Life space assessment and falls in older adults with multiple sclerosis.

BACKGROUND/OBJECTIVE: Falls research in older adults with MS (OAMS) is scarce, and no studies have reported on the association between life-space mobility and falls in this group. Herein, we hypothesized that higher baseline life-space scores would be associated with reduced odds of reporting falls during follow-up, and explored whether the association differed by MS subtype (progressive vs. relapsing-remitting). METHODS: OAMS (n = 91, mean age = 64.7 ± 4.3ys, %female = 66.9,%progressive MS = 30.7) completed the University of Alabama at Birmingham Life-Space-Assessment (UAB-LSA) scale and reported falls during a structured monthly telephone interview during follow-up (mean = 16.39 ± 11.44 months). General Estimated Equations (GEE) models were utilized to determine whether UAB-LSA scores predicted falls during follow-up. RESULTS: GEE models revealed that higher UAB-LSA scores were associated with a significant reduction in the odds of falling during follow-up (OR = 0.69, p = 0.012, 95 %CI = 0.51 to 0.92). Stratified analyses revealed that this association was significant in progressive (OR = 0.57, p = 0.004, 95 %CI = 0.39 to 0.84), but not relapsing-remitting (OR = 0.93, p = 0.779, 95 %CI = 0.57 to 1.53) MS. CONCLUSION: Higher life-space mobility was associated with lower odds of falling among OAMS with progressive subtype. The UAB-LSA may complement existing mobility measures for predicting fall risk.

Application of near-infrared spectroscopy to assess the effect of the cupping size on the spatial hemodynamic response from the area inside and outside the cup of the biceps.

Cupping therapy is a popular intervention for improving muscle recovery after exercise although clinical evidence is weak. Previous studies demonstrated that cupping therapy may improve microcirculation of the soft tissue to accelerate tissue healing. However, it is unclear whether the cupping size could affect the spatial hemodynamic response of the treated muscle. The objective of this study was to use 8-channel near-infrared spectroscopy to assess this clinical question by assessing the effect of 3 cupping sizes (35, 40, and 45 mm in inner diameter of the circular cup) under -300 mmHg for 5 min on the muscle hemodynamic response from the area inside and outside the cup, including oxyhemoglobin and deoxy-hemoglobin in 18 healthy adults. Two-way factorial design was used to assess the interaction between the cupping size (35, 40, and 45 mm) and the location (inside and outside the cup) and the main effects of the cupping size and the location. The two-way repeated measures ANOVA demonstrated an interaction between the cupping size and the location in deoxy-hemoglobin (P = 0.039) but no interaction in oxyhemoglobin (P = 0.100), and a main effect of the cup size (P = 0.001) and location (P = 0.023) factors in oxyhemoglobin. For the cupping size factor, the 45-mm cup resulted in a significant increase in oxyhemoglobin (5.738±0.760 µM) compared to the 40-mm (2.095±0.312 µM, P<0.001) and 35-mm (3.134±0.515 µM, P<0.01) cup. Our findings demonstrate that the cupping size and location factors affect the muscle hemodynamic response, and the use of multi-channel near-infrared spectroscopy may help understand benefits of cupping therapy on managing musculoskeletal impairment.

Brain control of dual-task walking can be improved in aging and neurological disease.

The peak prevalence of multiple sclerosis has shifted into older age groups, but co-occurring and possibly synergistic motoric and cognitive declines in this patient population are poorly understood. Dual-task-walking performance, subserved by the prefrontal cortex, and compromised in multiple sclerosis and aging, predicts health outcomes. Whether acute practice can improve dual-task walking performance and prefrontal cortex hemodynamic response efficiency in multiple sclerosis has not been reported. To address this gap in the literature, the current study examined task- and practice-related effects on dual-task-walking and associated brain activation in older adults with multiple sclerosis and controls. Multiple sclerosis (n = 94, mean age = 64.76 ± 4.19 years) and control (n = 104, mean age = 68.18 ± 7.01 years) participants were tested under three experimental conditions (dual-task-walk, single-task-walk, and single-task-alpha) administered over three repeated counterbalanced trials. Functional near-infrared-spectroscopy was used to evaluate task- and practice-related changes in prefrontal cortex oxygenated hemoglobin. Gait and cognitive performances declined, and prefrontal cortex oxygenated hemoglobin was higher in dual compared to both single task conditions in both groups. Gait and cognitive performances improved over trials in both groups. There were greater declines over trials in oxygenated hemoglobin in dual-task-walk compared to single-task-walk in both groups. Among controls, but not multiple sclerosis participants, declines over trials in oxygenated hemoglobin were greater in dual-task-walk compared to single-task-alpha. Dual-task walking and associated prefrontal cortex activation efficiency improved during a single session, but improvement in neural resource utilization, although significant, was attenuated in multiple sclerosis participants. These findings suggest encouraging brain adaptability in aging and neurological disease.

Initial validation of the university of Alabama Birmingham study of aging life-space assessment in older adults with multiple sclerosis.

BACKGROUND: Older adults with multiple sclerosis (OAMS) have declines in walking and physical performance that may erode community mobility defined as the spatial extent of mobility in one's daily life and environment. OBJECTIVE: This study provided the first application and validation of the University of Alabama Birmingham Study of Aging Life-Space Assessment (UAB LSA) as a measure of community mobility in OAMS. METHODS: The sample included 97 OAMS and 108 healthy controls (HCs) who completed baseline assessments as part of an ongoing, longitudinal study. The primary assessments included the UAB LSA and timed 25-foot walk (T25FW), short physical performance battery (SPPB), global health score (GHS), and geriatric depression scale (GDS) in both OAMS and HCs, and patient determined disease steps (PDDS) scale in only OAMS. RESULTS: OAMS had significantly lower UAB LSA scores than HCs (p < .001). UAB LSA scores had strong correlations with T25FW(rs = -.641) and SPPB(rs = 0.507) in OAMS, and moderate correlations in HCs (rs = -.300 & rs = 0.384). The correlations between UAB LSA and GHS and GDS scores were significant, but small in OAMS (rs = -.239 & rs = -.231), and not statistically significant in HCs (rs = -.009 & rs = -.166). There was a strong correlation between UAB LSA and PDDS scores in the OAMS sample (rs = -.605). CONCLUSION: We provided initial evidence for UAB LSA scores as a measure of community mobility in OAMS.

Classification of fall risk across the lifespan using gait derived features from a wearable device.

Falls are one of the leading factors of injury and fatality in older adults. Given the importance of early detection of adults at higher risk of falls, we evaluated the ability of machine learning to classify fall risk in adults across the lifespan using wearable sensors embedded in a smartshirt. We evaluated the classification performance of binary and multiclass fall risk classifier models using SciKit Digital Health in adults across the lifespan. Using a k-fold and group k-fold cross-validation strategy, we demonstrate the feasibility of fall risk classification using accelerometer data from 10 second epochs of treadmill walking data from adults across the lifespan. We achieved an 88% accuracy in a binary clasifier of fallers vs. non-fallers, and an 86% accuracy in a multiclass classifier comparing non-fallers, fallers, and recurrent fallers using retrospective fall histories. Comparing group k-fold vs. k-fold cross-validation strategies, we find a 22-27% drop-off in accuracy performance. Furthering the evaluation framework presented in this study would be valuable to the development of more robust and clinically relevant models used in the prediction of fall risk. These models could one day be applied in clinical settings to help better diagnose and monitor fall risk among older adults, improving the care of at-risk individuals and reducing the injury and associated cost of falls.

A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson's Disease.

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.

Using cross-correlation analysis of multi-channel near infrared spectroscopy to assess the hemodynamic response to cupping therapy.

Cupping therapy is a common intervention for the management of musculoskeletal impairment. Previous studies have demonstrated that cupping therapy can improve muscle hemodynamic responses using single-channel near-infrared spectroscopy (NIRS). However, the effects of cupping therapy on spatial hemodynamic responses as well as the correlation between oxyhemoglobin and deoxy-hemoglobin are largely unknown. The cross-correlation function (CCF) algorithm was used to determine the correlation between time-series NIRS signals from inside and outside the cup as well as time-series oxyhemoglobin and deoxy-hemoglobin under 4 cupping intensities, including -225 and -300 mmHg for 5 and 10 min. The main finding was that the maximum CCF values of oxyhemoglobin was significantly higher than those in deoxy-hemoglobin (p < 0.05). Furthermore, it was found that there was a correlation between deoxy-hemoglobin with a longer duration and a larger magnitude of negative pressure. This is the first study investigating time-series hemodynamic responses after cupping therapy using cross-correlation function analysis of multi-channel NIRS signals.

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait.

OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatric complexities. Mobility limitations are amongst the most frequent and early markers of MS. We evaluated the effectiveness of a DeepMS2G (deep learning (DL) for MS differentiation using multistride dynamics in gait) framework, which is a DL-based methodology to classify multi-stride sequences of persons with MS (PwMS) from healthy controls (HC), in order to generalize over newer walking tasks and subjects. METHODS: We collected single-task Walking and dual-task Walking-while-Talking gait data using an instrumented treadmill from a balanced collection of 20 HC and 20 PwMS. We utilized domain knowledge-based spatiotemporal and kinetic gait features along with two normalization schemes, namely standard size-based and multiple regression normalization strategies. To differentiate between multi-stride sequences of HC and PwMS, we compared 16 traditional machine learning and DL algorithms. Further, we studied the interpretability of our highest-performing models; and discussed the association between the lower extremity function of participants and our model predictions. RESULTS: We observed that residual neural network (ResNet) based models with regression-based normalization were the top performers across both task and subject generalization classification designs. Considering regression-based normalization, a multi-scale ResNet attained a subject classification accuracy and F 1-score of 1.0 when generalizing from single-task Walking to dual-task Walking-while-Talking; and a ResNet resulted in the top subject-wise accuracy and F 1 of 0.83 and 0.81 (resp.), when generalizing over unseen participants. CONCLUSION: We used advanced DL and dynamics across domain knowledge-based spatiotemporal and kinetic gait parameters to successfully classify MS gait across distinct walking trials and unseen participants. SIGNIFICANCE: Our proposed DL algorithms might contribute to efforts to automate MS diagnoses.

Beta cortical oscillatory activities and their relationship to postural control in a standing balance demanding test: influence of aging.

Background: Age-related changes in the cortical control of standing balance may provide a modifiable mechanism underlying falls in older adults. Thus, this study examined the cortical response to sensory and mechanical perturbations in older adults while standing and examined the relationship between cortical activation and postural control. Methods: A cohort of community dwelling young (18-30 years, N = 10) and older adults (65-85 years, N = 11) performed the sensory organization test (SOT), motor control test (MCT), and adaptation test (ADT) while high-density electroencephalography (EEG) and center of pressure (COP) data were recorded in this cross-sectional study. Linear mixed models examined cohort differences for cortical activities, using relative beta power, and postural control performance, while Spearman correlations were used to investigate the relationship between relative beta power and COP indices in each test. Results: Under sensory manipulation, older adults demonstrated significantly higher relative beta power at all postural control-related cortical areas (p < 0.01), while under rapid mechanical perturbations, older adults demonstrated significantly higher relative beta power at central areas (p < 0.05). As task difficulty increased, young adults had increased relative beta band power while older adults demonstrated decreased relative beta power (p < 0.01). During sensory manipulation with mild mechanical perturbations, specifically in eyes open conditions, higher relative beta power at the parietal area in young adults was associated with worse postural control performance (p < 0.001). Under rapid mechanical perturbations, specifically in novel conditions, higher relative beta power at the central area in older adults was associated with longer movement latency (p < 0.05). However, poor reliability measures of cortical activity assessments were found during MCT and ADT, which limits the ability to interpret the reported results. Discussion: Cortical areas are increasingly recruited to maintain upright postural control, even though cortical resources may be limited, in older adults. Considering the limitation regarding mechanical perturbation reliability, future studies should include a larger number of repeated mechanical perturbation trials.

Differential Associations of Mobility With Fronto-Striatal Integrity and Lesion Load in Older Adults With and Without Multiple Sclerosis.

BACKGROUND: Mobility impairment is common in older persons with multiple sclerosis (MS), and further compounded by general age-related mobility decline but its underlying brain substrates are poorly understood. OBJECTIVE: Examine fronto-striatal white matter (WM) integrity and lesion load as imaging correlates of mobility outcomes in older persons with and without MS. METHODS: Fifty-one older MS patients (age 64.9 ± 3.7 years, 29 women) and 50 healthy, matched controls (66.2 ± 3.2 years, 24 women), participated in the study, which included physical and cognitive test batteries and 3T MRI imaging session. Primary imaging measures were fractional anisotropy (FA) and WM lesion load. The relationship between mobility impairment, defined using a validated short physical performance battery cutoff score, and neuroimaging measures was assessed with stratified logistic regression models. FA was extracted from six fronto-striatal circuits (left/right): dorsal striatum (dStr)-to-anterior dorsolateral prefrontal cortex (aDLPFC), dStr-to-posterior DLPFC, and ventral striatum (vStr)-to-ventromedial prefrontal cortex (VMPFC). RESULTS: Mobility impairment was significantly associated with lower FA in two circuits, left dStr-aDLPFC (P = .003) and left vStr-VMPFC (P = .004), in healthy controls but not in MS patients (P > .20), for fully adjusted regression models. Conversely, in MS patients but not in healthy controls, mobility impairment was significantly associated with greater lesion volume (P < .02). CONCLUSIONS: Comparing older persons with and without MS, we provide compelling evidence of a double dissociation between the presence of mobility impairment and two neuroimaging markers of white matter integrity, fronto-striatal fractional anisotropy, and whole brain lesion load.

Using near-infrared spectroscopy to investigate the effects of pressures and durations of cupping therapy on muscle blood volume and oxygenation.

Cupping therapy has been widely used to manage musculoskeletal impairment. However, the effects of pressure and duration of cupping therapy on the hemodynamic activity of the muscle have not been investigated. A 2 × 2 repeated measures factorial design was used to examine the main effect and interaction of pressure (-225 and -300 mmHg) and duration (5 and 10 min) on biceps muscle blood flow using near-infrared spectroscopy in 18 participants. The results showed that a significant interaction is between pressure and duration on deoxy-hemoglobin (p = 0.045). A significant main effect of pressure is on oxyhemoglobin (p = 0.005) and a significant main effect of duration is on oxyhemoglobin (p = 0.005). Cupping therapy at -300 mmHg for 10 min results in a higher oxyhemoglobin (6.75 ± 2.08 µM) and deoxy-hemoglobin (1.71 ± 0.78 µM) compared to other three combinations. Our study provides first evidence that the pressure and duration factors of cupping therapy can significantly affect muscle blood volume and oxygenation.

Effects of Therapeutic Intervention on Spatiotemporal Gait Parameters in Adults With Neurologic Disorder: Systematic Review and Meta-analysis.

OBJECTIVE: This systematic review and meta-analysis aimed to review and quantify the changes in gait parameters after therapeutic intervention in adults with neurologic disorders. DATA SOURCES: A keyword search was performed in 4 databases: PubMed, CINAHL, Scopus, and Web of Science (01/2000-12/2021). We performed the search algorithm including all possible combinations of keywords. Full-text articles were examined further using forward/backward search methods. STUDY SELECTION: Studies were thoroughly screened using the following inclusion criteria: Study design: randomized controlled trial; adults ≥55 years old with a neurologic disorder; therapeutic intervention; spatiotemporal gait characteristics; and language: English. DATA EXTRACTION: A standardized data extraction form was used to collect the following methodological outcome variables from each of the included studies: author, year, population, age, sample size, and spatiotemporal gait parameters such as cadence, step length, step width, or double limb support. A meta-analysis was performed among trials presenting with similar characteristics, including study population and outcome measure. If heterogeneity was >50%, a random plot analysis was used; otherwise, a fixed plot analysis was done. DATA SYNTHESIS: We included 25 out of 34 studies in our meta-analysis that examined gait in adults with neurologic disorders. All analyses used effect sizes and standard error and a P<.05(denoted by *) threshold was considered statistically significant. Overall, we found that sensory (SS) and electrical stimulation (ES) had the most significant effect on step length (SS: z=5.44*, ES: z=2.42*) and gait speed (SS: z=6.19*, ES: z=7.38*) in adults with Parkinson disease (PD). Although balance or physical activity interventions were not found to be effective in modifying step length in adults with PD, they showed a significant effect on gait speed. Further, physical activity had the most significant effect on cadence in adults with PD (z=2.84*) relative to sensory stimulation effect on cadence (z=2.59*). For stroke, conventional physical therapy had the most significant effect on step length (z=3.12*) and cadence (z=3.57*). CONCLUSION: Sensory stimulation such as auditory and somatosensory stimulation while walking had the most significant effect on step length in adults with PD. We also found that conventional physical therapy did improve spatial gait parameters relative to other physical activity interventions in adults with PD and stroke.

A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions-A Deep Learning Approach.

This study examined the effectiveness of a vision-based framework for multiple sclerosis (MS) and Parkinson's disease (PD) gait dysfunction prediction. We collected gait video data from multi-view digital cameras during self-paced walking from MS, PD patients and age, weight, height and gender-matched healthy older adults (HOA). We then extracted characteristic 3D joint keypoints from the collected videos. In this work, we proposed a data-driven methodology to classify strides in persons with MS (PwMS), persons with PD (PwPD) and HOA that may generalize across different walking tasks and subjects. We presented a comprehensive quantitative comparison of 16 diverse traditional machine and deep learning (DL) algorithms. When generalizing from comfortable walking (W) to walking-while-talking (WT), multi-scale residual neural network achieved perfect accuracy and AUC for classifying individuals with a given gait disorder; for subject generalization in W trials, residual neural network resulted in the highest accuracy and AUC of 78.1% and 0.87 (resp.), and 1D convolutional neural network (CNN) had highest accuracy of 75% in WT trials. Finally, when generalizing over new subjects in different tasks, again 1D CNN had the top classification accuracy and AUC of 79.3% and 0.93 (resp.). This work is the first attempt to apply and demonstrate the potential of DL with a multi-view digital camera-based gait analysis framework for neurological gait dysfunction prediction. This study suggests the viability of inexpensive vision-based systems for diagnosing certain neurological disorders.

Online classifier of AMICA model to evaluate state anxiety while standing in virtual reality.

Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.

Effect of Treadmill Training Interventions on Spatiotemporal Gait Parameters in Older Adults with Neurological Disorders: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Objective: Treadmill interventions have been shown to promote 'normal' walking patterns, as they facilitate the proper movement and timing of the lower limbs. However, prior reviews have not examined which intervention provides the most effective treatment of specific gait impairments in neurological populations. The objective of this systematic review was to review and quantify the changes in gait after treadmill interventions in adults with neurological disorders. Data Sources: A keyword search was performed in four databases: PubMed, CINAHL, Scopus, and Web of Science (January 2000−December 2021). We performed the search algorithm including all possible combinations of keywords. Full-text articles were examined further using forward/backward search methods. Study Selection: Studies were thoroughly screened using the following inclusion criteria: study design: Randomized Controlled Trial (RCT); adults ≥55 years old with a neurological disorder; treadmill intervention; spatiotemporal gait characteristics; and language: English. Data Extraction: A standardized data extraction form was used to collect the following methodological outcome variables from each of the included studies: author, year, population, age, sample size, and spatiotemporal gait parameters including stride length, stride time, step length, step width, step time, stance time, swing time, single support time, double support time, or cadence. Data Synthesis: We found a total of 32 studies to be included in our systematic review through keyword search, out of which 19 studies included adults with stroke and 13 studies included adults with PD. We included 22 out of 32 studies in our meta-analysis that examined gait in adults with neurological disorders, which only yielded studies including Parkinson's disease (PD) and stroke patients. A meta-analysis was performed among trials presenting with similar characteristics, including study population and outcome measure. If heterogeneity was >50% (denoted by I2), random plot analysis was used, otherwise, a fixed plot analysis was performed. All analyses used effect sizes and standard errors and a p < 0.05 threshold was considered statistically significant (denoted by *). Overall, the effect of treadmill intervention on cadence (z = 6.24 *, I2 = 11.5%) and step length (z = 2.25 *, I2 = 74.3%) in adults with stroke was significant. We also found a significant effect of treadmill intervention on paretic step length (z = 2.34 *, I2 = 0%) and stride length (z = 6.09 *, I2 = 45.5%). For the active control group, including adults with PD, we found that overground physical therapy training had the largest effect on step width (z = −3.75 *, I2 = 0%). Additionally, for PD adults in treadmill intervention studies, we found the largest significant effect was on step length (z = 2.73 *, I2 = 74.2%) and stride length (z = −2.54 *, I2 = 96.8%). Conclusion: Treadmill intervention with sensory stimulation and body weight support treadmill training were shown to have the largest effect on step length in adults with PD and stroke.

Subthreshold Vibration Influences Standing Balance but Has Unclear Impact on Somatosensation in Persons With Transtibial Amputations.

Stochastic resonance has been successfully used to improve human movement when using subthreshold vibration. Recent work has shown promise in improving mobility in individuals with unilateral lower limb amputations. Furthering this work, we present an investigation of two different signal structures in the use of stochastic resonance to improve mobility in individuals with unilateral lower limb amputations. Cutaneous somatosensation and standing balance measures using spatial and temporal analysis were assessed. There were no differences in the somatosensation measures, but differences in the temporal characteristics of the standing measures were seen with the various vibration structures when compared to no vibration, one of which suggesting mass may play an important role in determining who may or may not benefit from this intervention. Stochastic resonance employed with subthreshold vibration influences mobility in individuals with unilateral amputations, but the full direction and extent of influence is yet to be understood.

Feasibility of VR Technology in Eliciting State Anxiety Changes While Walking in Older Women.

Virtual reality (VR) technology offers an exciting way to emulate real-life walking conditions that may better elicit changes in emotional state. We aimed to determine whether VR technology is a feasible way to elicit changes in state anxiety during walking. Electrocardiogram data were collected for 18 older adult women while they navigated a baseline walking task, a dual walking task, and four walking VR environments. Using heart rate variability (HRV) analysis, we found that all four of the VR environments successfully elicited a significantly higher level of state anxiety as compared to the walking baseline, with 84% of participants eliciting a significantly lower HRV in each of the four VR conditions as compared to baseline walking. VR was also found to be a more reliable tool for increasing state anxiety as compared to a dual task, where only 47% of participants demonstrated a significantly lower HRV as compared to baseline walking. VR, therefore, could be promising as a tool to elicit changes in state anxiety and less limited in its ability to elicit changes as compared to a traditional dual task condition.

The neural underpinnings of motor learning in people with neurodegenerative diseases: A scoping review.

Chronic progressive neurodegenerative diseases (NDD) cause mobility and cognitive impairments that disrupt quality of life. The learning of new motor skills, motor learning, is a critical component of rehabilitation efforts to counteract these chronic progressive impairments. In people with NDD, there are impairments in motor learning which appear to scale with the severity of impairment. Compensatory cortical activity plays a role in counteracting motor learning impairments in NDD. Yet, the functional and structural brain alterations associated with motor learning have not been synthesized in people with NDD. The purpose of this scoping review is to explore the neural alterations of motor learning in NDD. Thirty-five peer-reviewed original articles met the inclusion criteria. Participant demographics, motor learning results, and brain imaging results were extracted. Distinct motor learning associated compensatory processes were identified across NDD populations. Evidence from this review suggests the success of motor learning in NDD populations depends on the neural alterations and their interaction with motor learning networks, as well as the progression of disease.

Brain Activation Changes While Walking in Adults with and without Neurological Disease: Systematic Review and Meta-Analysis of Functional Near-Infrared Spectroscopy Studies.

(1) Functional near-infrared spectroscopy (fNIRS) provides a useful tool for monitoring brain activation changes while walking in adults with neurological disorders. When combined with dual task walking paradigms, fNIRS allows for changes in brain activation to be monitored when individuals concurrently attend to multiple tasks. However, differences in dual task paradigms, baseline, and coverage of cortical areas, presents uncertainty in the interpretation of the overarching findings. (2) Methods: By conducting a systematic review of 35 studies and meta-analysis of 75 effect sizes from 17 studies on adults with or without neurological disorders, we show that the performance of obstacle walking, serial subtraction and letter generation tasks while walking result in significant increases in brain activation in the prefrontal cortex relative to standing or walking baselines. (3) Results: Overall, we find that letter generation tasks have the largest brain activation effect sizes relative to walking, and that significant differences between dual task and single task gait are seen in persons with multiple sclerosis and stroke. (4) Conclusions: Older adults with neurological disease generally showed increased brain activation suggesting use of more attentional resources during dual task walking, which could lead to increased fall risk and mobility impairments. PROSPERO ID: 235228.

Benefits of tai ji quan practice on neuromuscular functions in older adults: A Systematic Review and meta-analysis.

BACKGROUND AND PURPOSE: Tai Ji Quan (TJQ) practice has been recommended for reducing falls in older adults, but a gap exists in our understanding of the neuromuscular mechanisms underlying TJQ practice benefits. This study aims to quantify and validate neuromuscular mechanisms underlying TJQ practice benefits in older adults. MATERIALS AND METHODS: This review and analysis followed the PRISMA framework. All meta-analyses were performed in R. RESULTS: For healthy older adults, TJQ practice was found to decrease muscle onset latency. Higher leg muscle activations were found during TJQ gait in comparison to normal gait. A significant interaction between TJQ practice time and age of the cohort was observed in muscle onset latency. For adults with pre-existing health conditions, TJQ practice has similar neuromuscular benefits as conventional rehabilitation methods. CONCLUSION: Neuromuscular function improvements associated with TJQ practice provide a mechanism for reducing falls in older adults with and without pre-existing health conditions.