Unraveling progression subtypes in people with Huntington's disease.

Background: Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care. Methods: Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits. Results: Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only. Conclusion: In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00368-2.

The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson's disease: a long-term feasibility study.

BACKGROUND: Exercise therapy is considered effective for the treatment of motor impairment in patients with Parkinson's disease (PD). During the COVID-19 pandemic, training sessions were cancelled and the implementation of telerehabilitation concepts became a promising solution. The aim of this controlled interventional feasibility study was to evaluate the long-term acceptance and to explore initial effectiveness of a digital, home-based, high-frequency exercise program for PD patients. Training effects were assessed using patient-reported outcome measures combined with sensor-based and clinical scores. METHODS: 16 PD patients (smartphone group, SG) completed a home-based, individualized training program over 6-8 months using a smartphone app, remotely supervised by a therapist, and tailored to the patient's motor impairments and capacity. A control group (CG, n = 16) received medical treatment without participating in digital exercise training. The usability of the app was validated using System Usability Scale (SUS) and User Version of the Mobile Application Rating Scale (uMARS). Outcome measures included among others Unified Parkinson Disease Rating Scale, part III (UPDRS-III), sensor-based gait parameters derived from standardized gait tests, Parkinson's Disease Questionnaire (PDQ-39), and patient-defined motor activities of daily life (M-ADL). RESULTS: Exercise frequency of 74.5% demonstrated high adherence in this cohort. The application obtained 84% in SUS and more than 3.5/5 points in each subcategory of uMARS, indicating excellent usability. The individually assessed additional benefit showed at least 6 out of 10 points (Mean = 8.2 ± 1.3). From a clinical perspective, patient-defined M-ADL improved for 10 out of 16 patients by 15.5% after the training period. The results of the UPDRS-III remained stable in the SG while worsening in the CG by 3.1 points (24%). The PDQ-39 score worsened over 6-8 months by 83% (SG) and 59% (CG) but the subsection mobility showed a smaller decline in the SG (3%) compared to the CG (77%) without reaching significance level for all outcomes. Sensor-based gait parameters remained constant in both groups. CONCLUSIONS: Long-term training over 6-8 months with the app is considered feasible and acceptable, representing a cost-effective, individualized approach to complement dopaminergic treatment. This study indicates that personalized, digital, high-frequency training leads to benefits in motor sections of ADL and Quality of Life.

Parkinson's disease or multiple system atrophy: potential separation by quantitative susceptibility mapping.

Background: Due to the absence of robust biomarkers, and the low sensitivity and specificity of routine imaging techniques, the differential diagnosis between Parkinson's disease (PD) and multiple system atrophy (MSA) is challenging. High-field magnetic resonance imaging (MRI) opened up new possibilities regarding the analysis of pathological alterations associated with neurodegenerative processes. Recently, we have shown that quantitative susceptibility mapping (QSM) enables visualization and quantification of two major histopathologic hallmarks observed in MSA: reduced myelin density and iron accumulation in the basal ganglia of a transgenic murine model of MSA. It is therefore emerging as a promising imaging modality on the differential diagnosis of Parkinsonian syndromes. Objectives: To assess QSM on high-field MRI for the differential diagnosis of PD and MSA. Methods: We assessed 23 patients (nine PDs and 14 MSAs) and nine controls using QSM on 3T and 7T MRI scanners at two academic centers. Results: We observed increased susceptibility in MSA at 3T in prototypical subcortical and brainstem regions. Susceptibility measures of putamen, pallidum, and substantia nigra reached excellent diagnostic accuracy to separate both synucleinopathies. Increase toward 100% sensitivity and specificity was achieved using 7T MRI in a subset of patients. Magnetic susceptibility correlated with age in all groups, but not with disease duration in MSA. Sensitivity and specificity were particularly high for possible MSA, and reached 100% in the putamen. Conclusion: Putaminal susceptibility measures, in particular on ultra-high-field MRI, may distinguish MSA patients from both, PD and controls, allowing an early and sensitive diagnosis of MSA.

Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data.

Falls are an eminent risk for older adults and especially patients with neurodegenerative disorders, such as Parkinson's disease (PD). Recent advancements in wearable sensor technology and machine learning may provide a possibility for an individualized prediction of fall risk based on gait recordings from standardized gait tests or from unconstrained real-world scenarios. However, the most effective aggregation of continuous real-world data as well as the potential of unsupervised gait tests recorded over multiple days for fall risk prediction still need to be investigated. Therefore, we present a data set containing real-world gait and unsupervised 4x10-Meter-Walking-Tests of 40 PD patients, continuously recorded with foot-worn inertial sensors over a period of two weeks. In this prospective study, falls were self-reported during a three-month follow-up phase, serving as ground truth for fall risk prediction. The purpose of this study was to compare different data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 74.0% (sensitivity: 60.0%, specificity: 88.0%) was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts and days of each participant. Our findings suggest that fall risk can be predicted best by merging the entire two-week real-world gait data of a patient, outperforming the prediction using unsupervised gait tests (68.0% balanced accuracy) and contribute to an improved understanding of fall risk prediction.

The Effects of an Individualized Smartphone-Based Exercise Program on Self-defined Motor Tasks in Parkinson Disease: Pilot Interventional Study.

BACKGROUND: Bradykinesia and rigidity are prototypical motor impairments of Parkinson disease (PD) highly influencing everyday life. Exercise training is an effective treatment alternative for motor symptoms, complementing dopaminergic medication. High frequency training is necessary to yield clinically relevant improvements. Exercise programs need to be tailored to individual symptoms and integrated in patients' everyday life. Due to the COVID-19 pandemic, exercise groups in outpatient setting were largely reduced. Developing remotely supervised solutions is therefore of significant importance. OBJECTIVE: This pilot study aimed to evaluate the feasibility of a digital, home-based, high-frequency exercise program for patients with PD. METHODS: In this pilot interventional study, patients diagnosed with PD received 4 weeks of personalized exercise at home using a smartphone app, remotely supervised by specialized therapists. Exercises were chosen based on the patient-defined motor impairment and depending on the patients' individual capacity (therapists defined 3-5 short training sequences for each participant). In a first education session, the tailored exercise program was explained and demonstrated to each participant and they were thoroughly introduced to the smartphone app. Intervention effects were evaluated using the Unified Parkinson Disease Rating Scale, part III; standardized sensor-based gait analysis; Timed Up and Go Test; 2-minute walk test; quality of life assessed by the Parkinson Disease Questionnaire; and patient-defined motor tasks of daily living. Usability of the smartphone app was assessed by the System Usability Scale. All participants gave written informed consent before initiation of the study. RESULTS: In total, 15 individuals with PD completed the intervention phase without any withdrawals or dropouts. The System Usability Scale reached an average score of 72.2 (SD 6.5) indicating good usability of the smartphone app. Patient-defined motor tasks of daily living significantly improved by 40% on average in 87% (13/15) of the patients. There was no significant impact on the quality of life as assessed by the Parkinson Disease Questionnaire (but the subsections regarding mobility and social support improved by 14% from 25 to 21 and 19% from 15 to 13, respectively). Motor symptoms rated by Unified Parkinson Disease Rating Scale, part III, did not improve significantly but a descriptive improvement of 14% from 18 to 16 could be observed. Clinically relevant changes in Timed Up and Go test, 2-minute walk test, and sensor-based gait parameters or functional gait tests were not observed. CONCLUSIONS: This pilot interventional study presented that a tailored, digital, home-based, and high-frequency exercise program over 4 weeks was feasible and improved patient-defined motor activities of daily life based on a self-developed patient-defined impairment score indicating that digital exercise concepts may have the potential to beneficially impact motor symptoms of daily living. Future studies should investigate sustainability effects in controlled study designs conducted over a longer period.

Adult-Onset Neurodegeneration in Nucleotide Excision Repair Disorders (NERDND ): Time to Move Beyond the Skin.

BACKGROUND: Variants in genes of the nucleotide excision repair (NER) pathway have been associated with heterogeneous clinical presentations ranging from xeroderma pigmentosum to Cockayne syndrome and trichothiodystrophy. NER deficiencies manifest with photosensitivity and skin cancer, but also developmental delay and early-onset neurological degeneration. Adult-onset neurological features have been reported in only a few xeroderma pigmentosum cases, all showing at least mild skin manifestations. OBJECTIVE: The aim of this multicenter study was to investigate the frequency and clinical features of patients with biallelic variants in NER genes who are predominantly presenting with neurological signs. METHODS: In-house exome and genome datasets of 14,303 patients, including 3543 neurological cases, were screened for deleterious variants in NER-related genes. Clinical workup included in-depth neurological and dermatological assessments. RESULTS: We identified 13 patients with variants in ERCC4 (n = 8), ERCC2 (n = 4), or XPA (n = 1), mostly proven biallelic, including five different recurrent and six novel variants. All individuals had adult-onset progressive neurological deterioration with ataxia, dementia, and frequently chorea, neuropathy, and spasticity. Brain magnetic resonance imaging showed profound global brain atrophy in all patients. Dermatological examination did not show any skin cancer or pronounced ultraviolet damage. CONCLUSIONS: We introduce NERDND as adult-onset neurodegeneration (ND ) within the spectrum of autosomal recessive NER disorders (NERD). Our study demonstrates that NERDND is probably an underdiagnosed cause of neurodegeneration in adulthood and should be considered in patients with overlapping cognitive and movement abnormalities. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson's Disease.

Falls are among the leading causes of injuries or death for the elderly, and the prevalence is especially high for patients suffering from neurological diseases like Parkinson's disease (PD). Today, inertial measurement units (IMUs) can be integrated unobtrusively into patients' everyday lives to monitor various mobility and gait parameters, which are related to common risk factors like reduced balance or reduced lower-limb muscle strength. Although stair ambulation is a fundamental part of everyday life and is known for its unique challenges for the gait and balance system, long-term gait analysis studies have not investigated real-world stair ambulation parameters yet. Therefore, we applied a recently published gait analysis pipeline on foot-worn IMU data of 40 PD patients over a recording period of two weeks to extract objective gait parameters from level walking but also from stair ascending and descending. In combination with prospective fall records, we investigated group differences in gait parameters of future fallers compared to non-fallers for each individual gait activity. We found significant differences in stair ascending and descending parameters. Stance time was increased by up to 20 % and gait speed reduced by up to 16 % for fallers compared to non-fallers during stair walking. These differences were not present in level walking parameters. This suggests that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the challenges stairs add to the balance and control system. Our work complements existing gait analysis studies by adding new insights into mobility and gait performance during real-world gait.

Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments.

Driven by the advancements of wearable sensors and signal processing algorithms, studies on continuous real-world monitoring are of major interest in the field of clinical gait and motion analysis. While real-world studies enable a more detailed and realistic insight into various mobility parameters such as walking speed, confounding and environmental factors might skew those digital mobility outcomes (DMOs), making the interpretation of results challenging. To consider confounding factors, context information needs to be included in the analysis. In this work, we present a context-aware mobile gait analysis system that can distinguish between gait recorded at home and not at home based on Bluetooth proximity information. The system was evaluated on 9 healthy subjects and 6 Parkinsons disease (PD) patients. The classification of the at home/not at home context reached an average F1-score of 98.2 ± 3.2 %. A context-aware analysis of gait parameters revealed different walking bout length distributions between the two environmental conditions. Furthermore, a reduction of gait speed within the at home context compared to walking not at home of 8.9 ± 9.4 % and 8.7 ±5.9 % on average for healthy and PD subjects was found, respectively. Our results indicate the influence of the recording environment on DMOs and, therefore, emphasize the importance of context in the analysis of continuous motion data. Hence, the presented work contributes to a better understanding of confounding factors for future real-world studies.

Detection of Unsupervised Standardized Gait Tests From Real-World Inertial Sensor Data in Parkinson's Disease.

Gait tests as part of home monitoring study protocols for patients with movement disorders may provide valuable standardized anchor-points for real-world gait analysis using inertial measurement units (IMUs). However, analyzing unsupervised gait tests relies on reliable test annotations by the patients requiring a potentially error-prone interaction with the recording system. To overcome this limitation, this work presents a novel algorithmic pipeline for the automated detection of unsupervised standardized gait tests from continuous real-world IMU data. In a study with twelve Parkinson's disease patients, we recorded real-world gait data over two weeks using foot-worn IMUs. During continuous daily recordings, the participants performed series of three consecutive 4×10 -Meters-Walking-Tests ( 4×10 MWTs) at different walking speeds, besides their usual daily-living activities. The algorithm first detected these gait test series using a gait sequence detection algorithm, a peak enhancement pipeline, and subsequence Dynamic Time Warping and then decomposed them into single 4×10 MWTs based on the walking speed. In the evaluation with 419 available gait test series, the detection reached an F1-score of 88.9% and the decomposition an F1-score of 94.0%. A concurrent validity evaluation revealed very good agreement between spatio-temporal gait parameters derived from manually labelled and automatically detected 4×10 MWTs. Our algorithm allows to remove the burden of system interaction from the patients and reduces the time for manual data annotation for researchers. The study contributes to an improved automated processing of real-world IMU gait data and enables a simple integration of standardized tests into continuous long-term recordings. This will help to bridge the gap between supervised and unsupervised gait assessment.

A Multisensory Deficit in the Perception of Pleasantness in Parkinson's Disease.

BACKGROUND: There is growing interest in non-motor symptoms in Parkinson's disease (PD), due to the impact on quality of life. Anhedonia, the inability to experience joy and lust, has a prevalence of up to 46% in PD. The perception of pleasantness of an odor is reduced in anhedonia without PD. We previously showed a reduced hedonic olfactory perception in PD, i.e., patients evaluated odors as less pleasant or unpleasant compared to controls. This deficit correlated with anhedonia. OBJECTIVE: We aimed to confirm these findings. Moreover, we hypothesized that the perception of pleasantness in PD is affected on a multisensory level and correlates with anhedonia. Therefore, we assessed olfactory, visual and acoustic evaluation of pleasantness in PD and healthy individuals. METHODS: Participants had to rate the pleasantness of 22 odors, pictures, and sounds on a nine-point Likert scale. Depression, anhedonia, and apathy were assessed by means of questionnaires. Results of the pleasantness-rating were compared between groups and correlated to scores of the questionnaires. RESULTS: In particular pleasant and unpleasant stimuli across all three modalities are perceived less intense in PD, suggesting that a reduced range of perception of pleasantness is a multisensory phenomenon. However, only a reduction of visual hedonic perception correlated with anhedonia in PD. A correlation of reduced perception of pleasantness with apathy or depression was not present. CONCLUSION: We provide evidence for a multisensory deficit in the perception of pleasantness. Further studies should delineate the underlying neural circuity and the diagnostic value to detect neuropsychiatric symptoms in PD.

Cardiac manifestation is evident in chorea-acanthocytosis but different from McLeod syndrome.

INTRODUCTION: We aimed to study the various cardiac manifestations of the two core neuroacanthocytosis (NA) syndromes, namely chorea-acanthocytosis (ChAc) and McLeod syndrome (MLS). So far, cardiac involvement has been described as specific feature only for MLS. METHODS: We studied six patients with ChAc (mean age 44.5 years, five men, one woman) and six patients with MLS (mean age 57.1 years, all men). Cardiac evaluation included echocardiography and/or cardiac magnetic resonance imaging (cardiac MRI), 24-h ECG-recording and examination of cardiac biomarkers. RESULTS: Cardiac involvement of ChAc was found in four of six patients. Two patients showed mildly reduced left ventricular ejection fraction (LVEF), two other patients mild to moderate left ventricular (LV) dilatation. Neither an increase in ventricular ectopic beats nor ventricular tachycardia were evident in ChAc. Four of five MLS patients showed left ventricle dilatation and reduced left ventricular ejection fraction (LVEF). Two of these, in addition, had critical ventricular tachycardia. High sensitive troponin T was elevated in all patients, for whom data were available (n = 10). In contrast, elevation of high sensitive troponin I was found in one of six ChAc and one of two MLS patients. CONCLUSION: For the first time, we reveal cardiac involvement in a cohort of six ChAc patients, while the risk to develop heart failure seems lower than in MLS. Our study confirms the malignant nature of MLS in terms of ventricular arrhythmias and progression to advanced heart failure. Herein, we define disease-specific recommendations for cardiac assessment in both conditions.

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients.

BACKGROUND: To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. METHOD: To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson's Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. RESULTS: The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ([Formula: see text] strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ([Formula: see text] strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. CONCLUSION: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

Human alpha-synuclein overexpressing MBP29 mice mimic functional and structural hallmarks of the cerebellar subtype of multiple system atrophy.

Multiple system atrophy (MSA) is a rare, but fatal atypical parkinsonian disorder. The prototypical pathological hallmark are oligodendroglial cytoplasmic inclusions (GCIs) containing alpha-synuclein (α-syn). Currently, two MSA phenotypes are classified: the parkinsonian (MSA-P) and the cerebellar subtype (MSA-C), clinically characterized by predominant parkinsonism or cerebellar ataxia, respectively. Previous studies have shown that the transgenic MSA mouse model overexpressing human α-syn controlled by the oligodendroglial myelin basic protein (MBP) promoter (MBP29-hα-syn mice) mirrors crucial characteristics of the MSA-P subtype. However, it remains elusive, whether this model recapitulates important features of the MSA-C-related phenotype. First, we examined MSA-C-associated cerebellar pathology using human post-mortem tissue of MSA-C patients and controls. We observed the prototypical GCI pathology and a preserved number of oligodendrocytes in the cerebellar white matter (cbw) accompanied by severe myelin deficit, microgliosis, and a profound loss of Purkinje cells. Secondly, we phenotypically characterized MBP29-hα-syn mice using a dual approach: structural analysis of the hindbrain and functional assessment of gait. Matching the neuropathological features of MSA-C, GCI pathology within the cbw of MBP29-hα-syn mice was accompanied by a severe myelin deficit despite an increased number of oligodendrocytes and a high number of myeloid cells even at an early disease stage. Intriguingly, MBP29-hα-syn mice developed a significant loss of Purkinje cells at a more advanced disease stage. Catwalk XT gait analysis revealed decreased walking speed, increased stride length and width between hind paws. In addition, less dual diagonal support was observed toward more dual lateral and three paw support. Taken together, this wide-based and unsteady gait reflects cerebellar ataxia presumably linked to the cerebellar pathology in MBP29-hα-syn mice. In conclusion, the present study strongly supports the notion that the MBP29-hα-syn mouse model mimics important characteristics of the MSA-C subtype providing a powerful preclinical tool for evaluating future interventional strategies.

Changes in Gastrointestinal Microbiome Composition in PD: A Pivotal Role of Covariates.

Altered gut microbiota may trigger or accelerate alpha-synuclein aggregation in the enteric nervous system in Parkinson's disease (PD). While several previous studies observed gut microbiota alterations in PD, findings like diversity indices, and altered bacterial taxa itself show a considerable heterogeneity across studies. We recruited 179 participants, of whom 101 fulfilled stringent inclusion criteria. Subsequently, the composition of the gut microbiota in 71 PD patients and 30 healthy controls was analyzed, sequencing V3-V4 regions of the bacterial 16S ribosomal RNA gene in fecal samples. Our goal was (1) to evaluate whether gut microbiota are altered in a southern German PD cohort, (2) to delineate the influence of disease duration, stage, and motor impairment, and (3) to investigate the influence of PD associated covariates like constipation and coffee consumption. Aiming to control for a large variety of covariates, strict inclusion criteria were applied. Finally, propensity score matching was performed to correct for, and to delineate the effect of remaining covariates (non-motor symptom (NMS) burden, constipation, and coffee consumption) on microbiota composition. Prior to matching altered abundances of distinct bacterial classes, orders, families, and genera were observed. Both, disease duration, and stage influenced microbiome composition. Interestingly, levodopa equivalent dose influenced the correlation of taxa with disease duration, while motor impairment did not. Applying different statistical tests, and after propensity score matching to control for NMS burden, constipation and coffee consumption, Faecalibacterium and Ruminococcus were most consistently reduced in PD compared to controls. Taken together, similar to previous studies, alterations of several taxa were observed in PD. Yet, further controlling for PD associated covariates such as constipation and coffee consumption revealed a pivotal role of these covariates. Our data highlight the impact of these PD associated covariates on microbiota composition in PD. This suggests that altered microbiota may mediate the protective effect of i.e., coffee consumption and the negative effect of constipation in PD.

Clinical Relevance of Standardized Mobile Gait Tests. Reliability Analysis Between Gait Recordings at Hospital and Home in Parkinson's Disease: A Pilot Study.

BACKGROUND: Gait impairments in Parkinson's disease (PD) are quantified using inertial sensors under standardized test settings in the hospital. Recent studies focused on the assessment of free-living gait in PD. However, the clinical relevance of standardized gait tests recorded at the patient's home is unclear. OBJECTIVE: To evaluate the reliability of supervised, standardized sensor-based gait outcomes at home compared to the hospital. METHODS: Patients with PD (n = 20) were rated by a trained investigator using the Unified Parkinson Disease Rating Scale (UPDRS-III). Gait tests included a standardized 4×10 m walk test and the Timed Up and Go Test (TUG). Tests were performed in the hospital (HOSPITAL) and at patients' home (HOME), and controlled for investigator, time of the day, and medication. Statistics included reliability analysis using Intra-Class correlations and Bland-Altman plots. RESULTS: UPDRS-III and TUG were comparable between HOSPITAL and HOME. PD patients' gait at HOME was slower (gait velocity Δ= -0.07±0.11 m/s, -6.1%), strides were shorter (stride length Δ= -9.2±9.4 cm; -7.3%), and shuffling of gait was more present (maximum toe-clearance Δ= -0.7±2.5 cm; -8.8%). Particularly, narrow walkways (<85 cm) resulted in a significant reduction of gait velocity at home. Reliability analysis (HOSPITAL vs. HOME) revealed excellent ICC coefficients for UPDRS-III (0.950, p < 0.000) and gait parameters (e.g., stride length: 0.898, p < 0.000; gait velocity: 0.914, p < 0.000; stance time: 0.922, p < 0.000; stride time: 0.907, p < 0.000). CONCLUSION: This pilot study underlined the clinical relevance of gait parameters by showing excellent reliability for supervised, standardized gait tests at HOSPITAL and HOME, even though gait parameters were different between test conditions.

Quantitative susceptibility mapping depicts severe myelin deficit and iron deposition in a transgenic model of multiple system atrophy.

Despite internationally established diagnostic criteria, multiple system atrophy (MSA) is frequently misdiagnosed, particularly at disease onset. While neuropathological changes such as demyelination and iron deposition are typically detected in MSA, these structural hallmarks were so far only demonstrated post-mortem. Here, we examine whether myelin deficit observed in a transgenic murine model of MSA can be visualized and quantified in vivo using specific magnetic resonance imaging (MRI) approaches. Reduced myelin content was measured histologically in prototypical white matter as well as mixed grey-white matter regions i.e. corpus callosum, anterior commissure, and striatum of transgenic mice overexpressing human α-synuclein under the control of the myelin basic protein promotor (MBP29-hα-syn mice). Correspondingly, in vivo quantitative susceptibility mapping (QSM) showed a strongly reduced susceptibility contrast in white matter regions and T2-weighted MR imaging revealed a significantly reduced grey-white matter contrast in MBP29-hα-syn mice. In addition, morphological analysis suggested a pronounced, white matter-specific deposition of iron in MBP29-hα-syn mice. Importantly, in vivo MRI results were matched by comprehensive structural characterization of myelin, iron, and axonal directionality. Taken together, our results provide strong evidence that QSM is a very sensitive tool measuring changes in myelin density in conjunction with iron deposition in MBP29-hα-syn mice. This multimodal neuroimaging approach may pave the way towards a novel non-invasive technique to detect crucial neuropathological changes specifically associated with MSA.

Huntingtin Lowering Strategies.

Trials using antisense oligonucleotide technology to lower Huntingtin levels in Huntington's disease (HD) are currently ongoing. This progress, taking place only 27 years after the identification of the Huntingtin gene (HTT) in 1993 reflects the enormous development in genetic engineering in the last decades. It is also the result of passionate basic scientific work and large worldwide registry studies that have advanced the understanding of HD. Increased knowledge of the pathophysiology of this autosomal dominantly inherited CAG-repeat expansion mediated neurodegenerative disease has led to the development of several putative treatment strategies, currently under investigation. These strategies span the whole spectrum of potential targets from genome editing via RNA interference to promoting protein degradation. Yet, recent studies revealed the importance of huntingtin RNA in the pathogenesis of the disease. Therefore, huntingtin-lowering by means of RNA interference appears to be a particular promising strategy. As a matter of fact, these approaches have entered, or are on the verge of entering, the clinical trial period. Here, we provide an overview of huntingtin-lowering approaches via DNA or RNA interference in present clinical trials as well as strategies subject to upcoming therapeutic options. We furthermore discuss putative implications for future treatment of HD patients.

Demonstration of brain region-specific neuronal vulnerability in human iPSC-based model of familial Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by protein inclusions mostly composed of aggregated forms of α-synuclein (α-Syn) and by the progressive degeneration of midbrain dopaminergic neurons (mDANs), resulting in motor symptoms. While other brain regions also undergo pathologic changes in PD, the relevance of α-Syn aggregation for the preferential loss of mDANs in PD pathology is not completely understood yet. To elucidate the mechanisms of the brain region-specific neuronal vulnerability in PD, we modeled human PD using human-induced pluripotent stem cells (iPSCs) from familial PD cases with a duplication (Dupl) of the α-Syn gene (SNCA) locus. Human iPSCs from PD Dupl patients and a control individual were differentiated into mDANs and cortical projection neurons (CPNs). SNCA dosage increase did not influence the differentiation efficiency of mDANs and CPNs. However, elevated α-Syn pathology, as revealed by enhanced α-Syn insolubility and phosphorylation, was determined in PD-derived mDANs compared with PD CPNs. PD-derived mDANs exhibited higher levels of reactive oxygen species and protein nitration levels compared with CPNs, which might underlie elevated α-Syn pathology observed in mDANs. Finally, increased neuronal death was observed in PD-derived mDANs compared to PD CPNs and to control mDANs and CPNs. Our results reveal, for the first time, a higher α-Syn pathology, oxidative stress level, and neuronal death rate in human PD mDANs compared with PD CPNs from the same patient. The finding implies the contribution of pathogenic α-Syn, probably induced by oxidative stress, to selective vulnerability of substantia nigra dopaminergic neurons in human PD.

Correction to: Gait variability as digital biomarker of disease severity in Huntington's disease.

The original version of this article unfortunately contained a mistake. The name of one author is not presented correctly in the author group.

Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies.

Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached a sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenarios.