ultrasound image analysis for spatial and temporal distribution of blood flow after spinal cord injury.

Ultrasound technology can provide high-resolution imaging of blood flow following spinal cord injury (SCI). Blood flow imaging may improve critical care management of SCI, yet its duration is limited clinically by the amount of contrast agent injection required for high-resolution, continuous monitoring. In this study, we aim to establish non-contrast ultrasound as a clinically translatable imaging technique for spinal cord blood flow via comparison to contrast-based methods and by measuring the spatial distribution of blood flow after SCI. A rodent model of contusion SCI at the T12 spinal level was carried out using three different impact forces. We compared images of spinal cord blood flow taken using both non-contrast and contrast-enhanced ultrasound. Subsequently, we processed the images as a function of distance from injury, yielding the distribution of blood flow through space after SCI, and found the following. (1) Both non-contrast and contrast-enhanced imaging methods resulted in similar blood flow distributions (Spearman's ρ = 0.55, p < 0.0001). (2) We found an area of decreased flow at the injury epicenter, or umbra (p < 0.0001). Unexpectedly, we found increased flow at the periphery, or penumbra (rostral, p < 0.05; caudal, p < 0.01), following SCI. However, distal flow remained unchanged, in what is presumably unaffected tissue. (3) Finally, tracking blood flow in the injury zones over time revealed interesting dynamic changes. After an initial decrease, blood flow in the penumbra increased during the first 10 min after injury, while blood flow in the umbra and distal tissue remained constant over time. These results demonstrate the viability of non-contrast ultrasound as a clinical monitoring tool. Furthermore, our surprising observations of increased flow in the injury periphery pose interesting new questions about how the spinal cord vasculature reacts to SCI, with potentially increased significance of the penumbra.

Neuromuscular implants: Interfacing with skeletal muscle for improved clinical translation of prosthetic limbs.

After an amputation, advanced prosthetic limbs can be used to interface with the nervous system and restore motor function. Despite numerous breakthroughs in the field, many of the recent research advancements have not been widely integrated into clinical practice. This review highlights recent innovations in neuromuscular implants-specifically those that interface with skeletal muscle-which could improve the clinical translation of prosthetic technologies. Skeletal muscle provides a physiologic gateway to harness and amplify signals from the nervous system. Recent surgical advancements in muscle reinnervation surgeries leverage the "bio-amplification" capabilities of muscle, enabling more intuitive control over a greater number of degrees of freedom in prosthetic limbs than previously achieved. We anticipate that state-of-the-art implantable neuromuscular interfaces that integrate well with skeletal muscle and novel surgical interventions will provide a long-term solution for controlling advanced prostheses. Flexible electrodes are expected to play a crucial role in reducing foreign body responses and improving the longevity of the interface. Additionally, innovations in device miniaturization and ongoing exploration of shape memory polymers could simplify surgical procedures for implanting such interfaces. Once implanted, wireless strategies for powering and transferring data from the interface can eliminate bulky external wires, reduce infection risk, and enhance day-to-day usability. By outlining the current limitations of neuromuscular interfaces along with potential future directions, this review aims to guide continued research efforts and future collaborations between engineers and specialists in the field of neuromuscular and musculoskeletal medicine.

Myomatrix arrays for high-definition muscle recording.

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

Visualizing tactile feedback: an overview of current technologies with a focus on ultrasound elastography.

Tissue elasticity remains an essential biomarker of health and is indicative of irregularities such as tumors or infection. The timely detection of such abnormalities is crucial for the prevention of disease progression and complications that arise from late-stage illnesses. However, at both the bedside and the operating table, there is a distinct lack of tactile feedback for deep-seated tissue. As surgical techniques advance toward remote or minimally invasive options to reduce infection risk and hasten healing time, surgeons lose the ability to manually palpate tissue. Furthermore, palpation of deep structures results in decreased accuracy, with the additional barrier of needing years of experience for adequate confidence of diagnoses. This review delves into the current modalities used to fulfill the clinical need of quantifying physical touch. It covers research efforts involving tactile sensing for remote or minimally invasive surgeries, as well as the potential of ultrasound elastography to further this field with non-invasive real-time imaging of the organ's biomechanical properties. Elastography monitors tissue response to acoustic or mechanical energy and reconstructs an image representative of the elastic profile in the region of interest. This intuitive visualization of tissue elasticity surpasses the tactile information provided by sensors currently used to augment or supplement manual palpation. Focusing on common ultrasound elastography modalities, we evaluate various sensing mechanisms used for measuring tactile information and describe their emerging use in clinical settings where palpation is insufficient or restricted. With the ongoing advancements in ultrasound technology, particularly the emergence of micromachined ultrasound transducers, these devices hold great potential in facilitating early detection of tissue abnormalities and providing an objective measure of patient health.

Hyperacute autonomic and cortical function recovery following cardiac arrest resuscitation in a rodent model.

OBJECTIVE: There is a complex interaction between nervous and cardiovascular systems, but sparse data exist on brain-heart electrophysiological responses to cardiac arrest resuscitation. Our aim was to investigate dynamic changes in autonomic and cortical function during hyperacute stage post-resuscitation. METHODS: Ten rats were resuscitated from 7-min cardiac arrest, as indicators of autonomic response, heart rate (HR), and its variability (HRV) were measured. HR was monitored through continuous electrocardiography, while HRV was assessed via spectral analysis, whereby the ratio of low-/high-frequency (LF/HF) power indicates the balance between sympathetic/parasympathetic activities. Cortical response was evaluated by continuous electroencephalography and quantitative analysis. Parameters were quantified at 5-min intervals over the first-hour post-resuscitation. Neurological outcome was assessed by Neurological Deficit Score (NDS, range 0-80, higher = better outcomes) at 4-h post-resuscitation. RESULTS: A significant increase in HR was noted over 15-30 min post-resuscitation (p < 0.01 vs.15-min, respectively) and correlated with higher NDS (rs = 0.56, p < 0.01). LF/HF ratio over 15-20 min was positively correlated with NDS (rs = 0.75, p < 0.05). Gamma band power surged over 15-30 min post-resuscitation (p < 0.05 vs. 0-15 min, respectively), and gamma band fraction during this period was associated with NDS (rs ≥0.70, p < 0.05, respectively). Significant correlations were identified between increased HR and gamma band power during 15-30 min (rs ≥0.83, p < 0.01, respectively) and between gamma band fraction and LF/HF ratio over 15-20 min post-resuscitation (rs = 0.85, p < 0.01). INTERPRETATIONS: Hyperacute recovery of autonomic and cortical function is associated with favorable functional outcomes. While this observation needs further validation, it presents a translational opportunity for better autonomic and neurologic monitoring during early periods post-resuscitation to develop novel interventions.

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel relations that are known to be beneficial for EEG-based classification. Additionally, these models require substantial data for training, which is often impractical due to the high cost of data collection. To simultaneously address these two issues, we propose a Self-Attentive Channel-Connectivity Capsule Network (SACC-CapsNet) for EEG-based driving fatigue detection in this paper. SACC-CapsNet starts with a temporal-channel attention module to investigate the critical temporal information and important channels for driving fatigue detection, refining the input EEG signals. Subsequently, the refined EEG data are transformed into a channel covariance matrix to capture the inter-channel relations, followed by selective kernel attention to extract the highly discriminative channel-connectivity features. Finally, a capsule neural network is employed to effectively learn the relationships between connectivity features, which is more suitable for limited data. To confirm the effectiveness of SACC-CapsNet, we collected 24-channel EEG data from 31 subjects (mean age=23.13±2.68 years, male/female=18/13) in a simulated fatigue driving environment. Extensive experiments were conducted with the acquired data, and the comparison results show that our proposed model outperforms state-of-the-art methods. Additionally, the channel covariance matrix learned from SACC-CapsNet reveals that the frontal pole is most informative for detecting driving fatigue, followed by the parietal and central regions. Intriguingly, the temporal-channel attention module can enhance the significance of these critical regions, and the reconstructed channel covariance matrix generated by the decoder network of SACC-CapsNet can effectively preserve valuable information about them.

Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing.

Although consistent evidence has revealed that cognitive impairment is a common sequela in patients with mild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying that mild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation.

Functionally Adaptive Myosite Selection Using High-Density sEMG for Upper Limb Myoelectric Prostheses.

OBJECTIVE: Our study defines a novel electrode placement method called Functionally Adaptive Myosite Selection (FAMS), as a tool for rapid and effective electrode placement during prosthesis fitting. We demonstrate a method for determining electrode placement that is adaptable towards individual patient anatomy and desired functional outcomes, agnostic to the type of classification model used, and provides insight into expected classifier performance without training multiple models. METHODS: FAMS relies on a separability metric to rapidly predict classifier performance during prosthesis fitting. RESULTS: The results show a predictable relationship between the FAMS metric and classifier accuracy (3.45%SE), allowing estimation of control performance with any given set of electrodes. Electrode configurations selected using the FAMS metric show improved control performance ( ) for target electrode counts compared to established methods when using an ANN classifier, and equivalent performance ( R2 ≥ .96) to previous top-performing methods on an LDA classifier, with faster convergence ( ). We used the FAMS method to determine electrode placement for two amputee subjects by using the heuristic to search through possible sets, and checking for saturation in performance vs electrode count. The resulting configurations that averaged 95.8% of the highest possible classification performance using a mean 25 number of electrodes (19.5% of the available sites). SIGNIFICANCE: FAMS can be used to rapidly approximate the tradeoffs between increased electrode count and classifier performance, a useful tool during prosthesis fitting.

Quantification of Cerebral Vascular Autoregulation Immediately Following Resuscitation from Cardiac Arrest.

Cerebral vascular autoregulation is impaired following resuscitation from cardiac arrest (CA), and its quantification may allow assessing CA-induced brain injury. However, hyperemia occurring immediately post-resuscitation limits the application of most metrics that quantify autoregulation. Therefore, to characterize autoregulation during this critical period, we developed three novel metrics based on how the cerebrovascular resistance (CVR) covaries with changes in cerebral perfusion pressure (CPP): (i) Î¸CVR, which quantifies the CVR vs CPP gradient, (ii) a CVR-based transfer function analysis, and (iii) CVRx, the correlation coefficient between CPP and CVR. We tested these metrics in a model of asphyxia induced CA and resuscitation using seven adult male Wistar rats. Mean arterial pressure (MAP) and cortical blood flow recorded for 30 min post-resuscitation via arterial cannulation and laser speckle contrast imaging, were used as surrogates of CPP and cerebral blood flow (CBF), while CVR was computed as the CPP/CBF ratio. Using our metrics, we found that the status of cerebral vascular autoregulation altered substantially during hyperemia, with changes spread throughout the 0-0.05 Hz frequency band. Our metrics push the boundary of how soon autoregulation can be assessed, and if validated against outcome markers, may help develop a reliable metric of brain injury post-resuscitation.

Low-Intensity Pulsed Ultrasound Neuromodulation of a Rodent's Spinal Cord Suppresses Motor Evoked Potentials.

OBJECTIVE: Here we investigate the ability of low-intensity ultrasound (LIUS) applied to the spinal cord to modulate the transmission of motor signals. METHODS: Male adult Sprague-Dawley rats (n = 10, 250-300 g, 15 weeks old) were used in this study. Anesthesia was initially induced with 2% isoflurane carried by oxygen at 4 L/min via a nose cone. Cranial, upper extremity, and lower extremity electrodes were placed. A thoracic laminectomy was performed to expose the spinal cord at the T11 and T12 vertebral levels. A LIUS transducer was coupled to the exposed spinal cord, and motor evoked potentials (MEPs) were acquired each minute for either 5- or 10-minutes of sonication. Following the sonication period, the ultrasound was turned off and post-sonication MEPs were acquired for an additional 5 minutes. RESULTS: Hindlimb MEP amplitude significantly decreased during sonication in both the 5- (p < 0.001) and 10-min (p = 0.004) cohorts with a corresponding gradual recovery to baseline. Forelimb MEP amplitude did not demonstrate any statistically significant changes during sonication in either the 5- (p = 0.46) or 10-min (p = 0.80) trials. CONCLUSION: LIUS applied to the spinal cord suppresses MEP signals caudal to the site of sonication, with recovery of MEPs to baseline after sonication. SIGNIFICANCE: LIUS can suppress motor signals in the spinal cord and may be useful in treating movement disorders driven by excessive excitation of spinal neurons.