Reduced Range of Gait Speed: A Parkinson's Disease-Specific Symptom?

At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface

Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.

Inertial-Based Dual-Task Gait Normalcy Index at Turns: A Potential Novel Gait Biomarker for Early-Stage Parkinson's Disease

As one of the main motor indicators of Parkinson's disease (PD), postural instability and gait disorder (PIGD) might manifest in various but subtle symptoms at early stage resulting in relatively high misdiagnosis rate. Quantitative gait assessment under dual task or complex motor task (i.e., turning) may contribute to better understanding of PIGD and provide a better diagnostic indicator of early-stage PD. However, few studies have explored gait deviation evaluation algorithms under a complex dual task that reflect disease specificity. In this work, we proposed a novel inertial-based gait normalcy index (GNI) based on inertial-based quantitative gait assessment model to characterize the overall gait performance during both straight walking and turning with or without serial-3 subtraction task. The factor of group and task on the GNI variable was investigated and the feasibility of GNI to improve early-stage PD diagnostic performance was validated. The experimental results showed that the task paradigm is a significant factor on GNI performance where the dual-task GNI at turn had the best discriminating ability between early PD and HC (AUC =0.992) and was significantly correlated with UPDRS III (r =0.81), MMSE(r =0.57) and Mini-BEST(r =0.65). We also observed that the turning-based GNI has larger effect size compared to clinical scales, demonstrating that GNI during turning can reflect the changes of functional mobility in rehabilitation for the early PD. Our work offers an innovative and potential gait biomarker for early-stage PD diagnostics and provides a new perspective into gait performance of complex dual task and its application in early PD.

Limb-Kinetic apraxia of legs in Parkinson's disease: Prospective clinical investigation

Background

The study of dynamic organization of motor acts is important for investigation of motor impairment, and a possible sign of a disorder of fronto-parietal areas of the brain in Parkinson's disease (PD). We aimed to prospectively investigate whether limb-kinetic apraxia in legs (LKA-L) is a heretofore unrecognized manifestation of PD independent of bradykinesia and rigidity.

Methods

Patients with PD and healthy controls (HC) performed bipedal reciprocal coordination (BRC) and monopedal reciprocal coordination (MRC) tests as a foot modification of the Oseretzky exam (originally alternate antiphase clenching and unclenching of the fists of the right and left hands). While MRC allowed for alternating movements of one leg per unit of time, BRC required synchronous movements of both legs in antiphase. Leg movement rates and their quality were measured by video recording and compared statistically between the groups of PD and HC.

Results

The cohort consisted of 31 PD patients (mean age 69.3 ± 7.1 years,16 males) and 12 HC (mean age 69 ± 6.2 years, 6 males). No differences between PD and HC groups were identified in MRC rate of performance, which were used as a measure of legs movement speed, although the quality of MRC movements was poorer in PD patients (p = 0.022). BRC rate and its performance quality were significantly flawed in PD compared to controls (P = 0.002 and P = 0.003, respectively).

Conclusions

Testing for dynamic organization of LKA-L revealed disorder in individuals with PD. LKA-L analyses should be considered in the diagnosis of leg movements and gait disorders in PD.

Machine learning for early detection and severity classification in people with Parkinson's disease

Early detection of Parkinson's disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning models in classifying early and moderate-stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-m walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features-walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of the stride length at LWS-achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, the stride length at HWS and CV of the stride length at LWS provided an accuracy of 67.3% with Naïve Bayes. Walking at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve the early detection and severity assessment of individuals with PD.

Modeling and Optimizing Deep Brain Stimulation to Enhance Gait in Parkinson's Disease: Personalized Treatment with Neurophysiological Insights

Although high-frequency deep brain stimulation (DBS) is effective at relieving many motor symptoms of Parkinson's disease (PD), its effects on gait can be variable and unpredictable. This is due to 1) a lack of standardized and robust metrics for gait assessment in PD patients, 2) the challenges of performing a thorough evaluation of all the stimulation parameters space that can alter gait, and 3) a lack of understanding for impacts of stimulation on the neurophysiological signatures of walking. In this study, our goal was to develop a data-driven approach to identify optimal, personalized DBS stimulation parameters to improve gait in PD patients and identify the neurophysiological signature of improved gait. Local field potentials from the globus pallidus and electrocorticography from the motor cortex of three PD patients were recorded using an implanted bidirectional neural stimulator during overground walking. A walking performance index (WPI) was developed to assess gait metrics with high reliability. DBS frequency, amplitude, and pulse width on the "clinically-optimized" stimulation contact were then systemically changed to study their impacts on gait metrics and underlying neural dynamics. We developed a Gaussian Process Regressor (GPR) model to map the relationship between DBS settings and the WPI. Using this model, we identified and validated personalized DBS settings that significantly improved gait metrics. Linear mixed models were employed to identify neural spectral features associated with enhanced walking performance. We demonstrated that improved walking performance was linked to the modulation of neural activity in specific frequency bands, with reduced beta band power in the pallidum and increased alpha band pallidal-motor cortex coherence synchronization during key moments of the gait cycle. Integrating WPI and GPR to optimize DBS parameters underscores the importance of developing and understanding personalized, data-driven interventions for gait improvement in PD.

Adjustability of Gait Speed in Clinics and Free-Living Environments for People With Parkinson's Disease

OBJECTIVE

Gait speed is regulated by varying gait parameters depending on the diverse contexts of the environment. People with Parkinson's disease (PwPD) have difficulty adapting to gait control in their environment; however, the relationships between gait speed and spatiotemporal parameters in free-living environments have not been clarified. This study aimed to compare gait parameters according to gait speed in clinics and free-living environments.

METHODS

PwPD were assessed at the clinic and in a free-living environment using an accelerometer on the lower back. By fitting a bimodal Gaussian model to the gait speed distribution, gait speed was divided into lower and higher speeds. We compared the spatiotemporal gait parameters using a 2 × 2 (environment [clinic/free-living] × speed [lower/higher]) repeated-measures analysis of variance. Associations between Parkinson's disease symptoms and gait parameters were evaluated using Bayesian Pearson's correlation coefficients.

RESULTS

In the 41 PwPD included in this study, spatiotemporal gait parameters were significantly worse in free-living environments than in clinics and at lower speeds than at higher speeds. The fit of the walking speed distribution to the bimodal Gaussian model (adjustability of gait speed) in free-living environments was related to spatiotemporal gait parameters, severity of Parkinson's disease, number of falls, and quality of life.

CONCLUSION

The findings suggest that gait control, which involves adjusting gait speed according to context, differs between clinics and free-living environments in PwPD. Gait assessments for PwPD in both clinical and free-living environments should interpret gait impairments in a complementary manner.

Association between the sarcopenia-related traits and Parkinson's disease: A bidirectional two-sample Mendelian randomization study

OBJECTIVE

To explore the causal association between sarcopenia-related traits and Parkinson's disease by Mendelian randomization (MR) approach.

METHODS

A genome-wide association study (GWAS) of sarcopenia-related traits was done at the UK Biobank (UKB). The traits were appendicular lean mass, low hand grip strength (including the European Working Group on Sarcopenia in Older People (EWGSOP) and the Foundation for the National Institutes of Health (FNIH) criteria and usual walking pace. The International Parkinson's Disease Genomics Consortium (IPDGC) gave us GWAS data for Parkinson's disease (PD). We used three different types of MR analyses: including Inverse-variance weighted (IVW), Mendelian randomized Egger regression (MR-Egger), and weighted median methods (both weighted and simple modes).

RESULTS

The MR analysis showed that low hand grip strength was negatively associated with the risk of developing Parkinson's disease, including EWGSOP criterion (odds ratio (OR) = 0.734; 95% confidence interval (CI) = 0.575-0.937, P = 0.013) and FNIH criterion (OR = 0.619; 95% CI = 0.419-0.914, P = 0.016), and usual walking pace was also a risk factor for Parkinson's disease (OR = 3.307, 95% CI = 1.277-8.565, P = 0.014).

CONCLUSIONS

In European population, low hand grip strength is negatively associated with the risk of developing Parkinson's disease, and usual walking pace is also a risk factor for Parkinson's disease. Further exploration of the potential genetic mechanisms underlying hand grip strength and Parkinson's disease and the potential relationship between walking pace, balance, and falls in Parkinson's patients may help to reduce the burden of sarcopenia and Parkinson's disease.

Turning alterations detected by mobile health technology in idiopathic REM sleep behavior disorder

Idiopathic REM sleep Behavior Disorder (iRBD) is a condition at high risk of developing Parkinson's disease (PD) and other alpha-synucleinopathies. The aim of the study was to evaluate subtle turning alterations by using Mobile health technology in iRBD individuals without subthreshold parkinsonism. A total of 148 participants (23 persons with polysomnography-confirmed iRBD without subthreshold parkinsonism, 60 drug-naïve PD patients, and 65 age-matched controls were included in this prospective cross-sectional study. All underwent a multidimensional assessment including cognitive and non-motor symptoms assessment. Then a Timed-Up-and-Go test (TUG) at normal and fast speed was performed using mobile health technology on the lower back (Rehagait®, Hasomed, Germany). Duration, mean, and peak angular velocities of the turns were compared using a multivariate model correcting for age and sex. Compared to controls, PD patients showed longer turn durations and lower mean and peak angular velocities of the turns in both TUGs (all p ≤ 0.001). iRBD participants also showed a longer turn duration and lower mean (p = 0.006) and peak angular velocities (p < 0.001) compared to controls, but only in the TUG at normal speed. Mobile health technology assessment identified subtle alterations of turning in subjects with iRBD in usual, but not fast speed. Longitudinal studies are warranted to evaluate the value of objective turning parameters in defining the risk of conversion to PD in iRBD and in tracking motor progression in prodromal PD.