Shanoir: Software as a Service Environment to Manage Population Imaging Research Repositories

International audienceSome of the major concerns of researchers and clinicians involved in popu- lation imaging experiments are on one hand, to manage the huge quantity and diversi- ty of produced data and, on the other hand, to be able to confront their experiments and the programs they develop with peers. In this context, we introduce Shanoir, a “Software as a Service” (SaaS) environment that offers cloud services for managing the information related to population imaging data production in the context of clini- cal neurosciences. We show how the produced images are accessible through the Sha- noir Data Management System, and we describe some of the data repositories that are hosted and managed by the Shanoir environment in different contexts

Evaluation of functional arterial spin labeling data using a perfusion template

International audiencepas de résum

Patch-based super-resolution for arterial spin labeling MRI

International audiencen

Quantification de la fraction d’eau piégée dans la myéline – Faisabilité et évaluation clinique en SEP

National audienc

Quantification of Myelin Degeneration in Multiple Sclerosis within Clinical Scan Times

International audienc

Symmetric Block-Matching Registration for the Distortion Correction of Echo-Planar Images

International audienc

Simultaneous Estimation of T1, T2 and B1 Maps From Relaxometry MR Sequences

International audienceInterest in quantitative MRI and relaxometry imaging is rapidly increasing to enable the discovery of new MRI disease imaging biomarkers. While DESPOT1 is a robust method for rapid whole-brain voxel-wise mapping of the longitudinal relaxation time (T1), the approach is inherently sensitive to inaccuracies in the transmitted flip angles, de- fined by the B1 inhomogeneity field, which become more severe at high field strengths (e.g., 3T). We propose a new approach for simultaneously mapping the B1 field, M0 (proton density), T1 and T2 relaxation times based on regular fast T1 and T2 relaxometry sequences. The new method is based on the intrinsic correlation between the T1 and T2 relaxometry sequences to jointly estimate all maps. It requires no additional sequence for the B1 correction. We evaluated our proposed algorithm on simulated and in-vivo data at 3T, demonstrating its improved accuracy with respect to regular separate estimation methods

Patch-based super-resolution for arterial spin labeling MRI

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Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback

Hybrid EEG and fMRI platform for multi-modal neurofeedback

International audienceNeurofeedback (NFB) relies on neurosignals for the estimation of brain activity. There exist a widevariety of NFB applications that use one type of neurosignals like fMRI or electroencephalography(EEG). Recently, the combination of two or more neurosignals has been receiving a lot of attentionin the research community, but still very few multi-modal NFB applications exist. This is primarilybecause of the lack of commercial multi-modal NFB systems and the associated technicaldifficulties in building them.Here we are going to describe a bi-modal EEG and fMRI NFB platform that we have build in ourlab. Our platform is designed to maximize modularity and parallel processing in order to be ableto provide real-time NFB with high level of synchronization and minimal delays. We havesuccessfully used our platform to conduct over 100 uni-modal and bi-modal NFB experimentswith more than 30 healthy subjects