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.

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.

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

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.

Aberrant cognitive phenotypes and altered hippocampal BDNF expression related to epigenetic modifications in mice lacking the post-synaptic scaffolding protein SHANK1: Implications for autism spectrum disorder.

Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by persistent deficits in social communication/interaction, together with restricted/repetitive patterns of behavior. ASD is among the most heritable neuropsychiatric conditions, and while available evidence points to a complex set of genetic factors, the SHANK gene family has emerged as one of the most promising candidates. Here, we assessed ASD-related phenotypes with particular emphasis on social behavior and cognition in Shank1 mouse mutants in comparison to heterozygous and wildtype littermate controls across development in both sexes. While social approach behavior was evident in all experimental conditions and social recognition was only mildly affected by genotype, Shank1-/- null mutant mice were severely impaired in object recognition memory. This effect was particularly prominent in juveniles, not due to impairments in object discrimination, and replicated in independent mouse cohorts. At the neurobiological level, object recognition deficits were paralleled by increased brain-derived neurotrophic factor (BDNF) protein expression in the hippocampus of Shank1-/- mice; yet BDNF levels did not differ under baseline conditions. We therefore investigated changes in the epigenetic regulation of hippocampal BDNF expression and detected an enrichment of histone H3 acetylation at the Bdnf promoter1 in Shank1-/- mice, consistent with increased learning-associated BDNF. Together, our findings indicate that Shank1 deletions lead to an aberrant cognitive phenotype characterized by severe impairments in object recognition memory and increased hippocampal BDNF levels, possibly due to epigenetic modifications. This result supports the link between ASD and intellectual disability, and suggests epigenetic regulation as a potential therapeutic target.

Effect of preoperative body mass index on weight loss after obesity surgery.

BACKGROUND: Previous studies suggest that individuals with body mass index (BMI) above versus below 60 kg/m(2) attain lower percentage of excess weight loss (%EWL) after bariatric surgery. The objectives of this study were to (1) test whether conclusions drawn about the effect of preoperative BMI on postoperative weight loss depend on the outcome measure, (2) test for evidence of a threshold effect at BMI = 60 kg/m(2), and (3) test the effect from surgery to 12-month follow-up, relative to 12- to 36-month follow-up. METHODS: Retrospective analyses of participants grouped according to preoperative BMI: 35-39.9 (n = 232); 40-49.9 (n = 1166); 50-59.9 (n = 429);≥60 (n = 166). RESULTS: As anticipated, individuals with higher versus lower preoperative BMI had greater total weight loss but lower %EWL at all postoperative time points (all, P<.0005). However, these individuals also had lower percentage of initial weight loss (%IWL) at all time points beyond 1 month postsurgery (all, P<.0005). From 12- to 36-months, individuals with BMI 35-39.9 had 3.2±14.3 %IWL (P<.0001); 40-49.9 had 1.0±8.9 %IWL (P<.0005); 50-59.9 had-2.4±10.0 %IWL (P<.0005); and≥60 had-3.6±11.5 %IWL (P<.0005). Overall F3,1989 = 20.2, P< .0005. CONCLUSIONS: Conclusions drawn about the effect of preoperative BMI may depend on the outcome measure. A dosage effect of preoperative BMI was apparent, with heavier individuals showing lower percentages of initial and excess weight loss, regardless of BMI above or below 60 kg/m(2). Finally, this effect was particularly apparent after the initial 12-month rapid weight loss phase, when less obese (BMI<50) individuals continued losing weight, while heavier individuals (BMI≥50) regained significant weight.