Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining difusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classifcation performance of subcortical FA and MD was also evaluated to compare the discriminant ability between difusion tensor-derived metrics and NOS. Using difusion-weighted images acquired in a 3T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classifcation procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classifed. NOS features outperformed the discrimination performance obtained with FA and MD. Our fndings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than difusion tensor-derived metrics for the detection of MSA

Brain abnormalities detection in de Novo Parkinsonian patients

International audienc

Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease

peer reviewedHighlights First whole-brain probabilistic tractography study in Parkinson's disease High quality diffusion-weighted images (120 gradient directions, b = 2500 s/mm2) Voxel-based group analysis comparing early-stage patients and controls Abnormal reconstructed track density in the nigrostriatal pathway and brainstem Track density also increased in limbic and cognitive circuits

Mixture Models for the characterization of brain abnormalities in "de novo" Parkinsonian patients

National audienc

How should we be using biomarkers in trials of disease modification in Parkinson’s disease?

The recent validation of the alpha synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson’s disease has formed the backbone for a proposed staging system for incorporation in Parkinson’s disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson’s disease patients into trials (as distinct from patients with non- Parkinson’s disease parkinsonism or non- Parkinson’s disease tremors). There remain however further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson’s disease, namely: optimising the distinction between different alpha synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; as sensitive means of confirming target engagement; and in the early prediction of longer-term clinical benefit. For example; levels of cerebrospinal fluid proteins such as the lysosomal enzyme ß-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer disease like pathology (detectable through cerebrospinal fluid levels of Amyloid Beta-42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline of Parkinson’s disease even in its later stages. The exploitation of additional biomarkers to the alpha synuclein seed amplification assay will therefore greatly add to our ability to plan trials and assess disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson’s disease. However, correlation with clinical progression does not necessarily equate to causation and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson’s disease

Towards frugal unsupervised detection of subtle abnormalities in medical imaging

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-o$\hookleftarrow$ between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design e$\hookleftarrow$ort or tuning. Their expressivity makes them good candidates to account for complex multivariate reference models. Their much smaller number of parameters makes them more amenable to interpretation and e cient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale

Magnetic resonance imaging techniques for diagnostics in Parkinson’s disease and atypical parkinsonism

Background: Parkinson’s disease (PD) is a neurodegenerative disease characterized by rigidity, hypokinesia, tremor and postural instability. PD is a clinical diagnosis based on neurological examination, patient history and treatment response. Similar symptoms can be caused by other movement disorders such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), making it difficult to clinically separate them in early stages. However, these diseases differ in underlying pathology, treatment and prognosis. PSP and MSA have more rapid deterioration and develop additional symptoms such as impaired eye movements or autonomic dysfunction. Magnetic resonance imaging (MRI) is commonly performed as part of the clinical work-up in patients presenting with parkinsonism. There are no overt changes on structural MRI in PD. In atypical parkinsonian syndromes there are typically no visible changes until later disease stages. Purpose: The aim of this thesis is to evaluate novel MRI techniques for diagnostics and for investigation of disease processes in Parkinson’s disease, PSP and MSA. Paper I: A retrospective cohort from Karolinska University Hospital (102 participants; 62 PD, 15 PSP, 11 MSA, 14 controls) was assessed using susceptibility mapping processed from susceptibility weighted imaging. We show that there is elevated susceptibility in the red nucleus and the globus pallidus in PSP compared to PD, MSA and controls. Higher susceptibility levels were also seen in MSA compared to PD in the putamen, and in PD compared to controls in the substantia nigra. Using the red nucleus susceptibility as a diagnostic biomarker, PSP could be separated from PD with an accuracy of 97% (based on the area under the receiver operating characteristic curve, AUC), from MSA with AUC 75% and from controls with AUC 98%. We concluded that susceptibility changes, particularly in the red nucleus in PSP, could be potential biomarkers for differential diagnostics in parkinsonism. Paper II: A prospective cohort from Lund, the BioFINDER study (199 participants; 134 PD, 11 PSP, 10 MSA, 44 controls), was investigated using the susceptibility mapping pipeline developed for Paper I. The finding from Paper I with elevated susceptibility in the red nucleus was validated for PSP compared to PD, MSA and controls. The elevated putaminal susceptibility was also confirmed in MSA compared to PD. The potential role of red nucleus susceptibility as a biomarker for separating PSP from PD and MSA was also similar to the results in Paper I, with AUC 98% for separating PSP from PD and AUC 96% for separating PSP from MSA. We concluded that we could confirm our previous findings from Paper I, with the red nucleus susceptibility being a potential biomarker for separating PSP from PD and MSA. Paper III: A retrospective cohort from Karolinska University Hospital (196 participants; 140 PD, 29 PSP, 27 MSA) was evaluated to employ automated volumetric brainstem segmentation using FreeSurfer. The volumetric approach was compared to manual planimetric measurements: midbrain-pons ratio, magnetic resonance parkinsonism index 1.0 and 2.0. Intra- and inter-scanner as well as intra- and inter-rater reliability were calculated. We found good repeatability in both automated volumetric and manual planimetric measurements. Normalized midbrain volume performed better than the planimetric measurements for separating PSP from PD. We concluded that, if further developed and incorporated in a radiology workflow, automated brainstem volumetry could increase availability of brainstem metrics and possibly save time for radiologists conducting manual measurements. Paper IV: Two cohorts, a retrospective from Karolinska University Hospital (184 participants; 129 PD, 28 PSP, 27 MSA) and a prospective from Lund (185 participants; 125 PD, 11 PSP, 8 MSA, 41 controls), were studied to investigate a new method of creating T1-/T2-weighted ratio images and its diagnostic capabilities in differentiating parkinsonian disorders. In the explorative retrospective cohort, differences in white matter normalized T1-/T2- weighted ratios were seen in the caudate nucleus, putamen, thalamus, subthalamic nucleus and red nucleus in PSP compared to PD; in the caudate nucleus and putamen in MSA compared to PD and in the subthalamic nucleus and the red nucleus in PSP compared to MSA. These differences were validated externally in the prospective cohort, where the changes could be confirmed in the subthalamic nucleus and the red nucleus in PSP compared to PD and MSA. We concluded that there are different patterns of white matter normalized T1-/T2-weighted ratio between the disorders and that this reflects differences in underlying pathophysiology. The T1-/T2-weighted ratio should be further investigated for better understanding of pathological processes in parkinsonian disorders and could possibly be utilized for diagnostic purposes if further developed

Functional MRI characterization of animal models of parkinsonism

Parkinson's disease (PD) is the second most common neurological disorder. It is characterized by the progressive development of motor symptoms - bradykinesia, resting tremor, muscular rigidity and difficulty with postural control - which serve as criterias for its clinical diagnosis. However, there is a need for biomarkers to detect PD early before the appearance of the symptoms, but also to evaluate efficacy of treatments. Such biomarkers would also to evaluate the translational value of models of the disease. In recent years, magnetic resonance imaging (MRI) has been used by researchers to identify biomarkers of PD in the patients' brain. One MRI method that is gradually becoming more popular is resting-state functional MRI (rs-fMRI). It consists in tracking the activity of brain by acquiring the MRI signal of the brain over time for several minutes while the patient is at rest, i.e. when he/she tries not to think about anything in particular. Compared to task-based fMRI, it is advantageous for studying PD as patients have problems to perform tasks, both because of motor symptoms but also cognitive symptoms which are common in PD. In this thesis, after successfully demonstrating the translational value of rs-fMRI by comparing a set of functional networks in naive Sprague-Dawley and healthy human subjects (paper I), several rat models of parkinsonism were characterized. These models consisted in a well-established model, the unilateral 6-hydroxydopamine (6-OHDA) model (paper II), and two progressive models of parkinsonism, the alpha-synuclein adeno-associated virus overexpression model, a genetic model (paper III), and the β-sitosterol-β-D-glucoside model, a new toxin-based model (paper IV). By acquiring rs-fMRI datasets and analysing them using seed-based correlation analysis, functional connectivity maps were generated. We could reproducibly demonstrate that sensorimotor corticostriatal functional connectivity is increased in the 6-OHDA lesioned animals compared to their control counterparts, while in models with milder parkinsonian pathology, the sensorimotor corticostriatal functional connectivity is decreased. We therefore emit the hypothesis that there is a U-shaped function describing corticostriatal functional connectivity relative to the level of striatal dopaminergic innervation. We also observed in both models of mild parkinsonism a reinforcement of negative functional connectivity between the prefrontal cortex, in particular the orbital cortex, and the primary somatosensory cortex compared to their healthy counterparts. These results demonstrate that rs-fMRI is a valid method to observe alterations in the brain related to parkinsonism in animals and that both motor and non-motor areas of the brain are affected by the loss of dopaminergic neurons. Further investigations must be conducted to understand the mechanisms involved in these changes and evaluate their translational value