View clinical trials related to Parkinsonian Disorders.
Filter by:Ultrasound can give important information about the morphology of the diaphragm and the amount of contraction. Our aim, with the prediction that a restrictive pathology will occur in the pulmonary function with the addition of camptocormia in Parkinson's patients; to compare respiratory functions in Parkinson's patients with and without camptocormia, to investigate the correlation between ultrasonographically measured diaphragmatic thickness and pulmonary function test values.
People living with Parkinson's disease experience progressive motor and non-motor symptoms, which negatively impact on health-related quality of life. Symptoms emerge and evolve as the disease progresses. Current care models are often inadequate to meet their needs. This study aims to evaluate whether a complex and innovative model of integrated care will increase an individual's ability to achieve their personal goals, have a positive impact on health and symptom burden, and be more cost-effective when compared with usual care.
With the aging of the population due to an increase in longevity, the number of people with Parkinson's disease is increasing (166,712 in France, as of December 31, 2015) and the number of patients with motor or cognitive-behavioral disorders is already a major public health challenge (1). In neurodegenerative diseases, the current strategy is to identify the disease early and, if possible, to consider therapeutic measures to slow down the progression of the disease. Classically, when faced with the early stages of Parkinsonism, the investigators differentiate idiopathic Parkinson's disease (IPD) from atypical Parkinsonian syndromes (AP), which include multiple system atrophy (MSA), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP), for which the prognoses are more severe and the therapies less effective. In the early stage of the disease, when the symptoms are not do no yet differentiate the diseases, the differential diagnosis between IPD and PSP is a real challenge for clinicians (2). Cerebral MRI can help in the diagnosis but is most often only an indicator, as it may be normal in the early stages of the disease (2). The recent emergence of targeted therapies, specific to tauopathies or synucleinopathies, makes it essential to establish a diagnosis as early as possible in order to curb the evolution of the disease (3). The investigators propose here a first study on the analysis of biomarkers of neurodegeneration from lipid metabolism allowing to discriminate IPD and AP from peripheral blood. Two recent studies have provided evidence of the discriminatory character of neurofilament blood testing in the early phases of parkinsonism (4,5). On the other hand, to our knowledge, none of them has studied markers from mitochondrial and peroxisomal metabolism, which could play a key role in the pathophysiology of these diseases (6,7,8,9,10). Our strategy will therefore be to study idiopathic or atypical Parkinsonism subjects with a clearly established diagnosis in a cross-sectional manner, and to identify one or more blood markers of neurodegeneration predictive of IPD or AP, hypothesizing that these markers will be at significantly different levels between the two groups (descriptive analysis). The markers studied will include markers of neurodegeneration, markers of mitochondrial function, peroxisomal function and oxidative stress. The investigators will then study the correlations between these biomarkers and motor scores of disease severity.
The diagnosis of Parkinson's disease (PD) relies mainly on clinical observation of the patient, looking for the three characteristic symptoms and sometimes remains a real challenge. Machine Learning (ML) algorithms could help to diagnose PD early and differentiate idiopathic PD from atypical Parkinsonian syndromes. In this context, the work of Castillo-Barnes' team provided a set of imaging features based on morphological characteristics extracted from DaTSCAN® or Ioflupane (iodine-123-labeled radiopharmaceutical) single-photon emission computed tomography (SPECT) scans to discern healthy participants from participants with Parkinson's disease in a balanced set of SPECTs from the "Parkinson's Progression Markers Initiative" (PPMI) data base. The team of a study evaluated the classification performance of Parkinson's patients and normal controls when semi-quantitative indicators and shape features obtained on the dopamine transporter (DAT) by Ioflupane (123I-IP) single-photon emission computed tomography (SPECT) are combined as a machine learning (ML) feature. Artificial Intelligence (AI) based methods can improve diagnostic assessments. Several dopaminergic imaging studies using Artificial have reported accuracy of up to 90% for the diagnosis of PD. These automated approaches use machine learning methods, based on textural analyses, to (i) differentiate PD and healthy subjects, (ii) differentiate PD and vascular parkinsonism, and (iii) distinguish between different forms of atypical parkinsonism. A study conducted in 2 centers using a linear support vector machine (SVM) model discriminated patients with PD and healthy subjects with an accuracy of 82.5%.This performance is similar to visual assessment by nuclear physicians A linear SVM model based on voxel values of statistical parametric images was able to differentiate PD from vascular parkinsonism with an accuracy of 90.4%. The Nancy team has extensive experience in the detection of PD in SPECT and SPECT/CT scans with Ioflupane or DaTSCAN™
Currently, the Movement Disorders Society (MDS)-UPDRS scale remains the gold standard to document the outcomes in clinical trials for Parkinson's disease (PD). The MDS-UPDRS is far from infallible, as it is based on subjective scoring (using a rather crude ordinal score), while execution of the tests depends on clinical experience. Not surprisingly, the scale is subject to both significant intra- and inter-rater variability that are sufficiently large to mask an underlying true difference between an effective intervention and placebo. Digital biomarkers may be able to overcome the limitations of the MDS-UPDRS, as they continuously collects real-time data, during the patient's day to day activities. In this study the investigators are interested in developing algorithms to track progression of bradykinesia, gait impairment, postural sway, tremor, physical activity, sleep quality, and autonomic dysfunction (the latter being derived from e.g. skin conductance and changes in heart rate variability).
Bradykinesia is a key parkinsonian feature yet subjectively assessed by the MDS-UPDRS score, making reproducible measurements and follow-up challenging. In a Movement Disorder Unit, the investigators acquired a large database of videos showing parkinsonian patients performing Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III protocols. Using a Deep Learning approach on these videos, the investigators aimed to develop a tool to compute an objective score of bradykinesia from the three upper limb tests described in the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III.
Dystonia is a disabling symptom affecting both patients with idiopathic Parkinson's disease (PD) and atypical parkinsonism (AP). Botulinum toxinum (BoNT), by blocking muscle contraction, is a possible treatment for focal dystonia. The benefit of BoNT treatment has been proven in some focal dystonia associated with PD or AP. The investigators aim to give an overview of the efficacy of BoNT in a variety of focal dystonia in a large cohort of parkinsonian patients.
The purpose of this project is to investigate whether a 3-week training program involving music beat (serving as a type of rhythmic auditory stimulation) reduces the severity of bradykinesia and dyskinesia in at-risk individuals and schizophrenia patients. It is hypothesized that the program is effective in reducing the severity of bradykinesia and dyskinesia in at-risk individuals and schizophrenia patients.
The investigators are conducting a study to compare the self-reports of executive functions (that is to say, what role cognitive processes such as working memory and attention) in persons with Parkinson's Disease to the reports of executive functions completed by their significant others. To conduct this study, the investigators need the participation of persons who are diagnosed with Parkinson's Disease and their significant others.
Diagnosing Parkinson's disease (PD) depends on the clinical history of the patient and the patient's response to specific treatments such as levodopa. Unfortunately, a definitive diagnosis of PD is still limited to post-mortem evaluation of brain tissues. Furthermore, diagnosis of idiopathic PD is even more challenging because symptoms of PD overlap with symptoms of other conditions such as essential tremor (ET) or Parkinsonian syndromes (PSs) such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), or vascular Parkinsonism (VaP). Based on the principle that PD and PSs affect brain areas involved in eye movement control, this trial will utilize a platform that records complex eye movements and use a proprietary algorithm to characterize PSs. Preliminary data demonstrate that by monitoring oculomotor alterations, the process can assign PD-specific oculomotor patterns, which have the potential to serve as a diagnostic tool for PD. This study will evaluate capabilities of the process and its ability to differentiate PD from other PSs with statistical significance. The specific aims of this proposal are: To optimize the detection and analysis algorithms, and then to evaluate the process against neurological diagnoses of PD patients in a clinical study.