Analyse spectrale de la complexité du cortex cérébral

La complexité de la forme de la surface est une caractéristique morphologique des surfaces pliées. Dans cette thèse, nous visons à développer des méthodes spectrales pour quantifier cette caractéristique du cortex cérébral humain reconstruit à partir d'images MR structurales. Tout d'abord, nous suggérons certaines propriétés qu'une mesure standard de la complexité de surface devrait posséder. Ensuite, nous proposons deux définitions claires de la complexité de la surface en fonction des propriétés de flexion de surface. Pour quantifier ces définitions, nous avons étendu la transformée de Fourier à fenêtres illustrée récemment pour transformer en maillage des surfaces. Grâce à certaines expériences sur les surfaces synthétiques, nous montrons que nos mesures basées sur la courbure permettent de surmonter les surfaces classiques basées sur la surface, ce qui ne distingue pas les plis profonds des oscillants ayant une surface égale. La méthode proposée est appliquée à une base de données de 124 sujets adultes en bonne santé. Nous définissons également la complexité de la surface par la régularité de Hölder des mouvements browniens fractionnés définis sur les collecteurs. Ensuite, pour la première fois, nous développons un algorithme de régression spectrale pour quantifier la régularité de Hölder d'une surface brownienne fractionnée donnée en estimant son paramètre Hurst H. La méthode proposée est évaluée sur un ensemble de sphères browniennes fractionnées simulées. En outre, en supposant que le cortex cérébral est une surface brownienne fractionnée, l'algorithme proposé est appliqué pour estimer les paramètres Hurst d'un ensemble de 14 corticus cérébraux fœtaux.Surface shape complexity is a morphological characteristic of folded surfaces. In this thesis, we aim at developing some spectral methods to quantify this feature of the human cerebral cortex reconstructed from structural MR images. First, we suggest some properties that a standard measure of surface complexity should possess. Then, we propose two clear definitions of surface complexity based on surface bending properties. To quantify these definitions, we extended the recently introduced graph windowed Fourier transform to mesh model of surfaces. Through some experiments on synthetic surfaces, we show that our curvature-based measurements overcome the classic surface area-based ones which may not distinguish deep folds from oscillating ones with equal area. The proposed method is applied to a database of 124 healthy adult subjects. We also define the surface complexity by the Hölder regularity of fractional Brownian motions defined on manifolds. Then, for the first time, we develop a spectral-regression algorithm to quantify the Hölder regularity of a given fractional Brownian surface by estimating its Hurst parameter H. The proposed method is evaluated on a set of simulated fractional Brownian spheres. Moreover, assuming the cerebral cortex is a fractional Brownian surface, the proposed algorithm is applied to estimate the Hurst parameters of a set of 14 fetal cerebral cortices

Analyse spectrale de la complexité du cortex cérébral

Surface shape complexity is a morphological characteristic of folded surfaces. In this thesis, we aim at developing some spectral methods to quantify this feature of the human cerebral cortex reconstructed from structural MR images. First, we suggest some properties that a standard measure of surface complexity should possess. Then, we propose two clear definitions of surface complexity based on surface bending properties. To quantify these definitions, we extended the recently introduced graph windowed Fourier transform to mesh model of surfaces. Through some experiments on synthetic surfaces, we show that our curvature-based measurements overcome the classic surface area-based ones which may not distinguish deep folds from oscillating ones with equal area. The proposed method is applied to a database of 124 healthy adult subjects. We also define the surface complexity by the Hölder regularity of fractional Brownian motions defined on manifolds. Then, for the first time, we develop a spectral-regression algorithm to quantify the Hölder regularity of a given fractional Brownian surface by estimating its Hurst parameter H. The proposed method is evaluated on a set of simulated fractional Brownian spheres. Moreover, assuming the cerebral cortex is a fractional Brownian surface, the proposed algorithm is applied to estimate the Hurst parameters of a set of 14 fetal cerebral cortices.La complexité de la forme de la surface est une caractéristique morphologique des surfaces pliées. Dans cette thèse, nous visons à développer des méthodes spectrales pour quantifier cette caractéristique du cortex cérébral humain reconstruit à partir d'images MR structurales. Tout d'abord, nous suggérons certaines propriétés qu'une mesure standard de la complexité de surface devrait posséder. Ensuite, nous proposons deux définitions claires de la complexité de la surface en fonction des propriétés de flexion de surface. Pour quantifier ces définitions, nous avons étendu la transformée de Fourier à fenêtres illustrée récemment pour transformer en maillage des surfaces. Grâce à certaines expériences sur les surfaces synthétiques, nous montrons que nos mesures basées sur la courbure permettent de surmonter les surfaces classiques basées sur la surface, ce qui ne distingue pas les plis profonds des oscillants ayant une surface égale. La méthode proposée est appliquée à une base de données de 124 sujets adultes en bonne santé. Nous définissons également la complexité de la surface par la régularité de Hölder des mouvements browniens fractionnés définis sur les collecteurs. Ensuite, pour la première fois, nous développons un algorithme de régression spectrale pour quantifier la régularité de Hölder d'une surface brownienne fractionnée donnée en estimant son paramètre Hurst H. La méthode proposée est évaluée sur un ensemble de sphères browniennes fractionnées simulées. En outre, en supposant que le cortex cérébral est une surface brownienne fractionnée, l'algorithme proposé est appliqué pour estimer les paramètres Hurst d'un ensemble de 14 corticus cérébraux fœtaux

Analytical prediction of switching losses in MOSFETs for variable drain-source voltage and current applications

This paper presents an analytical modeling of a MOSFET during switching operation, aimed at determining the switching losses. Moreover it compares the theoretical results with experimental data. The switching losses are calculated for different DC link voltages and load currents. The stray inductances and capacitances of the MOSFET were considered in the modeling. Besides the parasitic inductance in the circuit was calculated from the measurement and was applied in the loss modeling. It is shown that the switching waveforms obtained from the measurement are in agreement with the simulation results. However, due to the limitations of the drive circuit, the driver circuit output gate signal registered in the measurements had to be used in the simulations

Surface regularity via the estimation of fractional Brownian motion index

International audienceThe recent definition of fractional Brownian motions on surfaces has raised the statistical issue of estimating the Hurst index characterizing these models. To deal with this open issue, we propose a method which is a based on a spectral representation of surfaces built upon their Laplace-Beltrami operator. This method includes a first step where the surface supporting the motion is recovered using a mean curvature flow, and a second one where the Hurst index is estimated by linear regression on the motion spectrum. The method is evaluated on synthetic surfaces. The interest of the method is further illustrated on some fetal cortical surfaces extracted from MRI as a means to quantify the brain complexity during the gestational age. keywords fractional Brownian motions on surfaces, Laplace-Beltrami operator, Hurst index estimation, fetal cortical surface

Local Spectral Analysis of the Cerebral Cortex: New Gyrification Indices

International audienceGyrification index (GI) is an appropriate measure to quantify the complexity of the cerebral cortex. There is, however, no universal agreement on the notion of surface complexity and there are various methods in literature that evaluate different aspects of cortical folding. In this paper, we give two intuitive interpretations on folding quantification based on the magnitude and variation of the mean curvature of the cortical surface. We then present a local spectral analysis of the mean curvature to introduce two local gyrification indices that satisfy our interpretations. For this purpose, the graph windowed Fourier transform is extended to the framework of surfaces discretized with triangular meshes. An adaptive window function is also proposed to deal with the intersubject cortical size variability. The intrinsic nature of the method allows us to compute the degree of folding at different spatial scales. Our experiments show that while more classical surface area-based GIs may fail at differentiating deep folds from very convoluted ones, our spectral GIs overcome this issue. The method is applied to the cortical surfaces of 124 healthy adult subjects of OASIS database and average gyrification maps are computed and compared with other GI definitions. In order to illustrate the capacity of our method to capture and quantify important aspects of gyrification, we study the relationship between brain volume and cortical complexity, and design a scaling analysis with a power law model. Results indicate an allometric relation and confirm the well-known observations that larger brains are more folded. We also perform the scaling analysis at the vertex level to investigate how the degree of folding varies locally with the brain volume. Results reveal that in our healthy adult brain database, cortical regions which are the least folded on average show an increased folding complexity when brain size increases

Pronostiquer tôt les troubles du spectre autistique : Un défi ?

Les troubles du spectre de l’autisme (TSA) « naissent » in utero à la suite d’évènements pathologiques génétiques ou environnementaux. Le diagnostic des TSA n’est cependant effectué que vers l’âge de 3-5 ans en Europe et aux États-Unis. Un pronostic précoce permettrait pourtant d’atténuer la sévérité des atteintes cognitives, grâce à des approches psycho-éducatives. Une large panoplie d’approches a été suggérée pour établir un pronostic précoce des TSA, se fondant sur l’imagerie cérébrale, sur des enregistrements EEG, sur des biomarqueurs sanguins ou sur l’analyse des contacts visuels. Nous avons développé une approche fondée sur l’analyse par machine learning des données biologiques et échographiques recueillies en routine, du début de la grossesse au lendemain de la naissance, dans les maternités françaises. Ce programme qui permet d’identifier la presque totalité des bébés neurotypiques et la moitié des bébés qui auront un diagnostic de TSA quelques années plus tard, permet aussi d’identifier les paramètres ayant un impact sur le pronostic. Si quelques-uns d’entre eux étaient attendus, d’autres n’ont aucun lien avec les TSA. L’étude sans a priori des données de maternité devrait ainsi permettre un pronostic des TSA dès la naissance, ainsi que de mieux comprendre la pathogenèse de ces syndromes et de les traiter plus tôt

Effect of home-based exercise rehabilitation on quality of life early post-dischargeafter coronary artery bypass graft and percutaneous coronary intervention

BACKGROUND: The barriers to participation in cardiac rehabilitation programs are individualand economic problemsas well aslimited availability of rehabilitation services. Because of theimportant role of rehabilitation, home-basedexercise rehabilitation is a new approach toparticipate in such programs. The purpose of this study was to evaluate theeffect of home-basedrehabilitation on quality of life (QoL) in patients with coronary artery disease aftercoronaryartery bypass graft (CABG) and percutaneous coronary intervention (PCI).METHODS: Participants included 18 CABG (3 women, 15 men) and 40 PCI (12 women, 28men) low tomoderate risk patients. Finally, 17 patients in the exercise group and 16 patients inthe control group remained. The SF-36 was used to evaluate changes in QoL before and after theprogram.RESULTS: forty-three percent was dropped out from the program. Before and after program,the exercise group was betterin all domains of QoL (P &lt;0.05). After 8 weeks of cardiacrehabilitation, significant improvements were observed inquality of life in both groups (P&lt;0.05)but the exercise group showed more improvements in three domains.CONCLUSION: Home-based exercise rehabilitation after CABG and PCI may improve QoL andprovide an efficient low-costapproach to cardiac rehabilitation. It may be helpful due to limitedavailability and resources in Iran. Nevertheless,there is a need for more trainingto increaseparticipation and decrease drop outKeywords: Quality of life,Coronary artery bypass grafts, Angioplasty.</p

The graph windowed Fourier transform: a tool to quantify the gyrification of the cerebral cortex

International audienceThe Gyrification Index (GI) quantifies the amount of folding of a cortical surface. In this paper, we show the efficiency of spectral analysis to perform such a task, and in particular we explain how the graph windowed Fourier transform can be used as a tool to define new GIs with multiscale properties. We propose two different GIs and study their effect on a set of subjects whose cortical surfaces are modeled with triangular meshes

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

International audienceTo identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels