Neurofilament light levels predict clinical progression and death in multiple system atrophy

Disease-modifying treatments are currently being trialed in multiple system atrophy (MSA). Approaches based solely on clinical measures are challenged by heterogeneity of phenotype and pathogenic complexity. Neurofilament light chain protein has been explored as a reliable biomarker in several neurodegenerative disorders but data in multiple system atrophy have been limited. Therefore, neurofilament light chain is not yet routinely used as an outcome measure in MSA. We aimed to comprehensively investigate the role and dynamics of neurofilament light chain in multiple system atrophy combined with cross-sectional and longitudinal clinical and imaging scales and for subject trial selection. In this cohort study we recruited cross-sectional and longitudinal cases in multicentre European set-up. Plasma and cerebrospinal fluid neurofilament light chain concentrations were measured at baseline from 212 multiple system atrophy cases, annually for a mean period of 2 years in 44 multiple system atrophy patients in conjunction with clinical, neuropsychological and MRI brain assessments. Baseline neurofilament light chain characteristics were compared between groups. Cox regression was used to assess survival; ROC analysis to assess the ability of neurofilament light chain to distinguish between multiple system atrophy patients and healthy controls. Multivariate linear mixed effects models were used to analyse longitudinal neurofilament light chain changes and correlated with clinical and imaging parameters. Polynomial models were used to determine the differential trajectories of neurofilament light chain in multiple system atrophy. We estimated sample sizes for trials aiming to decrease NfL levels. We show that in multiple system atrophy, baseline plasma neurofilament light chain levels were better predictors of clinical progression, survival, and degree of brain atrophy than the NfL rate of change. Comparative analysis of multiple system atrophy progression over the course of disease, using plasma neurofilament light chain and clinical rating scales, indicated that neurofilament light chain levels rise as the motor symptoms progress, followed by deceleration in advanced stages. Sample size prediction suggested that significantly lower trial participant numbers would be needed to demonstrate treatment effects when incorporating plasma neurofilament light chain values into multiple system atrophy clinical trials in comparison to clinical measures alone. In conclusion, neurofilament light chain correlates with clinical disease severity, progression, and prognosis in multiple system atrophy. Combined with clinical and imaging analysis, neurofilament light chain can inform patient stratification and serve as a reliable biomarker of treatment response in future multiple system atrophy trials of putative disease-modifying agents.European Union’s Horizon 2020 research and innovation programm

Traitement des verbes (Etude neuropsychologique dans les pathologies sous-corticales)

TOULOUSE2-BUC Mirail (315552102) / SudocSudocFranceF

Landmark sequencing and route knowledge: An fMRI study

Introduction: The ability to navigate in a familiar environment mainly relies on route knowledge, that is, a mental representation of relevant locations along a way, sequenced according to a navigational goal. Despite the clear ecological validity of this issue, route navigation and route knowledge have been scarcely investigated and little is known about the neural and cognitive bases of this navigational strategy. Using functional magnetic resonance imaging (fMRI) we tested the validity of the predictions based on the main cognitive models of spatial knowledge acquisition about route-based navigation. Methods: An order judgment task was used with two conditions (route and activity). Subjects were required to detect potential mismatches between a current sensory input and expectations deriving from route and activity knowledge. Results: A medial occipto-temporal (e.g., lingual gyms, calcarine cortex, fusiform gyms, parahippocampal cortex) network was found activated during the route task, whereas a temporo-parietal (temporo-parietal junction) and frontal (e.g., Broca's area) network was related to the activity task. Conclusions: Functional data are congruent with cognitive models of route-based navigation. The route task activated areas related to both landmark identity and landmark order. Data are discussed in view of route-based navigation models. (C) 2011 Elsevier Ltd. All rights reserved

EVOLEX : apport de la reconnaissance vocale pour le diagnostic des dysfonctionnements cognitifs légers

National audienceLes troubles du langage (lexicaux et sémantiques), tout comme les troubles attentionnels, mnésiques ou des fonctions exécutives, révèlent précocement les dysfonctionnements cognitifs liés à de nombreuses atteintes pathologiques (maladies neurodégénératives, traumatismes crâniens), aux effets secondaires de certaines thérapeutiques (chimiothérapie ou radiothérapie cérébrale) ou au vieillissement. Plusieurs tests utilisant des réponses verbalisées par les patients sont ainsi utilisés pour explorer les facultés linguistiques et les fonctions exécutives à visée diagnostique : - les tests de fluence verbale sont une procédure courante comprenant ici deux tâches. La première est une fluence sémantique [1] : elle consiste à nommer des mots appartenant à la catégorie des animaux ou des fruits. La seconde correspond à de la fluidité phonémique [2] : il s'agit de nommer des mots commençant par les lettres R ou V. - lors de la tâche de dénomination d'image, les participants reçoivent une image très explicite (exemples : un igloo, un biberon, un chat) et doivent vocaliser le mot représenté sur l'image. - la tâche de génération verbale (ou association de mots) consiste à vocaliser le premier mot qui vient à l'esprit après avoir écouté un mot simple (exemples : fruit, peinture, igloo). Les progrès de la reconnaissance vocale permettent d'informatiser le recueil, le traitement et la production des résultats en gagnant en rapidité et en qualité d'analyse par rapport aux classiques passations «papier-crayon». Ainsi, le logiciel EVOLEX comprend un système qui reconnaît et analyse automatiquement la réponse vocale du sujet (patient). En effet, une reconnaissance automatique de la parole est réalisée et le temps de réponse est calculé (c'est-à-dire la période entre le début du stimulus et le début de la réponse orale du sujet). Cette transcription, actuellement basée sur Sphinx [3], utilise des modèles acoustiques du LIUM [4]. Les scores de reconnaissance avoisinent les 80 % et permettent une exploitation automatisée des résultats. Une interface web permet de corriger ce traitement automatique. EVOLEX permet une analyse fine en termes de qualité des réponses obtenues pour les tâches testées, i.e. de génération sémantique (qualité du lien sémantique) ou de fluence (groupements de mots et stratégie de changement de groupement, fréquence lexicale...). Cette utilisation de la reconnaissance vocale appliquée à des outils diagnostiques de maladies neurodégénératives ou de dysfonctionnements cognitifs permet une recherche translationnelle entre le monde de la clinique neurologique, orthophonique et neuropsychologique d'une part, et celui de la recherche fondamentale en psycholinguistique et en reconnaissance vocale d'autre part

Importance of Multimodal MRI in Characterizing Brain Tissue and Its Potential Application for Individual Age Prediction

This study presents a voxel-based multiple regression analysis of different magnetic resonance image modalities, including anatomical T1-weighted, T2∗ relaxometry, and diffusion tensor imaging. Quantitative parameters sensitive to complementary brain tissue alterations, including morphometric atrophy, mineralization, microstructural damage, and anisotropy loss, were compared in a linear physiological aging model in 140 healthy subjects (range 20–74 years). The performance of different predictors and the identification of the best biomarker of age-induced structural variation were compared without a priori anatomical knowledge. The best quantitative predictors in several brain regions were iron deposition and microstructural damage, rather than macroscopic tissue atrophy. Age variations were best resolved with a combination of markers, suggesting that multiple predictors better capture age-induced tissue alterations. The results of the linear model were used to predict apparent age in different regions of individual brain. This approach pointed to a number of novel applications that could potentially help highlighting areas particularly vulnerable to disease

Brain tissues atrophy is not always the best structural biomarker of physiological aging: A multimodal cross-sectional study

This study presents a voxel-based multiple regression analysis of different magnetic resonance image modalities, including anatomical T1-weighted, T2* relaxometry, and diffusion tensor imaging. Quantitative parameters sensitive to complementary brain tissue alterations, including morphometric atrophy, mineralization, microstructural damage, and anisotropy loss, were compared in a linear physiological aging model in 140 healthy subjects (range 20-74 years). The performance of different predictors and the identification of the best biomarker of age-induced structural variation were compared without a priori anatomical knowledge. The best quantitative predictors in several brain regions were iron deposition and microstructural damage, rather than macroscopic tissue atrophy. Age variations were best resolved with a combination of markers, suggesting that multiple predictors better capture age-induced tissue alterations. These findings highlight the importance of a combined evaluation of multimodal biomarkers for the study of aging and point to a number of novel applications for the method described

Visual interpretation of CNN decision-making process using Simulated Brain MRI

Convolutional neural networks (CNNs) are being extensively used to analyze medical images given the remarkable performances achieved so far. Due to the non-transparent decision-making process, CNNs are thought to be black boxes, so hindering their applicability. We submit a novel visualization technique to shed light on CNNs decisions in a classification task. Brain magnetic resonance images are fed as input to an original 3D CNN to allow discrimination of normal against modified brain data. This modification targets specific brain regions by linearly increasing their intensity, and involves regions with very different features in dimension, position, and enclosed tissues. The proposed visualization method merges all convolutional layers output in order to highlight where the model is “looking” during the decision-making process. Our visualizations allow to recover the same areas modified in the images, thus proving they are relevant to the prediction as expected. Comparing results from models with different accuracy, show that even in the case of low performance the expected regions are present in the activation maps leading the way to ameliorations of the CNN architecture

Atypical connectivity in the cortico-striatal network in NF1 children and its relationship with procedural perceptual-motor learning and motor skills

International audienceIntroduction: Neurofibromatosis type 1 (NF1) is considered a model of neurodevelopmental disorder because of the high frequency of learning deficits, especially developmental coordination disorder. In neurodevelopmental disorder, Nicolson and Fawcett formulated the hypothesis of an impaired procedural learning system that has its origins in cortico-subcortical circuits. Our aim was to investigate the relationship between cortico-striatal connectivity and procedural perceptual-motor learning performance and motor skills in NF1 children. Methods: Seventeen NF1 and 18 typically developing children aged between 8 and 12 years old participated in the study. All were right-handed and did not present intellectual or attention deficits. In all children, procedural perceptual-motor learning was assessed using a bimanual visuo-spatial serial reaction time task (SRTT) and motor skills using the Movement Assessment Battery for Children (M-ABC). All participants underwent a resting-state functional MRI session. We used a seed-based approach to explore cortico-striatal connectivity in somatomotor and frontoparietal networks. A comparison between the groups' striato-cortical connectivity and correlations between connectivity and learning (SRTT) and motor skills (M-ABC) were performed. Results: At the behavioral level, SRTT scores are not significantly different in NF1 children compared to controls. However, M-ABC scores are significantly impaired within 9 patients (scores below the 15th percentile). At the cerebral level, NF1 children present a higher connectivity in the cortico-striatal regions mapping onto the right angular gyrus compared to controls. We found that the higher the connectivity values between these regions, differentiating NF1 and controls, the lower the M-ABC scores in the whole sample. No correlation was found for the SRTT scores. Conclusion: NF1 children present atypical hyperconnectivity in cortico-striatal connections. The relationship with motor skills could suggest a sensorimotor dysfunction already found in children with developmental coordination disorder. These abnormalities are not linked to procedural perceptual-motor learning assessed by SRTT