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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Effect of Heart Rate Reserve on Prefrontal Cortical Activation While Dual-Task Walking in Older Adults

Hypertension is considered a risk factor for cardiovascular health and non-amnestic cognitive impairment in older adults. While heart rate reserve (HRR) has been shown to be a risk factor for hypertension, how impaired HRR in older adults can lead to cognitive impairment is still unclear. The objective of this study was to examine the effects of HRR on prefrontal cortical (PFC) activation under varying dual-task demands in older adults. Twenty-eight older adults (50-82 years of age) were included in this study and divided into higher (n = 14) and lower (n = 14) HRR groups. Participants engaged in the cognitive task which was the Modified Stroop Color Word Test (MSCWT) on a self-paced treadmill while walking. Participants with higher HRR demonstrated increased PFC activation in comparison to lower HRR, even after controlling for covariates in analysis. Furthermore, as cognitive task difficulty increased (from neutral to congruent to incongruent to switching), PFC activation increased. In addition, there was a significant interaction between tasks and HRR group, with older adults with higher HRR demonstrating increases in PFC activation, faster gait speed, and increased accuracy, relative to those with lower HRR, when going from neutral to switching tasks. These results provide evidence of a relationship between HRR and prefrontal cortical activation and cognitive and physical performance, suggesting that HRR may serve as a biomarker for cognitive health of an older adult with or without cardiovascular risk.

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.

Dual task walking costs in older adults with mild cognitive impairment: a systematic review and meta-analysis

OBJECTIVE

The objective of this systematic review and meta-analysis (PROSPERO registration No CRD42020192121) is to review existing literature focusing on effects of different dual task paradigms on walking speed in older adults with and without Mild Cognitive Impairment.

METHODS

(1) Data Sources: PubMEd, Cumulative Index of Nursing and Allied Health, Cochrane library, and Web of Science. (2) Study Selection: The key terms searched included those associated with dual task, walking speed, executive function, older adults, and MCI. (3) Data Extraction: The search yielded 140 results with 20 studies meeting the inclusion criteria, which were rated by two independent reviewers using the Quality Assessment Tool. Descriptions of each study including the single and dual task protocol, outcome measure, and final outcomes were extracted. Meta-analysis was performed to evaluate the dual task effects on walking costs in older adults with and without MCI.

RESULTS

Meta-analysis revealed that there were significant differences in the dual task walking costs among older adults with or without MCI (p < .05). Pooled effect sizes of the serial subtraction (9.54; 95%CI, 3.93-15.15) and verbal fluency tasks (10.06; 95%CI, 6.26-15.65) showed that there are higher motor dual-task costs in older adults with MCI than age-matched controls. For quality assessment, all studies ranged from 12 to 16 in score, out of 18 (high quality).

CONCLUSIONS

In the studies included in this review, mental tracking tasks, consisting of serial subtraction and verbal fluency, were found to be the most sensitive in detecting MCI-related changes in older adults, and could serve an important role as a target measure for evaluating the efficacy of interventions aimed at improving cognitive and motor function in older adults.

Predicting Multiple Sclerosis From Gait Dynamics Using an Instrumented Treadmill: A Machine Learning Approach

OBJECTIVE

Multiple Sclerosis (MS) is a neurological condition which widely affects people 50-60 years of age. While clinical presentations of MS are highly heterogeneous, mobility limitations are one of the most frequent symptoms. This study examines a machine learning (ML) framework for identifying MS through spatiotemporal and kinetic gait features.

METHODS

In this study, gait data during self-paced walking on an instrumented treadmill from 20 persons with MS and 20 age, weight, height, and gender-matched healthy older adults (HOA) were obtained. We explored two strategies to normalize data and minimize dependence on subject demographics; size-normalization (standard body size-based normalization) and regress-normalization (regression-based normalization using scaling factors derived by regressing gait features on multiple subject demographics); and proposed an ML based methodology to classify individual strides of older persons with MS (PwMS) from healthy controls. We generalized both across different walking tasks and subjects.

RESULTS

We observed that regress-normalization improved the accuracy of identifying pathological gait using ML when compared to size-normalization. When generalizing from comfortable walking to walking while talking, gradient boosting machine achieved the optimal subject classification accuracy and AUC of 94.3 and 1.0, respectively and for subject generalization, a multilayer perceptron resulted in the best accuracy and AUC of 80% and 0.86, respectively, both with regression-normalized data.

CONCLUSION

The integration of gait data and ML may provide a viable patient-centric approach to aid clinicians in monitoring MS.

SIGNIFICANCE

The results of this study have future implications for the way regression normalized gait features may be clinically used to design ML-based disease prediction strategies and monitor disease progression in PwMS.

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.

Effects of aerobic fitness on cognitive motor interference during self-paced treadmill walking in older adults

BACKGROUND

Older adults experience greater cognitive motor interference (CMI) due to declines in cognitive and physical function. Although aerobic fitness has beneficial effects on cognition, its association with CMI is not clear.

AIMS

This study aims to investigate the effects of aerobic fitness on CMI during self-paced treadmill walking in older adults.

METHODS

Thirty participants (67.6 ± 10.34 years, 21 females) were included in a 2-day cross-sectional design study. Aerobic fitness was assessed with the Rockport 1-mile test. The dual-task paradigm consisted of walking only, and dual-task standing and dual-task walking (i.e., standing/walking while performing the Modified Stroop color word test) on a treadmill. To assess CMI, gait speed and accuracy rate were measured to later calculate the dual-task cost for each parameter.

RESULTS

Individuals with low aerobic fitness exhibited significantly greater gait speed dual-task cost than individuals with high aerobic fitness (p < 0.05). There were no significant findings for accuracy rate dual-task cost.

DISCUSSION

These study findings are the first to demonstrate increases in CMI in relation to low aerobic fitness. Results can be attributed to the relationship between aerobic fitness and cognition as well as theories related to attentional capacity.

CONCLUSION

Older adults with low aerobic fitness possess greater CMI when compared to older adults with high aerobic fitness. This provides a foundation of knowledge on how aerobic fitness in older adults may affect CMI which can lead researchers to examine the causal relationships between an aerobic exercise intervention program and CMI in older adults.

Exploration of Machine Learning to Identify Community Dwelling Older Adults with Balance Dysfunction Using Short Duration Accelerometer Data

The incidence of fall-related injuries in older adults is high. Given the significant and adverse outcomes that arise from injurious falls in older adults, it is of the utmost importance to identify older adults at greater risk for falls as early as possible. Given that balance dysfunction provides a significant risk factor for falls, an automated and objective identification of balance dysfunction in community dwelling older adults using wearable sensor data when walking may be beneficial. In this study, we examine the feasibility of using wearable sensors, when walking, to identify older adults who have trouble with balance at an early stage using state-of-the-art machine learning techniques. We recruited 21 community dwelling older women. The experimental paradigm consisted of two tasks: Normal walking with a self-selected comfortable speed on an instrumented treadmill and a test of reflexive postural response, using the motor control test (MCT). Based on the MCT, identification of older women with low or high balance function was performed. Using short duration accelerometer data from sensors placed on the knee and hip while walking, supervised machine learning was carried out to classify subjects with low and high balance function. Using a Gradient Boosting Machine (GBM) algorithm, we classified balance function in older adults using 60 seconds of accelerometer data with an average cross validation accuracy of 91.5% and area under the receiver operating characteristic curve (AUC) of 0.97. Early diagnosis of balance dysfunction in community dwelling older adults through the use of user friendly and inexpensive wearable sensors may help in reducing future fall risk in older adults through earlier interventions and treatments, and thereby significantly reduce associated healthcare costs.

Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach

Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to the exploration of real-time brain computer interface (BCI) applications. EEG analysis demands substantial training and time for removal of distinct unwanted independent components (ICs), generated via independent component analysis, corresponding to artifacts. The considerable subject-wise variations across these components motivates defining a procedural way to identify and eliminate these artifacts. We propose DeepIC-virtual, a convolutional neural network (CNN) deep learning classifier to automatically identify brain components in the ICs extracted from the subject's EEG data gathered while they are being immersed in a virtual reality (VR) environment. This work examined the feasibility of DL techniques to provide automated ICs classification on noisy and visually engaging upright stance EEG data. We collected the EEG data for six subjects while they were standing upright in a VR testing setup simulating pseudo-randomized variations in height and depth conditions and induced perturbations. An extensive 1432 IC representation images data set was generated and manually labelled via an expert as brain components or one of the six distinct removable artifacts. The supervised CNN architecture was utilized to categorize good brain ICs and bad artifactual ICs via generated images of topographical maps. Our model categorizing good versus bad IC topographical maps resulted in a binary classification accuracy and area under curve of 89.20% and 0.93 respectively. Despite significant imbalance, only 1 out of the 57 present brain ICs in the withheld testing set was miss-classified as an artifact. These results will hopefully encourage clinicians to integrate BCI methods and neurofeedback to control anxiety and provide a treatment of acrophobia, given the viability of automatic classification of artifactual ICs.

Dataset of quantitative structured office measurements of movements in the extremities

A low-cost quantitative structured office measurement of movements in the extremities of people with Parkinson's disease [1,2] was performed on people with Parkinson's disease, multiple system atrophy, and age-matched healthy volunteers. Participants underwent twelve videotaped procedures rated by a trained examiner while connected to four accelerometers [1,2] generating a trace of the three location dimensions expressed as spreadsheets [3,4]. The signals of the five repetitive motion items [1,2] underwent processing to fast Fourier [5] and continuous wavelet transforms [6]. The dataset [7] includes the coding form with scores of the live ratings [1,2], the raw files [3], the converted spreadsheets [4], and the fast Fourier [5] and continuous wavelet transforms [6]. All files are unfiltered. The data also provide findings suitable to compare and contrast with data obtained by investigators applying the same procedure to other populations. Since this is an inexpensive procedure to quantitatively measure motions in Parkinson's disease and other movement disorders, this will be a valuable resource to colleagues, particularly in underdeveloped regions with limited budgets. The dataset will serve as a template for other investigations to develop novel techniques to facilitate the diagnosis, monitoring, and treatment of Parkinson's disease, other movement disorders, and other nervous and mental conditions. The procedure will provide the basis to obtain objective quantitative measurements of participants in clinical trials of new agents.

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings

BACKGROUND

Unsupervised home exercise is a major component of physical therapy (PT). This study proposes an inexpensive, inertial measurement unit-based wearable device to capture kinematic data to facilitate exercise. However, conveying and interpreting kinematic data to non-experts poses a challenge due to the complexity and background knowledge required that most patients lack.

OBJECTIVES

The objectives of this study were to identify key user interface and user experience features that would likely improve device adoption and assess participant receptiveness toward the device.

METHODS

Fifty participants were recruited to perform nine upper extremity exercises while wearing the device. Prior to exercise, participants completed an orientation of the device, which included examples of software graphics with exercise data. Surveys that measured receptiveness toward the device, software graphics, and ergonomics were given before and after exercise.

RESULTS

Participants were highly receptive to the device with 90% of the participants likely to use the device during PT. Participants understood how the simple kinematic data could be used to aid exercise, but the data could be difficult to comprehend with more complex movements. Devices should incorporate wireless sensors and emphasize ease of wear.

CONCLUSION

Device-guided home physical rehabilitation can allow for individualized treatment protocols and improve exercise self-efficacy through kinematic analysis. Future studies should implement clinical testing to evaluate the impact a wearable device can have on rehabilitation outcomes.

Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data

The amount of home-based exercise prescribed by a physical therapist is difficult to monitor. However, the integration of wearable inertial measurement unit (IMU) devices can aid in monitoring home exercise by analyzing exercise biomechanics. The objective of this study is to evaluate machine learning models for classifying nine different upper extremity exercises, based upon kinematic data captured from an IMU-based device. Fifty participants performed one compound and eight isolation exercises with their right arm. Each exercise was performed ten times for a total of 4500 trials. Joint angles were calculated using IMUs that were placed on the hand, forearm, upper arm, and torso. Various machine learning models were developed with different algorithms and train-test splits. Random forest models with flattened kinematic data as a feature had the greatest accuracy (98.6%). Using triaxial joint range of motion as the feature set resulted in decreased accuracy (91.9%) with faster speeds. Accuracy did not decrease below 90% until training size was decreased to 5% from 50%. Accuracy decreased (88.7%) when splitting data by participant. Upper extremity exercises can be classified accurately using kinematic data from a wearable IMU device. A random forest classification model was developed that quickly and accurately classified exercises. Sampling frequency and lower training splits had a modest effect on performance. When the data were split by subject stratification, larger training sizes were required for acceptable algorithm performance. These findings set the basis for more objective and accurate measurements of home-based exercise using emerging healthcare technologies.

Characterization and Control of Dynamic Rearrangement in a Self-Assembled Antibody Carrier

Thorough characterization of protein assemblies is required for the control of structure and robust performance in any given application, especially for the safety and stability of protein therapeutics. Here, we report the use of multiple, orthogonal characterization techniques to enable control over the structure of a multivalent antibody carrier for future use in drug delivery applications. The carrier, known as Hex, contains six antibody binding domains that bind the Fc region of antibodies. Using size exclusion chromatography, analytical ultracentrifugation, and dynamic light scattering, we identified the stoichiometry of assembled Hex-antibody complexes and observed changes in the stoichiometry of nanocarriers when incubated at higher temperatures over time. The characterization data informed the modification of Hex to achieve tighter control over the protein assembly structure for future therapeutic applications. This work demonstrates the importance of using orthogonal characterization techniques and observing protein assembly in different conditions over time to fully understand and control structure and dynamics.

Age-related differences to neck muscle activation latency as a potential risk factor to fall-related traumatic brain injuries

This investigation examined age-related differences in neck muscle activation latency in response to anterior and posterior postural perturbations to understand the potential implications in fall-related traumatic brain injuries. 57 adults were recruited and categorized into 3 groups based on age: Young (18-30 years old), Young-Old (60-74 years) and Old-Old (75-89 years) group. Study participants underwent six anterior and posterior postural perturbations while bilateral sternocleidomastoid, upper trapezius, and splenius capitis electromyography was collected. Muscle activation latency time was calculated with established procedures. During anterior translations, a significant group effect for muscle activation latency of the right SCM (F(2,43) = 8.786, p < 0.001), right (F(2,34) = 4.838, p = 0.014) and left (F(2,34) = 5.015, p = 0.012) upper trapezius, and right (F(2,45) = 3.195, p = 0.050) and left (F(2,45) = 3.819, p = 0.029) splenius capitis was observed. During posterior translations, a significant group effect for muscle activation latency was observed in the right (F(2,34) = 6.419, p = 0.004) and left (F(2,41) = 5.275, p = 0.009) SCM, and the right (F(2,34) = 4.925, p = 0.013) and left (F(2,32) = 4.055, p = 0.027) upper trapezius. Both older groups displayed longer muscle activation latencies than the young group. The age-related differences in neck muscle activation latency may be placing older adults at a greater risk of fall-related traumatic brain injuries.

Using Virtual Reality to Examine the Neural and Physiological Responses to Height and Perturbations in Quiet Standing

We describe an experimental setup, which uses virtual reality to understand neural responses to height and perturbations in human postural control. This system could help clinicians develop better methods to alleviate symptoms from a significant fear of heights, especially in the elderly and those with movement disorders, such as Parkinson's disease. In our design, EEG and EKG systems monitor the participants' neural responses and heart activities respectively, while they try to maintain balance on a force plate in an induced virtual world, experiencing randomized height changes and perturbations. These responses are then analyzed to understand the participants' anxiety caused by height and postural challenges.

Brain Activation Changes During Balance- and Attention-Demanding Tasks in Middle- and Older-Aged Adults With Multiple Sclerosis

Functional near-infrared spectroscopy was used to evaluate prefrontal cortex activation differences between older adults with multiple sclerosis (MS) and healthy older adults (HOA) during the performance of a balance- and attention-demanding motor task. Ten older adults with MS and 12 HOA underwent functional near-infrared spectroscopy recording while talking, virtual beam walking, or virtual beam walking while talking on a self-paced treadmill. The MS group demonstrated smaller increases in prefrontal cortex oxygenation levels than HOA during virtual beam walking while talking than talking tasks. These findings indicate a decreased ability to allocate additional attentional resources in challenging walking conditions among MS compared with HOA. This study is the first to investigate brain activation dynamics during the performance of balance- and attention-demanding motor tasks in persons with MS.

Exploring Characteristic Features in Gait Patterns for Predicting Multiple Sclerosis

Multiple Sclerosis (MS), an autoimmune and demyelinating disease, is one the most prevalent neurological disabilities in young adults. It results in damage of the central nervous system, disrupting communication between the patient's brain, spinal cord and body. Mobility limitations is one of the earliest symptoms and affects a majority of persons with Multiple Sclerosis. We are working towards an effort to characterize individuals with MS, from those without, on the basis of variations in the gait patterns. In the proposed work, statistical methods were used to identify differentiating gait data features for MS characterization. The prediction algorithms built upon these characteristic features will help clinicians develop effective and early cure and therapy designs for persons with Multiple Sclerosis.

Physical Function in Older Adults With Multiple Sclerosis: An Application of the Short Physical Performance Battery

BACKGROUND AND PURPOSE

There is a growing prevalence of older persons living with multiple sclerosis (MS), and this cohort likely undergoes changes in physical function associated with MS and its progression as well as those associated with normal aging. This cross-sectional study examined physical function in a community-dwelling sample of older adults with MS compared with matched controls using the Short Physical Performance Battery (SPPB).

METHODS

The sample (N = 40) included 20 older adults with MS and 20 older adults without MS who were matched on sex and age. All participants completed the SPPB.

RESULTS

Statistically significant differences were observed between groups for the overall SPPB score (P = .013; d = 0.45) and the balance (P = .002; d = 0.46) and gait speed (P = .009; d = 0.30) component scores. The difference between groups in the lower extremity strength component approached significance (P = .056; d = 0.34). Of note, only 2 older adults without MS had SPPB scores below 10 (ie, 10%), whereas 8 older adults with MS had SPPB scores below 10 (ie, 40%); this represented a statistically significant difference in future risk for disability (P = .028).

DISCUSSION/CONCLUSIONS

We provide preliminary evidence for reduced physical function based on the SPPB as a valid, objective measure of lower extremity functional performance among older adults with MS.

Neuromechanical Simulation of Hand Pronation and Supination Task in Parkinson's disease

Parkinson's disease is a prevalent and debilitating neurological disorder, where the severity of motor symptoms are frequently monitored using clinical tests that include a hand pronation and supination task. Objective quantification of motor symptoms in persons with Parkinson's disease and detection of dopamine-induced dyskinesias during treatment is important for the management of the most common symptoms in persons with Parkinson's disease. Thus, the development of a neuromechanical model of rhythmic hand pronation and supination may further our understanding of the mechanisms underlying motor symptoms during rhythmic upper extremity tasks in persons with Parkinson's disease. The aim of this study was to create a model for a rhythmic hand pronation and supination task. This was done to create a simulation of a popular diagnostic task used in determining the severity of motor impairments in persons with Parkinson's disease. It is imperative to understand the neural dynamics as well as the physiological constraints placed on a system such as this in both the creation of a usable model as well as understanding the neuromechanical interactions occurring during this diagnostic task. This model of either normal or slowed, clinical behavior, can then serve as a springboard for the creation of models that characterize disordered motor movement and perhaps even the creation of models that could be incorporated into the diagnostic process.

Stress during puberty facilitates precancerous prostate lesions in adult rats

Puberty can be a critical period for the long-term development of diseases, especially for stress-related disorders that depend on neuroendocrine and immune responses. Some organs like the prostate are prone to diseases that result from neuroendocrine or immune challenges, such as cancer.

AIM

In the present study, we assessed the long-term effects of an acute pubertal stressor (immune-challenge) on the development of precancerous lesions in adult rats, and compared them with testosterone-induced prostatic lesions.

MATERIALS AND METHODS

Pubertal male rats received a single injection of lipopolysaccharide (LPS) or saline during puberty (5 weeks old). At adulthood (8 weeks old) males were subcutaneously implanted with either an empty capsule or filled with testosterone propionate (100 mg/kg). This resulted in a total of five groups: 1) intact untreated, 2) saline-treated and implanted with a blank capsule, 3) saline-treated and implanted with a testosterone capsule, 4) LPS-treated and implanted with a blank capsule, 5) LPS-treated and implanted with a testosterone capsule. Four weeks later, the rats were sacrified and their prostates processed for histology (hematoxylin and eosin stain) and blood serum processed for hormone analysis (testosterone and corticosterone).

RESULTS

Males treated with LPS (stressed during puberty via immune challenge) expressed epithelium dysplasia (specially in the ventral prostate), anisocytosis, presence of mononuclear cells, anisokariosis, non-basal polarity, abnormal nucleus-cytoplasm ratio, proplastic myoepithelium, and granular content in the lumen. These histological alterations were similar, but less severe than those observed in males implanted with testosterone during adulthood.

CONCLUSION

These results indicate that pubertal exposure to an immune challenge (stress) facilitates the long-term development of prostatic lesions in adult male rats.

Frontal brain activation changes due to dual-tasking under partial body weight support conditions in older adults with multiple sclerosis

Background

Gait impairments present while dual-tasking in older adults with multiple sclerosis (MS) have been associated with an increased risk of falls. Prior studies have examined prefrontal cortex (PFC) activity using functional near infrared spectroscopy (fNIRS) while dual-tasking in older adults with and without cognitive impairment. While the benefits of partial body weight support (PBWS) on gait have been clearly outlined in the literature, the potential use of PBWS to improve the ability to dual task in older adults with and without MS has not been examined. The aim of this study was to examine the effects of PBWS on the PFC activation while dual-tasking in older adults with and without MS.

Methods

Ten individuals with MS (mean 56.2 ± 5.1 yrs., 8 females) and 12 healthy older adults (HOA) (mean 63.1 ± 4.4 yrs., 9 females) participated in this study. PFC activation (i.e., oxygenated hemoglobin-HbO₂) was measured using fNIRS. Assessments were done under two treadmill walking conditions: no body weight support (NBWS) and PBWS. Under each condition, participants were asked to walk at a comfortable speed (W) or walk and talk (WT). Linear mixed models were used to test for differences between cohorts, conditions, and tasks.

Results

HbO₂ levels differed significantly between task (p < .001), cohort (p < .001), and BWS (p = .02). HbO₂ levels increased under higher cognitive demands (i.e., W vs WT), in individuals with MS, and under different conditions (i.e., NBWS vs PBWS). Post-hoc analysis demonstrated a significant difference between cohorts during the WT and NBWS condition (p = .05). When examining the relative change in HbO₂ levels during each task, a significant interaction between task, BWS, and cohort across time was observed (p < 0.01). While HOA increased PFC activation across time, MS exhibited a maintenance of PFC activation patterns during the WT under PBWS condition.

Conclusions

This study establishes the potential impact of PBWS on PFC activation patterns under dual-tasking conditions and sheds light on the ability for PBWS to be used as a therapeutic tool in individuals with neurological conditions to decrease cognitive demands while dual-tasking and thus decrease the risk of falls.

Virtual reality applications in assessing the effect of anxiety on sensorimotor integration in human postural control

Falls are a leading cause of injury and mortality among adults over the age of 65 years. Given the strong relation between fear of falling and fall risk, identification of the mechanisms that underlie anxiety-related changes in postural control may pave the way to the development of novel therapeutic strategies aimed at reducing fall risk in older adults. First, we review potential mechanisms underlying anxiety-mediated changes in postural control in older adults with and without neurological conditions. We then present a system that allows for the simultaneous recording of neural, physiological, and behavioral data in an immersive virtual reality (VR) environment while implementing sensory and mechanical perturbations to evaluate alterations in sensorimotor integration under conditions with high postural threat. We also discuss applications of VR in minimizing falls in older adults and potential future studies.

Gait variability in people with neurological disorders: A systematic review and meta-analysis

There has been growing evidence showing gait variability provides unique information about gait characteristics in neurological disorders. This study systemically reviewed and quantitatively synthesized (via meta-analysis) existing evidence on gait variability in various neurological diseases, including Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), cerebellar ataxia (CA), Huntington's disease (HD), multiple sclerosis (MS), and Parkinson's disease (PD). Keyword search were conducted in PubMed, Web of science, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Library. Meta-analysis was performed to estimate the pooled effect size for gait variability for each neurological group. Meta-regression was performed to compare gait variability across multiple groups with neurological diseases. Gait variability of 777 patients with AD, ALS, CA, HD, MS, or PD participating in 25 studies was included in meta-analysis. All pathological groups had increased amount of gait variability and loss of fractal structure of gait dynamics compared to healthy controls, and gait variability differentiated distinctive neurological conditions. The HD groups had the highest alterations in gait variability among all pathological groups, whereas the PD, AD and MS groups had the lowest. Interventions that aim to improve gait function in patients with neurological disorders should consider the heterogeneous relationship between gait variability and neurological conditions.

A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics

The inability to maintain balance during varying postural control conditions can lead to falls, a significant cause of mortality and serious injury among older adults. However, our understanding of the underlying dynamical and stochastic processes in human postural control have not been fully explored. To further our understanding of the underlying dynamical processes, we examine a novel conceptual framework for studying human postural control using the center of pressure (COP) velocity autocorrelation function (COP-VAF) and compare its results to Stabilogram Diffusion Analysis (SDA). Eleven healthy young participants were studied under quiet unipedal or bipedal standing conditions with eyes either opened or closed. COP trajectories were analyzed using both the traditional posturographic measure SDA and the proposed COP-VAF. It is shown that the COP-VAF leads to repeatable, physiologically meaningful measures that distinguish postural control differences in unipedal versus bipedal stance trials with and without vision in healthy individuals. More specifically, both a unipedal stance and lack of visual feedback increased initial values of the COP-VAF, magnitude of the first minimum, and diffusion coefficient, particularly in contrast to bipedal stance trials with open eyes. Use of a stochastic postural control model, based on an Ornstein-Uhlenbeck process that accounts for natural weight-shifts, suggests an increase in spring constant and decreased damping coefficient when fitted to experimental data. This work suggests that we can further extend our understanding of the underlying mechanisms behind postural control in quiet stance under varying stance conditions using the COP-VAF and provides a tool for quantifying future neurorehabilitative interventions.

Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals

Parkinson's disease (PD) is known to lead to marked alterations in cortical-basal ganglia activity that may be amenable to serve as a biomarker for PD diagnosis. Using non-linear delay differential equations (DDE) for classification of PD patients on and off dopaminergic therapy (PD-on, PD-off, respectively) from healthy age-matched controls (CO), we show that 1 second of quasi-resting state clean and raw electroencephalogram (EEG) data can be used to classify CO from PD-on/off based on the area under the receiver operating characteristic curve (AROC). Raw EEG is shown to classify more robustly (AROC=0.59-0.86) than clean EEG data (AROC=0.57-0.72). Decomposition of the raw data into stereotypical and non-stereotypical artifacts provides evidence that increased classification of raw EEG time series originates from muscle artifacts. Thus, non-linear feature extraction and classification of raw EEG data in a low dimensional feature space is a potential biomarker for Parkinson's disease.

Delay differential analysis of electroencephalographic data

We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.

Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals

The pathophysiology of Parkinson’s disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve A′. In a companion paper, we show that DDEs can uncover hidden dynamical structure from short segments of simulated time series of known dynamical systems in high noise regimes. Using the same method for finding the best models, we found here that even short segments of EEG data in PD patients and controls contained dynamical structure, and moreover, that PD patients exhibited a greater dynamic range than controls. DDE model output on the means from one set of 50 trials provided nearly complete separation of PD patients off medication from controls: across brain regions, the area under the receiver-operating characteristic curves, A′, varied from 0.95 to 1.0. For distinguishing PD patients on vs. off medication, classification performance A′ ranged from 0.86 to 1.0 across brain regions. Moreover, the generalizability of the model to the second set of 50 trials was excellent, with A′ ranging from 0.81 to 0.94 across brain regions for controls vs. PD off medication, and from 0.62 to 0.82 for PD on medication vs. off. Finally, model features significantly predicted individual patients’ motor severity, as assessed with standard clinical rating scales.

Non-linear dynamical classification of short time series of the rössler system in high noise regimes

Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson’s disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to −30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A′ under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

Age-related differences in maintenance of balance during forward reach to the floor

BACKGROUND

Downward reaching may lead to falls in older adults, but the underlying mechanisms are poorly understood. This study assessed differences between younger and older adults in postural control and losses of balance when performing a forward reach to the floor in 2 possible real-world situations, with and without full foot contact with the floor.

METHODS

Healthy younger (n = 13) and older (n = 12) women reached as fast as possible to a target placed at their maximal forward reaching distance on floor, either standing on their whole foot or on the shortest base of support (BOS) that they were willing to perform a toe touch with.

RESULTS

Compared with younger women, older women used a 50% larger BOS when stooping down to touch their toes and had 22% less maximal forward reaching distance on the floor. Older women were twice as likely to lose their balance as younger women while performing a rapid forward floor reach (χ(2)(2) = 3.9; p < .05; relative risk = 1.91; 95% CI = 0.99-3.72). Postural sway, measured as center of pressure excursions and center of pressure root mean square error, did not differ between younger and older women anteriorly, but posteriorly, older women decreased their sway in full foot BOS and increased their sway in forefoot BOS (Age × BOS, p < .05). Leg strength was reduced in older versus younger women and was correlated with maximal reach distance (r = .65-.71).

CONCLUSIONS

Healthy older women performing a rapid maximum forward reach on the floor, particularly when using their forefoot for support, are at an increased risk for losing their balance.