Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.DFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels Neuromodulatio

Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing

Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p>0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.BMBF, 16SV8168, Verbundprojekt: Mobilitätsassistent für Parkinsonpatienten - Mobil4Park -; Teilvorhaben: On-Demand Stimulationssystem mit Tele-Medizin-FunktionDFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels NeuromodulationDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments

Unique properties of PTEN-L contribute to neuroprotection in response to ischemic-like stress

Abstract Phosphatase and tensin homolog (PTEN) signalling might influence neuronal survival after brain ischemia. However, the influence of the less studied longer variant termed PTEN-L (or PTENα) has not been studied to date. Therefore, we examined the translational variant PTEN-L in the context of neuronal survival. We identified PTEN-L by proteomics in murine neuronal cultures and brain lysates and established a novel model to analyse PTEN or PTEN-L variants independently in vitro while avoiding overexpression. We found that PTEN-L, unlike PTEN, localises predominantly in the cytosol and translocates to the nucleus 10–20 minutes after glutamate stress. Genomic ablation of PTEN and PTEN-L increased neuronal susceptibility to oxygen-glucose deprivation. This effect was rescued by expression of either PTEN-L indicating that both PTEN isoforms might contribute to a neuroprotective response. However, in direct comparison, PTEN-L replaced neurons were protected against ischemic-like stress compared to neurons expressing PTEN. Neurons expressing strictly nuclear PTEN-L NLS showed increased vulnerability, indicating that nuclear PTEN-L alone is not sufficient in protecting against stress. We identified mutually exclusive binding partners of PTEN-L or PTEN in cytosolic or nuclear fractions, which were regulated after ischemic-like stress. GRB2-associated-binding protein 2, which is known to interact with phosphoinositol-3-kinase, was enriched specifically with PTEN-L in the cytosol in proximity to the plasma membrane and their interaction was lost after glutamate exposure. The present study revealed that PTEN and PTEN-L have distinct functions in response to stress and might be involved in different mechanisms of neuroprotection