Comparative analysis of gait and speech in Parkinson's disease: hypokinetic or dysrhythmic disorders?

International audienceGait and speech are automatic motor activities which are frequently impaired in Parkinson's disease (PD). Obvious clinical similarities exist between these disorders but were never investigated. We propose to determine whether there exist any common features in PD between spatiotemporal gait disorders and temporal speech disorders. Gait and speech were analyzed on eleven PP undergoing deep-brain-stimulation of the sub-thalamic-nucleus (STN-DBS) and eleven control subjects (CS) under 3 conditions of velocity (natural, slow and speed). The patients were tested with and without L-Dopa and stimulator ON or OFF. Locomotor parameters were recorded using an optoelectronic system. Speech parameters were recorded with a headphone while subjects were reading a short paragraph. The results confirmed that PP walk and read more slowly than controls. Patient's difficulties in modulating walking and speech velocities seem to be due mainly to an inability to internally control the step length and the interpause-speech duration ISD. STN-DBS and levodopa increased patients' walking velocity by increasing the step length. STN-DBS and levodopa had no effect on speech velocity but restored the patients' ability to modulate the ISD. The walking cadence and speech index of rythmicity (SPIR) tended to be lower in patients and were not significantly improved by STN-DBS or levodopa. Speech and walking velocity as well as ISD and step length were correlated in both groups. Negative correlations between SPIR and walking cadence were observed in both groups Similar fundamental hypokinetic impairment and probably a similar rhythmic factor affected similarly the patients' speech and gait. These results suggest a similar physiopathological process in both walking and speaking dysfunction

LES FLUCTUATIONS NON MOTRICES AU COURS DE LA MALADIE DE PARKINSON (DES NEUROLOGIE)

AIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Electronic Health Record-Derived Phenotyping Models to Improve Genomic Research in Stroke

Stroke is a highly heterogeneous and complex disease that is a leading cause of death in the United States. The landscape of risk factors for stroke is vast, and its large genetic burden has yet to be fully discovered. We hypothesize that the small number of stroke variants recovered so far is due to 1) the vast phenotypic heterogeneity of stroke and 2) binary labeling of stroke genome-wide association study (GWAS) participants as cases or controls. Specifically, genome-wide association studies accumulate hundreds of thousands to millions of participants to acquire adequate signal for variant discovery. This requires time-consuming manual curation of cases and controls often involving large-scale collaborations. Genetic biobanks connected to electronic health records (EHR) can facilitate these studies by using data routinely captured during clinical care like billing diagnosis codes. These data, however, do not define adjudicated cases and controls, with many patients falling somewhere in between. There is an opportunity to use machine learning to add nuance to these definitions. We hypothesize that an expanded definition of disease by incorporating correlated diseases and risk factors from EHR data will improve GWAS power. We also hypothesize that granularly subtyping stroke using unsupervised learning methods can provide insight into stroke etiology and heterogeneity. In Chapter 1, we described the motivation for building upon current phenotyping methods for subtyping and genome-wide association studies to improve GWAS power. In Chapter 2, using patients from Columbia-New York Presbyterian (NYP) Hospital, we built and evaluated machine learning models to identify patients with acute ischemic stroke based on 75 different case-control and classifier combinations. In chapter 3, we compared two data-driven and unsupervised methods, non-negative matrix factorization (NMF) and Hierarchical Poisson Factorization, to subtype stroke patients and determined whether any of the subtypes correlate to stroke severity. In chapter 4, we estimated the heritability of acute ischemic stroke by treating the patient probabilities assigned by the machine learning phenotyping models for acute ischemic stroke in chapter 2 as a quantitative trait and mapping the probabilities to Columbia-NYP EHR-generated pedigrees. We also applied our machine learning phenotyping algorithm method, which we call QTPhenProxy, to venous thromboembolism on Columbia eMERGE Consortium patients and ran a genome-wide association study using the model probabilities as a quantitative trait. Finally, we applied QTPhenProxy to subjects in the UK Biobank for stroke and 14 other diseases and ran genome-wide association studies for each disease. We found that our machine-learned models performed well in identifying acute ischemic stroke patients in the Columbia-NYP EHR and in the UK Biobank. We also found some NMF-derived subtypes that were significantly correlated with stroke severity. We were underpowered in the eMERGE venous thromboembolism cohort GWAS and did not recover any known or new variants. Finally, we found that QTPhenProxy improved the power of GWAS of stroke and several subtypes in the UK Biobank, recovered known variants, and discovered a new variant that replicates in a previous stroke GWAS. Our results for QTPhenProxy demonstrate the promise of incorporating large but messy sets of data, such as the electronic health record, to improve signal in genome-wide association studies

Early atypical signs and insula hypometabolism predict survival in multiple system atrophy

International audienceObjective We aim to search for predictors of survival among clinical and brain 18 F-FDG positron emission tomography (PET) metabolic features in our cohort of patients with multiple system atrophy (MSA). Methods We included patients with a ‘probable’ MSA diagnosis for whom a clinical evaluation and a brain PET were performed early in the course of the disease (median 3 years, IQR 2–5). A retrospective analysis was conducted using standardised data collection. Brain PET metabolism was characterised using the Automated Anatomical Labelling Atlas. A Cox model was applied to look for factors influencing survival. Kaplan-Meier method estimated the survival rate. We proposed to develop a predictive ‘risk score’, categorised into low-risk and high-risk groups, using significant variables entered in multivariate Cox regression analysis. Results Eighty-five patients were included. The overall median survival was 8 years (CI 6.64 to 9.36). Poor prognostic factors were orthostatic hypotension (HR=6.04 (CI 1.58 to 23.12), p=0.009), stridor (HR=3.41 (CI 1.31 to 8.87), p=0.012) and glucose PET hypometabolism in the left insula (HR=0.78 (CI 0.66 to 0.92), p=0.004). Good prognostic factors were time to diagnosis (HR=0.68 (CI 0.54 to 0.86), p=0.001) and use of selective serotonin reuptake inhibitor (SSRI) (HR=0.17 (CI 0.06 to 0.46), p<0.001). The risk score revealed a 5-year gap separating the median survival of the two groups obtained (5 years vs 10 years; HR=5.82 (CI 2.94 to 11.49), p<0.001). Conclusion The clinical prognosis factors we have described support published studies. Here, we also suggest that brain PET is of interest for prognosis assessment and in particular in the search for left insula hypometabolism. Moreover, SSRIs are a potential drug candidate to slow the progression of the disease

Brain PET substrate of impulse control disorders in Parkinson's disease: A metabolic connectivity study

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Exploring the heterogeneous morphometric data in essential tremor with probabilistic modelling

Essential tremor (ET) is a prevalent movement disorder characterized by marked clinical heterogeneity. Here, we explored the morphometric underpinnings of this cross-subject variability on a cohort of 34 patients with rightdominant drug-resistant ET and 29 matched healthy controls (HCs). For each brain region, group-wise morphometric data was modelled by a multivariate Gaussian to account for morphometric features' (co)variance. No group differences were found in terms of mean values, highlighting the limits of more basic group comparison approaches. Variance in surface area was higher in ET in the left lingual and caudal anterior cingulate cortices, while variance in mean curvature was lower in the right superior temporal cortex and pars triangularis, left supramarginal gyrus and bilateral paracentral gyrus. Heterogeneity further extended to the right putamen, for which a mixture of two Gaussians fitted the ET data better than a single one. Partial Least Squares analysis revealed the rich clinical relevance of the ET population's heterogeneity: first, increased head tremor and longer symptoms' duration were accompanied by broadly lower cortical gyrification. Second, more severe upper limb tremor and impairments in daily life activities characterized the patients whose morphometric profiles were more atypical compared to the average ET population, irrespective of the exact nature of the alterations. Our results provide candidate morphometric substrates for two different types of clinical variability in ET. They also demonstrate the importance of relying on analytical approaches that can efficiently handle multivariate data and enable to test more sophisticated hypotheses regarding its organization

Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy

Essential tremor (ET) is the most common movement disorder. Its neural underpinnings remain unclear. Here, we quantified structural covariance between cortical thickness (CT), surface area (SA), and mean curvature (MC) estimates in patients with ET before and 1 year after ventro-intermediate nucleus stereotactic radiosurgical thalamotomy, and contrasted the observed patterns with those from matched healthy controls. For SA, complex rearrangements within a network of motion-related brain areas characterized patients with ET. This was complemented by MC alterations revolving around the left middle temporal cortex and the disappearance of positive-valued covariance across both modalities in the right fusiform gyrus. Recovery following thalamotomy involved MC readjustments in frontal brain centers, the amygdala, and the insula, capturing nonmotor characteristics of the disease. The appearance of negative-valued CT covariance between the left parahippocampal gyrus and hippocampus was another recovery mechanism involving high-level visual areas. This was complemented by the appearance of negative-valued CT/MC covariance, and positive-valued SA/MC covariance, in the right inferior temporal cortex and bilateral fusiform gyrus. Our results demonstrate that different morphometric properties provide complementary information to understand ET, and that their statistical cross-dependences are also valuable. They pinpoint several anatomical features of the disease and highlight routes of recovery following thalamotomy