Improving accuracy and power with transfer learning using a meta-analytic database

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e. to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.Comment: MICCAI, Nice : France (2012

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Dimensionnement d'un générateur photovoltaïque pour un système communicant autonome

National audiencePour rendre autonome un système communicant utilisant la technologie bluetooth, nous avons choisi de l'équiper d'un générateur photovoltaïque. Pour dimensionner ce générateur, il faut tenir compte de la consommation énergétique journalière, des caractéristiques de l'accumulateur à charger, de l'encombrement disponible et des caractéristiques d'ensoleillement. Nous donnons ainsi dans cet article un exemple de dimensionnement pour un système situé en extérieur dans le sud de la France

Radiation damages in CMOS image sensors: testing and hardening challenges brought by deep sub-micrometer CIS processes

This paper presents a summary of the main results we observed after several years of study on irradiated custom imagers manufactured using 0,18 µm CMOS processes dedicated to imaging. These results are compared to irradiated commercial sensor test results provided by the Jet Propulsion Laboratory to enlighten the differences between standard and pinned photodiode behaviors. Several types of energetic particles have been used (gamma rays, X-rays, protons and neutrons) to irradiate the studied devices. Both total ionizing dose (TID) and displacement damage effects are reported. The most sensitive parameter is still the dark current but some quantum eficiency and MOSFET characteristics changes were also observed at higher dose than those of interest for space applications. In all these degradations, the trench isolations play an important role. The consequences on radiation testing for space applications and radiation-hardening-by-design techniques are also discussed

Capacités de mobilité, ancrage et rapport à l’espace des jeunes des classes populaires rurales

Le séminaire « Théories, sources et méthodes » a fait sa rentrée au laboratoire Migrinter le 19 janvier 2017, sous l’impulsion de Naïk Miret, Celio Sierra-Paycha et Marie-Françoise Valette. Outre le fait de réunir l’équipe une fois par mois, ce rendez-vous important de la vie du laboratoire permet d’élargir les réflexions relatives aux théories des migrations. Lors de cette première séance de reprise, Thomas Venet est venu présenter les résultats de sa thèse en sociologie et démographie qu’il..

Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies.

International audienceGroup studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses

Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses.

International audienceThe aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies