Aging is a heterogeneous process characterized by marked variability in biological, cognitive, and functional trajectories among individuals of the same chronological age. Consequently, there is greater interest in biomarkers capable of identifying early deviations from healthy aging and predicting adverse outcomes. This review provides an updated overview of neuronal and behavioral biomarkers associated with aging and disease and discusses their integration with physical performance and functional outcomes in older adults. Neuronal biomarkers, including neuroimaging, blood-based, and neurovascular markers, capture key biological processes such as neurodegeneration, neuroinflammation, vascular dysfunction, and impaired resilience. Behavioral biomarkers, including gait, mobility, dual-task performance, balance, physical activity, sleep, and digital phenotyping, reflect the functional expression of these underlying biological changes. Evidence suggests that alterations in these biomarkers may precede cognitive decline, frailty, disability, and loss of independence by several years. Integrating neuronal and behavioral biomarkers with measures of physical performance provides a comprehensive framework for identifying individuals at increased risk of adverse aging trajectories and may support precision geroscience approaches aimed at preserving intrinsic capacity, functional ability, and healthy aging. Future research should focus on validating multimodal biomarker models and their application in clinical and population settings.

To determine the influence of environmental complexity on cognitive-motor interaction during wheelchair propulsion. Fourteen participants completed two single- to dual-task trials of Serial-7 Subtraction Task and four environments varying in complexity. Differences in cognitive and motor function were determined through paired samples t-tests and percent changes (dual-task costs [DTCs]) were calculated. Within-subjects, repeated-measures ANOVAs were performed to determine differences in DTCs across environments. Cognitive function was maintained from the single- to dual-task trial in all environments (p ≥ 0.16) and yielded similar DTCs across environments (p = 0.80). Compromised motor function was observed in all environments (p ≤ 0.03) and yielded similar DTCs across environments (p ≥ 0.37). Wheelchair propulsion is attentionally demanding; however, environmental complexity did not exacerbate cognitive or motor dual-task costs.

Mobility impairment is common in multiple sclerosis, especially in older adults with multiple sclerosis (OAMS). Grey matter (GM) changes are well documented in MS, and GM atrophy is common in older adults. The relationship between GM changes and mobility disability in OAMS is unknown. We sought to identify GM patterns associated with gait speed in OAMS and healthy older controls, using structural covariance network analysis. OAMS (n = 102; 64.8 ± 4.4 years) and healthy controls (n = 106; 68.2 ± 7.3 years) underwent brain MRI and gait assessments; structural covariance networks were constructed to elucidate brain regions with significant associations between GM volume and 25-foot walk gait speed. We used voxel-wise linear regression analyses to elucidate per-network subregions with significant correlations with gait speed. Voxel-wise moderation analysis tested for group differences in these associations. Across the entire cohort, the following networks demonstrated significant gait speed associations: bilateral hippocampus, bilateral caudate/pallidum/putamen, bilateral thalamus/putamen, right middle temporal gyrus and multiple cerebellar regions. There were no significant group-by-network interaction effects. In summary, structural network analysis reveals unique brain patterns of gait speed in older adults, but these patterns are common amongst healthy older adults and OAMS and highlight the importance of cerebellar and subcortical networks in supporting gait speed.

To determine how motor control patterns in wheelchair propulsion mediate attentional demands. Fourteen participants completed two single- and dual-task trials of Serial-7 Subtraction and four, 30-second motor tasks: Static Sitting, Short Leans, Maximal Leans, and Stationary Propulsion. Differences in cognitive and motor function were determined through paired samples t-tests and percent changes (dual-task costs [DTCs]) were calculated. Within-subjects, repeated-measures ANOVAs determined differences in DTCs across motor tasks. Cognitive function was maintained from the single- to dual-task trials across tasks (DTC ≤ 13%; p = 0.12-0.74). Motor function was uniquely maintained during Maximal Leans (DTC ≤ 0.5%; p = 0.12-0.93), which yielded significantly lower dual-task cognitive function than the others (p < 0.01). Seated balance may be the primary contributor to the attentional demands of wheelchair propulsion.

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.

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.

Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson’s disease (PD). This study examined the ability of deep learning approaches to grade the 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. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extremity (finger tapping, hand movements, and pronation–supination movements of the hands) and lower extremity (toe tapping and leg agility) tasks consistent with the MDS-UPDRS. Overall, we found a 12.2% improvement in accuracy after expanding the training dataset and pre-training using max-vote inference on hand movement tasks. Moreover, we found that the classification performance improves for every task except toe tapping after the addition of HOA training data. These findings suggest that learning from HOA motion data can implicitly improve the representations of PD motion data for the purposes of motor impairment classification. Further, our results suggest that unsupervised pre-training can improve the performance of motor impairment classifiers without any additional annotated PD data, which may provide a viable solution for a widely deployable telemedicine solution.

A low-cost quantitative structured office measurement of movements in the extremities of people with Parkinson's disease [1,2] was performed on participants with Parkinson's disease and multiple system atrophy as well as age- and sex-matched healthy participants with typical development. 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 (3.4 Finger tapping, 3.5 Hand movements, 3.6 Pronation-supination movements of hands, 3.7 Toe tapping, and 3.8 Leg agility) [1] underwent processing to fast Fourier [5] and amor and bump continuous wavelet transforms [6], [7], [8], [9], [10], [11], [12], [13]. Images of the signals and their transforms [4], [5], [6] of the five repetitive tasks of each participant were randomly expressed as panels on an electronic framework for rating by 35 trained examiners who did not know the source of the original output [14]. The team of international raters completed ratings of the signals and their transforms independently using criteria like the scoring systems for live assessments of movements in human participants [1,2]. The raters scored signals and transforms for deficits in the sustained performance of rhythmic movements (interruptions, slowing, and amplitude decrements) often observed in people with Parkinson's disease [15], [16], [17], [18], [19], [20]. Raters were first presented the images of the signals and transforms of a man with multiple system atrophy as a test and a retest in a different random order. After the raters completed the assessments of the man with multiple system atrophy, they were presented random test and retest panels of the images of signals and transforms of ten participants with Parkinson's disease who completed a single rating session. After the raters completed the assessments of the participants with Parkinson's disease who completed one set of ratings, they were presented random test and retest panels of the images of signals and transforms of (A) ten participants with Parkinson's disease and (B) eight age- and sex-match healthy participants with typical development who completed two rating session separated by a month or more [15], [16], [17], [18], [19], [20]. The data provide a framework for further analysis of the acquired information. Additionally, the data provide a template for the construction of electronic frameworks for the remote analysis by trained raters of signals and transforms of rhythmic processes to verify that the systems are operating smoothly without interruptions or changes in frequency and amplitude. Thus, the data provide the foundations to construct electronic frameworks for the virtual quality assurance of a vast spectrum of rhythmic processes. The dataset is a suitable template for solving unsupervised and supervised machine learning algorithms. Readers may utilize this procedure to assure the quality of rhythmic processes by confirming the absence of deviations in rate and rhythm. Thus, this procedure provides the means to confirm the quality of the vast spectrum of rhythmic processes.

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.

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.

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.

A low-cost quantitative continuous measurement of movements utilizes accelerometers to generate signal outputs to precisely record the positions of extremities during the performance of movements. This procedure can readily be accomplished with inexpensive materials constructed indivisuals throughout the world. The proposed protocol provides the framework for trained raters to assess the signal outputs by visual observation to generate objective measurements like the measurements of the actual movements. Expert raters can then remotely give quantitative suggestions for providers in underserved regions to utilize precision medicine to develop optimal treatment plans tailored to the specific needs of each individual. The proposed protocol lays the foundations for experts located in tertiary centers to provide optimal assessments of signal outputs generated remotely in underserved regions. This protocol provides the means to address gaps in current research including the dearth of objective measurements of movements utilizing automatic intelligence and machine learning to accurately and precisely analyze movement assessments. Future research will include the development of robotic tools to perform assessments and analyses of the movements of human beings to enhance the conduct of movement evaluations of people with Parkinson's disease and related conditions to apply precision medicine for optimal diagnostic and therapeutic interventions.

Real-time examination of the effect of emotional state changes on the control of movement in complex environments presents a significant challenge. Towards the goal of developing a novel test bed for monitoring and manipulating emotional states in complex environments, we aim to synthesize real-time sensing of state anxiety in humans while walking in virtual outdoor environments with implicit biofeedback. Through the use of immersive virtual reality, realistic and complex ambulation environments will be provided to participants to examine their response and adaptation to potentially anxiety-inducing stimuli in their community. Through the integration of real-time sensing of anxiety changes with biofeedback, we seek to examine the neural and behavioral response to virtual community ambulation environments and how successfully biofeedback may alleviate state anxiety in older adults.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world. PD is known to lead to marked alterations in cortico-thalamo-basal ganglia activity and subsequent movements, which may provide a biomarker for PD diagnosis. Delay differential analysis (DDA) is a time domain analysis framework based on embedding theory in nonlinear dynamics. An embedding reveals the nonlinear invariant properties of an unknown dynamical system (here the brain) from a single time series electroencephalography (EEG) or behavioral signals. The DDA models serve as a low-dimensional nonlinear functional basis onto which the data are mapped. The combination of behavioral and neurological observations gives rise to a multimodal analysis framework that could improve our understanding and classification of PD using time series data from physical systems. We demonstrate how 750 ms of multimodal data can be used to improve DDA classification performance of PD, over clean EEG or behavioral time series data on their own, in two distinct virtual reach to grasp tasks in an uncertain and dynamic virtual reality environment. Thus, multimodal DDA may provide a tool for aiding the clinician in the diagnosis of PD and bolster classification performance through the combination of a wide array of neural or behavioral signals.

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.

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.

We outline an experimental setup designed to dynamically understand neural responses to visual cliffs while walking. The goal of our work is understanding and mitigating fear of falling, particularly among the elderly. In our setup, an EEG cap monitors a subject’s neural activity while the subject is immersed in a virtual world and walking on an instrumented treadmill. The subject’s response to visual stimuli is measured by both the EEG cap and by speed and pressure data from the treadmill. Based on this data, we can dynamically alter the landscape in the virtual world. We hope that our setup may be useful in helping subjects develop mechanisms to compensate for significant fear of falling while walking.

Mobility and cognitive impairments are common in persons with multiple sclerosis (MS), and are expected to worsen with increasing age. However, no studies, to date, in part due to limitations of conventional neuroimaging methods, have examined changes in brain activation patterns during active locomotion in older patients with MS. This study used functional Near Infrared Spectroscopy (fNIRS) to evaluate real-time neural activation differences in the pre-frontal cortex (PFC) between middle-aged to older adults with MS and healthy controls during single (Normal Walk; NW) and dual-task (Walking While Talking; WWT) locomotion tasks. Eight middle-aged to older adults with MS and eight healthy controls underwent fNIRS recording while performing the NW and WWT tasks with an fNIRS cap consisting of 16 optodes positioned over the forehead. The MS group had greater elevations in PFC oxygenation levels during WWT compared to NW than healthy controls. There was no walking performance difference between groups during locomotion. These findings suggest that middle-aged to older individuals with MS might be able to achieve similar levels of performance through the use of increased brain activation. This study is the first to investigate brain activation changes during the performance of simple and divided-attention locomotion tasks in MS using fNIRS.