Impact of perceptual learning on resting-state fMRI connectivity: A supervised classification study

International audiencePerceptual learning sculpts ongoing brain activity [1]. This finding has been observed by statistically comparing the functional connectivity (FC) patterns computed from resting-state functional MRI (rs-fMRI) data recorded before and after intensive training to a visual attention task. Hence, functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience. Following this line of research, we trained three groups of individuals to a visual discrimination task during a magneto-encephalography (MEG) experiment [2]. The same individuals were then scanned in rs-fMRI. Here, in a supervised classification framework, we demonstrate that FC metrics computed on rs-fMRI data are able to predict the type of training the participants received. On top of that, we show that the prediction accuracies based on tangent embedding FC measure outperform those based on our recently developed multivariate wavelet-based Hurst exponent estimator [3], which captures low frequency fluctuations in ongoing brain activity too

A Gaussian beam approach for computing Wigner measures in convex domains

A Gaussian beam method is presented for the analysis of the energy of the high frequency solution to the mixed problem of the scalar wave equation in an open and convex subset, with initial conditions compactly supported in this set, and Dirichlet or Neumann type boundary condition. The transport of the microlocal energy density along the broken bicharacteristic flow at the high frequency limit is proved through the use of Wigner measures. Our approach consists first in computing explicitly the Wigner measures under an additional control of the initial data allowing to approach the solution by a superposition of first order Gaussian beams. The results are then generalized to standard initial conditions

Oscillations haute fréquence en milieux élastiques bornés

Cette thèse est consacrée à l étude haute fréquence de problèmes de Dirichlet et Neumann pour le système de l élasticité. On y étudie le phénomène de réflexion au bord au moyen de deux techniques : la sommation de faisceaux gaussiens et les mesures de Wigner. Dans les chapitres 1 et 2, on commence par étudier le problème plus simple de l équation des ondes scalaire à une vitesse. Sous certaines hypothèses sur les conditions initiales, on construit des solutions approchées par superposition de faisceaux gaussiens. La justification de l asymptotique se fonde sur une estimation de normes de certains opérateurs intégraux à phases complexes. Pour des conditions initiales plus générales, on utilise les mesures de Wigner pour calculer la densité d énergie microlocale. On calcule explicitement les transformées de Wigner d intégrales de faisceaux gaussiens. Le comportement de la densité d énergie microlocale de la solution exacte se déduit de celui établi pour la solution approchée. Dans le chapitre 3, on utilise les résultats établis pour les sommes infinies de faisceaux gaussiens pour construire une solution approchée pour les équations de l élasticité et calculer sa densité d énergie microlocale. L existence de deux vitesses différentes dans le système de l élasticité introduit de nouvelles difficultés qui sont traitées dans ce chapitre.This thesis is devoted to the study of the high frequency Dirichlet and Neumann problems for the elasticity system. We study the reflection phenomenon at the boundary by means of two techniques: Gaussian beams summation and Wigner measures. In chapters 1 and 2, we start by studying the simpler problem of the scalar wave equation with one speed. Under some hypotheses on the initial conditions, we build an approximate solution by a Gaussian beams superposition. Justification of the asymptotics is based on norms estimate of some integral operators with complex phases. For more general initial conditions, we use Wigner measures to compute the microlocal energy density. We compute Wigner transforms of Gaussian beams integrals in an explicit way. The behaviour of the microlocal energy density for the exact solution is deduced from the one for the approximate solution. In chapter 3, we use the established results on infinite sums of Gaussian beams to build an approximate solution for the elasticity equations and to compute its microlocal energy density. We treat new difficulties arising from the existence of two different speeds in the elasticity system.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

White matter hyperintensities in Alzheimer’s disease: Topography of lesions and association with cognition: Neuroimaging/differential diagnosis

peer reviewe

Oscillations haute fréquence en milieux élastiques bornés

Cette thèse est consacrée à l’étude haute fréquence de problèmes de Dirichlet et Neumann pour le système de l’élasticité. On y étudie le phénomène de réflexion au bord au moyen de deux techniques : la sommation de faisceaux gaussiens et les mesures de Wigner. Dans les chapitres 1 et 2, on commence par étudier le problème plus simple de l’équation des ondes scalaire à une vitesse. Sous certaines hypothèses sur les conditions initiales, on construit des solutions approchées par superposition de faisceaux gaussiens. La justification de l’asymptotique se fonde sur une estimation de normes de certains opérateurs intégraux à phases complexes. Pour des conditions initiales plus générales, on utilise les mesures de Wigner pour calculer la densité d’énergie microlocale. On calcule explicitement les transformées de Wigner d’intégrales de faisceaux gaussiens. Le comportement de la densité d’énergie microlocale de la solution exacte se déduit de celui établi pour la solution approchée. Dans le chapitre 3, on utilise les résultats établis pour les sommes infinies de faisceaux gaussiens pour construire une solution approchée pour les équations de l’élasticité et calculer sa densité d’énergie microlocale. L’existence de deux vitesses différentes dans le système de l’élasticité introduit de nouvelles difficultés qui sont traitées dans ce chapitre.This thesis is devoted to the study of the high frequency Dirichlet and Neumann problems for the elasticity system. We study the reflection phenomenon at the boundary by means of two techniques: Gaussian beams summation and Wigner measures. In chapters 1 and 2, we start by studying the simpler problem of the scalar wave equation with one speed. Under some hypotheses on the initial conditions, we build an approximate solution by a Gaussian beams superposition. Justification of the asymptotics is based on norms estimate of some integral operators with complex phases. For more general initial conditions, we use Wigner measures to compute the microlocal energy density. We compute Wigner transforms of Gaussian beams integrals in an explicit way. The behaviour of the microlocal energy density for the exact solution is deduced from the one for the approximate solution. In chapter 3, we use the established results on infinite sums of Gaussian beams to build an approximate solution for the elasticity equations and to compute its microlocal energy density. We treat new difficulties arising from the existence of two different speeds in the elasticity system

Functional connectivity outperforms scale-free brain dynamics as fMRI predictive feature of perceptual learning underwent in MEG

Perceptual learning sculpts ongoing brain activity [1]. This finding has been observed by statistically comparing the functional connectivity (FC) patterns computed from resting-state functional MRI (rs-fMRI) data recorded before and after intensive training to a visual attention task. Hence, functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience. Following this line of research, we trained three groups of individuals to a visual discrimination task during a magneto-encephalography (MEG) experiment [2]. The same individuals were then scanned in rs-fMRI. Here, in a supervised classification framework, we demonstrate that FC metrics computed on rs-fMRI data are able to predict the type of training the participants received. On top of that, we show that the prediction accuracies based on tangent embedding FC measure outperform those based on our recently developed multivariate wavelet-based Hurst exponent estimator [3], which captures low frequency fluctuations in ongoing brain activity too

Sammba-MRI: A library for processing SmAll-MaMmal BrAin MRI data in Python

International audienceSmall-mammal neuroimaging offers incredible opportunities to investigate structural and functional aspects of the brain. Many tools have been developed in the last decade to analyse small animal data, but current softwares are less mature than the available tools that process human brain data. The Python package Sammba-MRI (SmAll-MaMmal BrAin MRI in Python; http://sammba-mri.github.io) allows flexible and efficient use of existing methods and enables fluent scriptable analysis workflows, from raw data conversion to multimodal processing

A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy

International audienceThe gray mouse lemur (Microcebus murinus) is a small prosimian of growing interest for studies of primate biology and evolution, and notably as a model organism of brain aging. As brain atlases are essential tools for brain investigation, the objective of the current work was to create the first 3D digital atlas of the mouse lemur brain. For this, a template image was constructed from in vivo magnetic resonance imaging (MRI) data of 34 animals. This template was then manually segmented into 40 cortical, 74 subcortical and 6 cerebro-spinal fluid (CSF) regions. Additionally, we generated probability maps of gray matter, white matter and CSF. The template, manual segmentation and probability maps, as well as imaging tools used to create and manipulate the template, can all be freely downloaded. The atlas was first used to automatically assess regional age-associated cerebral atrophy in a cohort of mouse lemurs previously studied by voxel based morphometry (VBM). Results based on the atlas were in good agreement with the VBM ones, showing age-associated atrophy in the same brain regions such as the insular, parietal or occipital cortices as well as the thalamus or hypothalamus. The atlas was also used as a tool for comparative neuroanatomy. To begin with, we compared measurements of brain regions in our MRI data with histology-based measures from a reference article largely used in previous comparative neuroanatomy studies. We found large discrepancies between our MRI-based data and those of the reference histology-based article. Next, regional brain volumes were compared amongst the mouse lemur and several other mammalian species where high quality volumetric MRI brain atlases were available, including rodents (mouse, rat) and primates (marmoset, macaque, and human). Unlike those based on histological atlases, measures from MRI atlases indicated similar cortical to cerebral volume indices in all primates, including in mouse lemurs, and lower values in mice. On the other hand, white matter to cerebral volume index increased from rodents to small primates (mouse lemurs and marmosets) to macaque, reaching their highest values in humans

Digital templates and brain atlas dataset for the mouse lemur primate

We present a dataset made of 3D digital brain templates and of an atlas of the gray mouse lemur (Microcebus murinus), a small prosimian primate of growing interest for studies of primate biology and evolution. A template image was constructed from in vivo magnetic resonance imaging (MRI) data of 34 animals. This template was then manually segmented into 40 cortical, 74 subcortical and 6 cerebro-spinal fluid (CSF) regions. Additionally, the dataset contains probability maps of gray matter, white matter and CSF. The template, manual segmentation and probability maps can be downloaded in NIfTI-1 format at https://www.nitrc.org/projects/mouselemuratlas. Further construction and validation details are given in “A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy” (Nadkarni et al., 2018) [1], which also presents applications of the atlas such as automatic assessment of regional age-associated cerebral atrophy and comparative neuroanatomy studies

Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla

International audienceMeasures of resting-state functional connectivity allow the description of neuronal networks in humans and provide a window on brain function in normal and pathological conditions. Characterizing neuronal networks in animals is complementary to studies in humans to understand how evolution has modelled network architecture. The mouse lemur (Microcebus murinus) is one of the smallest and more phylogenetically distant primates as compared to humans. Characterizing the functional organization of its brain is critical for scientists studying this primate as well as to add a link for comparative animal studies. Here, we created the first functional atlas of mouse lemur brain and describe for the first time its cerebral networks. They were classified as two primary cortical networks (somato-motor and visual), two high-level cortical networks (fronto-parietal and fronto-temporal) and two limbic networks (sensory-limbic and evaluative-limbic). Comparison of mouse lemur and human networks revealed similarities between mouse lemur high-level cortical networks and human networks as the dorsal attentional (DAN), executive control (ECN), and default-mode networks (DMN). These networks were however not homologous, possibly reflecting differential organization of high-level networks. Finally, cerebral hubs were evaluated. They were grouped along an antero-posterior axis in lemurs while they were split into parietal and frontal clusters in humans