References of "Garraux, Gaëtan"
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See detailQuestion Intégrative - Médecine - Module Système Nerveux
Pasquet, Coralie ULg; Van de Poël, Jean-François ULg; Schaffer, Patrick ULg et al

Poster (2015, May 18)

Au cours du premier quadrimestre de l’année académique 2014 - 2015, une nouvelle activité a été proposée aux 270 étudiants inscrits en 3ième année du grade de Bachelier en Médecine à l’Université de Liège ... [more ▼]

Au cours du premier quadrimestre de l’année académique 2014 - 2015, une nouvelle activité a été proposée aux 270 étudiants inscrits en 3ième année du grade de Bachelier en Médecine à l’Université de Liège. Cette activité a été réalisée dans le cadre du « Module Système Nerveux ». [less ▲]

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See detailProtocol Optimization for Alpha–Synuclein Immuno-Labeling in the Autonomous Nervous System in Parkinson’s Disease
Pasquet, Coralie ULg; Garraux, Gaëtan ULg; Borgs, Laurence ULg et al

Scientific conference (2015, January 27)

Reviewing and testing of published protocols for the immuno-labelling of α–synuclein in the autonomous nervous system in Parkinson’s disease.

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See detailEffects of α-synuclein levels on cerebral synaptic function: Validation of a novel PET radioligand for the early diagnosis of Parkinson’s disease
Tarragon Cros, Ernesto ULg; Ferrara, André ULg; Tirelli, Ezio ULg et al

Poster (2015, January 27)

Background In Parkinson’s disease, converging evidence supports a pathogenic role for excessive α–synuclein accumulation in synaptic terminals that may propagate back to the soma of vulnerable nerve cells ... [more ▼]

Background In Parkinson’s disease, converging evidence supports a pathogenic role for excessive α–synuclein accumulation in synaptic terminals that may propagate back to the soma of vulnerable nerve cells such as neurons in the substantia nigra pars compacta. The resulting loss of dopaminergic terminals in the striatum can be demonstrated in vivo using 18F-Dopa-PET (positron emission tomography). However, there’s currently no validated biomarker of the progressive synaptic dysfunction in other vulnerable areas such as the cerebral cortex. Goal In this longitudinal study, we will test the hypothesis that the loss of synaptic terminals in a mouse model of excessive α–synuclein accumulation can be demonstrated in vivo before the occurrence of behavioural disturbances using 18F-UCB-H, a new PET biomarker developed at CRC. We will also test if this new imaging modality is sensitive enough to study the effect of a disease modifying therapy such as chronic physical exercise. Methods We will use microPET for the in vivo quantification of 18F-UCB-H brain uptake in 16 wild type animals and 16 transgenic (Tg) mice overexpressing human α–syn under the mThy1 promotor every 2 months. Data will be validated against post-mortem analyses after the last PET study. Predictions We predict decreased tracer uptake over time in the basal ganglia and cerebral cortex in Tg mice as compared with WT animals. Also, we predict a relationship between 18F-UCB-H uptake levels in basal ganglia and cerebral cortex and progressive alterations in both motor and cognitive functions, respectively. Further, we also expect that chronic exercise will slow down both motor and cognitive disturbances, as well as the rate of 18F-UCB-H brain uptake decreases. Conclusion If 18F-UCB-H PET proves to be a valid biomarker for the early detection of α–synuclein accumulation in the pre-clinical model of PD, the methods will tested on human clinical populations. [less ▲]

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See detailDéveloppement de nouveaux marqueurs neuroradiologiques de la maladie de Parkinson par reconnaissance de motifs
Himri, Khadidja ULg; Depierreux, Frédérique ULg; GARRAUX, Gaëtan ULg

Poster (2015, January 27)

Background and objectives: Automatic classification of Parkinson’s disease (PD) versus healthy controls (HC) based on structural MRI has so far focused on unimodal approaches. However, this method is ... [more ▼]

Background and objectives: Automatic classification of Parkinson’s disease (PD) versus healthy controls (HC) based on structural MRI has so far focused on unimodal approaches. However, this method is subject to a poor temporal and spatial resolution leading to low classification accuracy. To overcome this limitation we propose to integrate different modalities by generating a single decision function based on a multi-kernel method, exploiting the complementary information it offers. We predict that the integration of multiple modalities produces greater classification enhancement. Materials and methods: 3Tesla MRI was acquired in 42 patients with PD and 42 age and gender matched healthy controls. We relied on Unified Parkinson’s Disease Rating Scale (UPDRS) for evaluating the clinical status. We used structural and quantitative maps of T1, T2*, proton density (PD), magnetization transfer (MT), Multi-parameter (MT magnetization transfer, proton density (A), Iron Deposit (R2 *), mixing water content, iron, and the fraction of macromolecules tissues (R1) at 1 × 1 × 1 mm3 resolution. We identified cortical and subcortical brain regions (cortex, putamen, globus pallidus, substantia nigra), and cortical grey matter. We applied existing classification algorithms in the field of neuroscience using a classification algorithm based on Support Vector Machines (SVMs) [1], executed using the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) [2]. The processes of classification was the following, data were mean centered and leave one subject out cross-validation was performed, making the test set independent from the training set. Analyses were restricted to voxels where all subjects had non-zero values. Statistical significance of the classifications was tested using permutation testing (1000 permutations) with random assignment of group class to the input image. Subsequently, we combined different modalities (MT, A, R1, R2) and identified the combination giving the highest sensitivity and sensibility in PD classification. As classifier we used support vector machines that are inspired by statistical learning theory Vladimir Vapnik and Multiple Kernel Learning approach, introduced by Lanckriet [3],[4]. Our approach can be seen as an analogue of MKL with SVMs. Conclusion & Future work: Identification of brain areas with affected intensity in the Parkinson’s group compared to Healthy Controls in single modalities using pronto is helpful. However, the subsequent multi-kernel approach utilizes unimodal information in a combined fashion so that emergent information is obtained, transcending effectiveness unimodal approaches. In conclusion, our findings suggest that combining different imaging modalities and different regions of interest increase classification accuracy significantly. These results are promising for objective diagnosis in medical practice. [less ▲]

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See detailComparison of brain functional connectivity methods for the diagnosis of Parkinson’s disease using resting state fMRI
Baquero Duarte, Katherine Andrea ULg; Rouillard, Maud; Depierreux, Frédérique ULg et al

Scientific conference (2015, January 27)

Background In the absence of validated biomarkers, the early diagnosis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide [1], is challenging and is prone to low ... [more ▼]

Background In the absence of validated biomarkers, the early diagnosis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide [1], is challenging and is prone to low accuracy [2]. Recent evidence suggests that the average pattern of functional connectivity (FC) between the basal ganglia and cerebral cortex assessed in the resting state using functional magnetic resonance imaging (rs-fMRI) might discriminate between mild PD and healthy controls with 85% overall accuracy [3]. Goal We will test if this finding can be replicated in our population. We will also compare the diagnosis accuracy of this approach, which depicts an average pattern of connectivity during the whole scanning period, with that of dynamic FC that investigates the spontaneous fluctuations of the pattern of connectivity over the scanning period [4]. Methods We are currently processing and analyzing rs-fMRI data prospectively acquired on a 3T MRI in 39 patients with PD (mean disease duration 5.4 years; mean Hoehn and Yahr stage 1.5) and 39 healthy controls matched for age, gender and levels of education. For dynamic FC we will compare two different methods [4], one that use slice-time windows to capture brain dynamics with another that captures spatial co-activation patters (CAPs) at specific time points. Conclusion The selected methods will be further validated in a new cohort of de novo drug-naïve PD patients. [1] Tessitore, A., et al. Sensorimotor connectivity in Parkinson’s disease: the role of functional neuroimaging. Frontiers in neurology 5 (2014). [2] Adler et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease. Clinicopathologic study. Neurology 2014;83:406–412 [3] Szewczyk-Krolikowski, K., et al. Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neurology 83.3 (2014): 208-214. [4] Hutchison, M., et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80 (2013): 360-378. [less ▲]

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See detailDevelopment and validation of an accelerometer-based method for quantifying gait events
Boutaayamou, Mohamed ULg; Schwartz, Cédric ULg; Stamatakis, Julien et al

in Medical Engineering & Physics (2015)

An original signal processing algorithm is presented to automatically extract, on a stride-by-stride basis, four consecutive fundamental events of walking, heel strike (HS), toe strike (TS), heel-off (HO ... [more ▼]

An original signal processing algorithm is presented to automatically extract, on a stride-by-stride basis, four consecutive fundamental events of walking, heel strike (HS), toe strike (TS), heel-off (HO), and toe-off (TO), from wireless accelerometers applied to the right and left foot. First, the signals recorded from heel and toe three-axis accelerometers are segmented providing heel and toe flat phases. Then, the four gait events are defined from these flat phases. The accelerometer-based event identification was validated in seven healthy volunteers and a total of 247 trials against reference data provided by a force plate, a kinematic 3D analysis system, and video camera. HS, TS, HO, and TO were detected with a temporal accuracy ± precision of 1.3 ms ± 7.2 ms, ‒4.2 ms ± 10.9 ms, ‒3.7 ms ± 14.5 ms, and ‒1.8 ms ± 11.8 ms, respectively, with the associated 95% confidence intervals ranging from ‒6.3 ms to 2.2 ms. It is concluded that the developed accelerometer-based method can accurately and precisely detect HS, TS, HO, and TO, and could thus be used for the ambulatory monitoring of gait features computed from these events when measured concurrently in both feet. [less ▲]

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See detailContribution of a Trunk Accelerometer System to the Characterization of Gait in Patients With Mild-to-Moderate Parkinson’s Disease
Demonceau, Marie ULg; Donneau, Anne-Françoise ULg; CROISIER, Jean-Louis ULg et al

in IEEE Journal of Biomedical and Health Informatics (2015)

OBJECTIVE: Gait disturbances like shuffling and short steps are obvious at visual observation in patients with advanced Parkinson's disease (PD). However, quantitative methods are increasingly used to ... [more ▼]

OBJECTIVE: Gait disturbances like shuffling and short steps are obvious at visual observation in patients with advanced Parkinson's disease (PD). However, quantitative methods are increasingly used to evaluate the wide range of gait abnormalities that may occur over the disease course. The goal of this study was to test the ability of a trunk accelerometer system to quantify the effects of PD on several gait features when walking at self-selected speed. METHODS: We recruited 96 subjects split into three age-matched groups: 32 healthy controls (HC), 32 PD patients at Hoehn and Yahr stage < II (PD-1), and 32 patients at Hoehn & Yahr stage II-III (PD-2). The following outcomes were extracted from the signals of the tri-axial accelerometer worn on the lower back: stride length, cadence, regularity index, symmetry index and mechanical powers yielded in the cranial-caudal, antero-posterior and medial-lateral directions. Walking speed was measured using a stopwatch. RESULTS: beside other gait features, the PD-1 and the PD-2 groups showed significantly reduced stride length normalized to height (p<0.02) and symmetry index (p<0.009) in comparison to the HC. Regularity index was the only feature significantly decreased in the PD-2 group as compared with the two other groups (p<0.01). The clinical relevance of this finding was supported by significant correlations with mobility and gait scales (r is around -0.3; p<0.05). CONCLUSION: Gait quantified by a trunk accelerometer may provide clinically useful information for the screening and follow-up of PD patients. [less ▲]

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See detailDevelopment and validation of a 3D kinematic-based method for determining gait events during overground walking
Boutaayamou, Mohamed ULg; Schwartz, Cédric ULg; Denoël, Vincent ULg et al

in IEEE International Conference on 3D Imaging (IC3D) (2014, December 09)

A new signal processing algorithm is developed for quantifying heel strike (HS) and toe-off (TO) event times solely from measured heel and toe coordinates during overground walking. It is based on a rough ... [more ▼]

A new signal processing algorithm is developed for quantifying heel strike (HS) and toe-off (TO) event times solely from measured heel and toe coordinates during overground walking. It is based on a rough estimation of relevant local 3D position signals. An original piecewise linear fitting method is applied to these local signals to accurately identify HS and TO times without the need of using arbitrary experimental coefficients. We validated the proposed method with nine healthy subjects and a total of 322 trials. The extracted temporal gait events were compared to reference data obtained from a force plate. HS and TO times were identified with a temporal accuracy ± precision of 0.3 ms ± 7.1 ms, and –2.8 ms ± 7.2 ms in comparison with reference data defined with a force threshold of 10 N. This algorithm improves the accuracy of the HS and TO detection. Furthermore, it can be used to perform stride-by-stride analysis during overground walking with only recorded heel and toe coordinates. [less ▲]

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See detailMapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease
Ziegler, Erik ULg; Rouillard, Maud; André, Elodie et al

in NeuroImage (2014), 99

Highlights 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 ... [more ▼]

Highlights 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. [less ▲]

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See detailValidation des paramètres de marche par un système accélérométrique (Locométrix*) à l'aide d'un système opto-électronique 3D (Coda Motion )
GILLAIN, Sophie ULg; Schwartz, Cédric ULg; Boutaayamou, Mohamed ULg et al

in Gériatrie et Psychologie Neuropsychiatrie du Vieillissement (2014), 12(supplément 3),

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See detailQuantitative multi-parameter mapping in parkinson’s disease: preliminary results
Rouillard, Maud ULg; D'Ostilio, Kevin ULg; Albinet, Cedric et al

Poster (2014, May)

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See detailBiased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions
Noirhomme, Quentin ULg; Lesenfants, Damien ULg; Gomez, Francisco et al

in NeuroImage: Clinical (2014), 4

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a ... [more ▼]

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with crossvalidation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the crossvalidation was further illustrated on real-data from a brain–computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson’s disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation. [less ▲]

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See detailValidation des paramètres de marche par un système accélérométrique (Locométrix ®) à l’aide d’un système opto-électronique 3D (Coda Motion ®)
GILLAIN, Sophie ULg; Schwartz, C; Dramé, M et al

in Cahiers de l'Année Gérontologique (Les) (2014), 6(Suppl. 1), 186

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See detail3D analysis of gait using accelerometer measurements
Boutaayamou, Mohamed ULg; Schwartz, Cédric ULg; Stamatakis, Julien et al

Scientific conference (2013, November 07)

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