Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, ß, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype ß signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values.

Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80 % compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable explanations with higher quality.

A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping.

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Clinical data associated with TBI patients are often measured over time and represented as time-series variables characterized by missing values and irregular time intervals. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.

Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction.

Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model's strengths in both predictive tasks and interpretable abilities.

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.

Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction.

Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ([Formula: see text]) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of [Formula: see text]'s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. [Formula: see text]'s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury.

A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reasons behind their decisions in ways that are interpretable and understandable to human users. . Numerous XAI approaches have been developed thus far, and a comparative analysis of these strategies seems necessary to discern their relevance to clinical prediction models. To this end, we first implemented two prediction models for short- and long-term outcomes of traumatic brain injury (TBI) utilizing structured tabular as well as time-series physiologic data, respectively. Six different interpretation techniques were used to describe both prediction models at the local and global levels. We then performed a critical analysis of merits and drawbacks of each strategy, highlighting the implications for researchers who are interested in applying these methodologies. The implemented methods were compared to one another in terms of several XAI characteristics such as understandability, fidelity, and stability. Our findings show that SHAP is the most stable with the highest fidelity but falls short of understandability. Anchors, on the other hand, is the most understandable approach, but it is only applicable to tabular data and not time series data.

MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain.

Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a 'second opinion' on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction-categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model's predicted results.

Recurrent Neural Network based Time-Series Modeling for Long-term Prognosis Following Acute Traumatic Brain Injury.

We developed a prognostic model for longer-term outcome prediction in traumatic brain injury (TBI) using an attention-based recurrent neural network (RNN). The model was trained on admission and time series data obtained from a multi-site, longitudinal, observational study of TBI patients. We included 110 clinical variables as model input and Glasgow Outcome Score Extended (GOSE) at six months after injury as the outcome variable. Designed to handle missing values in time series data, the RNN model was compared to an existing TBI prognostic model using 10-fold cross validation. The area under receiver operating characteristic curve (AUC) for the RNN model is 0.86 (95% CI 0.83-0.89) for binary outcomes, whereas the AUC of the comparison model is 0.69 (95% CI 0.67-0.71). We demonstrated that including time series data into prognostic models for TBI can boost the discriminative ability of prediction models with either binary or ordinal outcomes.

Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial.

BACKGROUND: With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions. OBJECTIVE: This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients. METHODS: Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment-Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods. RESULTS: The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage. CONCLUSIONS: Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation.

Deep Reinforcement Learning for Sequence-to-Sequence Models.

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide state-of-the-art performance in a wide variety of tasks, such as machine translation, headline generation, text summarization, speech-to-text conversion, and image caption generation. The underlying framework for all these models is usually a deep neural network comprising an encoder and a decoder. Although simple encoder-decoder models produce competitive results, many researchers have proposed additional improvements over these seq2seq models, e.g., using an attention-based model over the input, pointer-generation models, and self-attention models. However, such seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently, a completely novel point of view has emerged in addressing these two problems in seq2seq models, leveraging methods from reinforcement learning (RL). In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with seq2seq models that enable remembering long-term memories. We present some of the most recent frameworks that combine the concepts from RL and deep neural networks. Our work aims to provide insights into some of the problems that inherently arise with current approaches and how we can address them with better RL models. We also provide the source code for implementing most of the RL models discussed in this paper to support the complex task of abstractive text summarization and provide some targeted experiments for these RL models, both in terms of performance and training time.

Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs.

Brain-Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm-specific neuromodulations as weighted activations of prototypical templates over time. The set of timings, weights and indexes comprise a temporal marked point process (TMPP) that accesses a set of bases from vector spaces of different dimensions-a dictionary. The shallow nature of the model admits the equivalence between latent variables and representations. In this way, learning the model parameters is a case of unsupervised representation learning. We exploit principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neuromodulations (not restricted to a particular duration) can be estimated alongside the timings and features of the TMPP. We validate the proposed methodology on discrimination of movement-related tasks utilizing 32-electrode grids implanted in the frontal cortex of six epileptic subjects. We show that the learned representations from the high-gamma band (85-145 Hz) are not only interpretable, but also discriminant in a lower dimensional space. The results also underscore the practicality of our algorithm, i.e., 2 main hyperparameters that can be readily set via neurophysiology, and emphasize the need of principled and interpretable representation learning in order to model encoding mechanisms in the brain.

The Use of Botulinum Toxin Injection for Brachial Plexus Birth Injuries: A Systematic Review of the Literature.

BACKGROUND: Most brachial plexus birth injuries (BPBIs) are caused by traction on the brachial plexus during a difficult delivery. Fortunately, the possibility of complete recovery from such an incident is relatively high, with only 10% to 30% of patients having prolonged and persistent disability. These patients have muscle imbalances and co-contractions typically localized around the shoulder and elbow. These imbalances and co-contractures cause abnormal motor performances and bone/joint deformities. Typically, physical/occupational therapies are the conventional therapeutic modalities but are often times inadequate. Botulinum toxin A (BTX-A) injections into targeted muscles have been used to combat the muscular imbalances and co-contractions. METHODS: With compliance to PRISMA guidelines, a systematic review was performed to identify studies published between 2000 and 2017 that used BTX-A to treat neonatal brachial plexus palsies. RESULTS: Ten studies were included, involving 325 patients. Three groups of indications for the use of BTX-A were identified: (1) internal rotation/adduction contracture of the shoulder; (2) elbow flexion lag/elbow extension lag; and (3) forearm pronation contracture. CONCLUSIONS: The included studies show an overall beneficial effect of BTX-A in treating co-contractures seen in patients with BPBI. Specifically, BTX-A is shown to reduce internal rotation/adduction contractures of the shoulder, elbow flexion/extension contractures, and forearm pronation contractures. These beneficial effects are blunted when used in older patients. Nevertheless, BTX-A is a useful treatment for BPBIs with a relatively low-risk profile.

Survey of the functional priorities in patients with disability due to neuromuscular disorders.

TITLE: Survey of the functional priorities in patients with disability due to neuromuscular disorders. OBJECTIVE: This study attempts to determine the functional priorities for patients with neuromuscular disorders. METHODS: A survey asking about functional priorities with respect to activities of daily living, ankle foot orthotic design, and assistive device design, was distributed to patients with neuromuscular disorders to assess the needs of patients from their perspectives. Descriptive statistics were used to analyse answers. RESULTS: A total of 171 subjects with neuromuscular disorders responded to the questionnaire. Of the respondents with weakness in both the upper and lower extremities, 45% stated that if they had to choose between correction of one or the other, they would prefer that of their lower extremities. Activities that patients most frequently wanted to gain independence with were mobility and transfers (46%), followed by toilet use and hygiene (32%). The most popular control mechanism of an assistive device was voice activation (35%). CONCLUSION: This study assessed the functional priorities of those with neuromuscular disorders. Although such individuals can experience a range of weakness in the upper and/or lower extremities, common functional priorities were reported: independence with mobility, transfers, toilet use and hygiene. Knowledge of these priorities will help guide development of assistive devices that will restore function in the future. Implications for Rehabilitation Neuromuscular Disorders • Neuromuscular disorders result in disabling weakness; there are few cures and many are unable to carry out activities of daily living. • Information that would be helpful in determining functional priorities is limited. • In a survey of 171 patients with neuromuscular disorders, functional priorities included mobility and transfers (46%), followed by toilet use and hygiene (32%). • Of the respondents with weakness in both the upper and lower extremities, 45% stated that if they had to choose between correction of one or the other, they would prefer that of their lower extremities. • If an assistive device were to be created to help those with neuromuscular disorders, the most popular control mechanism would be voice activation (35%).

A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone.

This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-time SE. This arrangement works as an assistive tool to HA. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the developed SE application in noisy environments.

Smartphone based real-time super Gaussian single microphone Speech Enhancement to improve intelligibility for hearing aid users using formant information.

In this paper, we present a Speech Enhancement (SE) technique to improve intelligibility of speech perceived by Hearing Aid users using smartphone as an assistive device. We use the formant frequency information to improve the overall quality and intelligibility of the speech. The proposed SE method is based on new super Gaussian joint maximum a Posteriori (SGJMAP) estimator. Using the priori information of formant frequency locations, the derived gain function has " tradeoff" factors that allows the smartphone user to customize perceptual preference, by controlling the amount of noise suppression and speech distortion in real-time. The formant frequency information helps the hearing aid user to control the gains over the non-formant frequency band, allowing the HA users to attain more noise suppression while maintaining the speech intelligibility using a smartphone application. Objective intelligibility measures and subjective results reflect the usability of the developed SE application in noisy real world acoustic environment.

Influence of MVDR beamformer on a Speech Enhancement based Smartphone application for Hearing Aids.

This paper presents the minimum variance distortionless response (MVDR) beamformer combined with a Speech Enhancement (SE) gain function as a real-time application running on smartphones that work as an assistive device to Hearing Aids. It has been shown that beamforming techniques improve the Signal to Noise Ratio (SNR) in noisy conditions. In the proposed algorithm, MVDR beamformer is used as an SNR booster for the SE method. The proposed SE gain is based on the Log-Spectral Amplitude estimator to improve the speech quality in the presence of different background noises. Objective evaluation and intelligibility measures support the theoretical analysis and show significant improvements of the proposed method in comparison with existing methods. Subjective test results show the effectiveness of the application in real-world noisy conditions at SNR levels of -5 dB, 0 dB, and 5 dB.

Ovine model of neuropathic pain for assessing mechanisms of spinal cord stimulation therapy via dorsal horn recordings, von Frey filaments, and gait analysis.

BACKGROUND: It is becoming increasingly important to understand the mechanisms of spinal cord stimulation (SCS) in alleviating neuropathic pain as novel stimulation paradigms arise. PURPOSE: Additionally, the small anatomic scale of current SCS animal models is a barrier to more translational research. METHODS: Using chronic constriction injury (CCI) of the common peroneal nerve (CPN) in sheep (ovine), we have created a chronic model of neuropathic pain that avoids motor deficits present in prior large animal models. This large animal model has allowed us to implant clinical grade SCS hardware, which enables both acute and chronic testing using von Frey filament thresholds and gait analysis. Furthermore, the larger anatomic scale of the sheep allows for simultaneous single-unit recordings from the dorsal horn and SCS with minimal electrical artifact. RESULTS: Detectable tactile hypersensitivity occurred 21 days after nerve injury, with preliminary indications that chronic SCS may reverse it in the painful limb. Gait analysis revealed no hoof drop in the CCI model. Single neurons were identified and discriminated in the dorsal horn, and their activity was modulated via SCS. Unlike previous large animal models that employed a complete transection of the nerve, no motor deficit was observed in the sheep with CCI. CONCLUSION: To our knowledge, this is the first reported large animal model of chronic neuropathic pain which facilitates the study of both acute and chronic SCS using complementary behavioral and electrophysiologic measures. As demonstrated by our successful establishment of these techniques, an ovine model of neuropathic pain is suitable for testing the mechanisms of SCS.

Femoral vascular conductance and peroneal muscle sympathetic nerve activity responses to acute epidural spinal cord stimulation in humans.

NEW FINDINGS: What is the central question of this research? Does acute spinal cord stimulation increase vascular conductance and decrease muscle sympathetic nerve activity in the lower limbs of humans? What is the main finding and its importance? Acute spinal cord stimulation led to a rapid rise in femoral vascular conductance, and peroneal muscle sympathetic nerve activity demonstrated a delayed reduction that was not associated with the initial increase in femoral vascular conductance. These findings suggest that neural mechanisms in addition to attenuated muscle sympathetic nerve activity might be involved in the initial increase in femoral vascular conductance during acute spinal cord stimulation. ABSTRACT: Clinical cases have indicated an increase in peripheral blood flow after continuous epidural spinal cord stimulation (SCS) and that reduced muscle sympathetic nerve activity (MSNA) might be a potential mechanism. However, no studies in humans have directly examined the effects of acute SCS (<60 min) on vascular conductance and MSNA. In study 1, we tested the hypothesis that acute SCS (<60 min) of the thoracic spine would lead to increased common femoral vascular conductance, but not brachial vascular conductance, in 11 patients who previously underwent surgical SCS implantation for management of neuropathic pain. Throughout 60 min of SCS, common femoral artery conductance was elevated and significantly different from brachial artery conductance [in millilitres per minute: 15 min, change (Δ) 26 ± 37 versus Δ-2 ± 19%; 30 min, Δ28 ± 45 versus Δ0 ± 26%; 45 min, Δ48 ± 43 versus Δ2 ± 21%; 60 min, Δ36 ± 61 versus Δ1 ± 24%; and 15 min post-SCS, Δ51 ± 64 versus Δ6 ± 33%; P = 0.013]. A similar examination in a patient with cervical SCS revealed minimal changes in vascular conductance. In study 2, we examined whether acute SCS reduces peroneal MSNA in a subset of SCS patients (n = 5). The MSNA burst incidence in response to acute SCS gradually declined and was significantly reduced at 45 and 60 min of SCS (in bursts per 100 heart beats: 15 min, Δ-1 ± 12%; 30 min, Δ-14 ± 12%; 45 min, Δ-19 ± 16%; 60 min, Δ-24 ± 18%; and 15 min post-SCS: Δ-11 ± 7%; P = 0.015). These data demonstrate that acute SCS rapidly increases femoral vascular conductance and reduces peroneal MSNA. The gradual reduction in peroneal MSNA observed during acute SCS suggests that neural mechanisms in addition to attenuated MSNA might be involved in the acute increase in femoral vascular conductance.

Spinal dura mater: biophysical characteristics relevant to medical device development.

Understanding the relevant biophysical properties of the spinal dura mater is essential to the design of medical devices that will directly interact with this membrane or influence the contents of the intradural space. We searched the literature and reviewed the pertinent characteristics for the design, construction, testing, and imaging of novel devices intended to perforate, integrate, adhere or reside within or outside of the spinal dura mater. The spinal dura mater is a thin tubular membrane composed of collagen and elastin fibres that varies in circumference along its length. Its mechanical properties have been well-described, with the longitudinal tensile strength exceeding the transverse strength. Data on the bioelectric, biomagnetic, optical and thermal characteristics of the spinal dura are limited and sometimes taken to be similar to those of water. While various modalities are available to visualise the spinal dura, magnetic resonance remains the best modality to segment its structure. The reaction of the spinal dura to imposition of a foreign body or other manipulations of it may compromise its biomechanical and immune-protective benefits. Therefore, dural sealants and replacements are of particular clinical, research and commercial interest. In conclusion, existing devices that are in clinical use for spinal cord stimulation, intrathecal access or intradural implantation largely adhere to traditional designs and their attendant limitations. However, if future devices are built with an understanding of the dura's properties incorporated more fully into the designs, there is potential for improved performance.