Identifying neural correlates of balance impairment in traumatic brain injury using partial least squares correlation analysis.

Objective.Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment. However, such stratification is not straightforward, and it warrants a data-driven approach.Approach.We conducted a study to assess the balance control using a computerized posturography platform in 17 individuals with TBI and 15 age-matched healthy controls. We stratified the TBI participants into balance-impaired and non-impaired TBI usingk-means clustering of either center of pressure (COP) displacement during a balance perturbation task or Berg Balance Scale score as a functional outcome measure. We analyzed brain functional connectivity using the imaginary part of coherence across different cortical regions in various frequency bands. These connectivity features are then studied using the mean-centered partial least squares correlation analysis, which is a multivariate statistical framework with the advantage of handling more features than the number of samples, thus making it suitable for a small-sample study.Main results.Based on the nonparametric significance testing using permutation and bootstrap procedure, we noticed that the weakened theta-band connectivity strength in the following regions of interest significantly contributed to distinguishing balance impaired from non-impaired population, regardless of the type of stratification:left middle frontal gyrus, right paracentral lobule, precuneus, andbilateral middle occipital gyri. Significance.Identifying neural regions linked to balance impairment enhances our understanding of TBI-related balance dysfunction and could inform new treatment strategies. Future work will explore the impact of balance platform training on sensorimotor and visuomotor connectivity.

Effects of Computerized Biofeedback-based Balance Intervention on the Muscle Coactivation Patterns during Dynamic Postural Control in Traumatic Brain Injury.

Balance Dysfunction (BDF) is a severe conse-quence of Traumatic Brain Injury (TBI) that significantly increases the falls risk. However, the neuromuscular mecha-nisms of the BDF are not adequately researched. Therefore, in this study, our objective was to investigate the effects of a Computerized Biofeedback-based Balance Intervention (CBBI) on the muscle coactivation patterns in a group of TBI participants. This study presents the findings from 13 TBI individuals randomized into the Intervention group (TBI - INT, N=6) and Control group (TBI-CTL, N=7). Using a computerized posturography platform (Neurocom Balance Master) during baseline and follow-up assessment visits, the participant's pos-tural response to anterior-posterior balance perturbations were recorded in a multimodal setup including electroencephalogra-phy (EEG), electromyography (EMG), and the platform sway in terms of center of pressure (COP). The muscle responses were recorded from lower-limb muscles, including tibialis an-terior (TA) and gastrocnemius (GAST), whose coactivation was computed using a metric called Co-Contraction Index (CCI). Clinical outcome measures such as Berg Balance Scale (BBS), 10 Meter Walk Test (10MWT), and Timed Up-and-Go (TUG) tests were used to evaluate functional balance and mobility. The comparison of CCI values across time points (baseline and follow-up) revealed a significant decrease (p<0.01) in the TBI-INT group but not TBI-CTL. The intervention-related changes in CCI correlated with the changes in BBS score (from baseline to follow-up). These preliminary findings demonstrate that the CBBI training may help postural stability by facilitating the coactivation between muscles involved in postural control. Clinical relevance- The current knowledge of changes in the neuromuscular response to balance perturbation in TBI is limited. Our study opens the possibility of using the muscle CCI metric to evaluate the muscle response in individuals with impaired balance.

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders.

Goal: This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Methods: First, an iEMG signal is decimated to produce a set of "disjoint" downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. Results: The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation-accuracy = [Formula: see text], sensitivity (normal) = [Formula: see text], sensitivity (myopathy) = [Formula: see text], sensitivity (neuropathy) = [Formula: see text], specificity (normal) = [Formula: see text], specificity (myopathy) = [Formula: see text], and specificity (neuropathy) = [Formula: see text]-surpassing the existing approaches. Conclusions: A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis.

EEG Database of Seizure Disorders for Experts and Application Developers.

This article presents an online accessible electroencephalogram (EEG) database, where the EEG recordings comprise abnormal patterns such as spikes, poly spikes, slow waves, and sharp waves to help diagnose related disorders. The data, as of now, are a collection of EEGs from a diagnostic center in Coimbatore, Tamil Nadu, India, and the data samples pertain to an age-group ranging from 1 to 107 years. Eventually, the EEG data concerning other disorders as well as those from other institutions will be included. The present database provides information under the following categories: major classification of the disorder, patient's record, digitized EEG, and specific diagnosis; in addition, a search facility is incorporated into the database. The mode of access by the domain experts, application developers, and researchers, along with a few classical applications are explained in this article. With the advance of clinical neuroscience, this database will be helpful in developing software for applications such as diagnosis and treatment.