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Current Research Interest and projects



Journal Article

  • BESSON P., CARRIÈRE N., BANDT S. K., TOMMASI M., LECLERC X., DERAMBURE P., LOPES R., TYVAERT L. “Whole-Brain High-Resolution Structural Connectome: Inter-Subject Validation and Application to the Anatomical Segmentation of the Striatum.”. Brain Topography [En ligne]. 2017. Disponible sur : < > (consulté le no date)
    Résumé : The present study describes extraction of high-resolution structural connectome (HRSC) in 99 healthy subjects, acquired and made available by the Human Connectome Project. Single subject connectomes were then registered to the common surface space to allow assessment of inter-individual reproducibility of this novel technique using a leave-one-out approach. The anatomic relevance of the surface-based connectome was examined via a clustering algorithm, which identified anatomic subdivisions within the striatum. The connectivity of these striatal subdivisions were then mapped on the cortical and other subcortical surfaces. Findings demonstrate that HRSC analysis is robust across individuals and accurately models the actual underlying brain networks related to the striatum. This suggests that this method has the potential to model and characterize the healthy whole-brain structural network at high anatomic resolution.
    Mots-clés : Connectome, Diffusion Magnetic Resonance Imaging, High-resolution, Striatum clustering, Surface-based connectivity.


Journal Article

  • MASSIRE A., TASO M., BESSON P., GUYE M., RANJEVA J. - P., CALLOT V. “High-resolution multi-parametric quantitative magnetic resonance imaging of the human cervical spinal cord at 7T.”. NeuroImage [En ligne]. 2016. Vol. 143, p. 58-69. Disponible sur : < > (consulté le no date)
    Résumé : Quantitative MRI techniques have the potential to characterize spinal cord tissue impairments occurring in various pathologies, from both microstructural and functional perspectives. By enabling very high image resolution and enhanced tissue contrast, ultra-high field imaging may offer further opportunities for such characterization. In this study, a multi-parametric high-resolution quantitative MRI protocol is proposed to characterize in vivo the human cervical spinal cord at 7T. Multi-parametric quantitative MRI acquizitions including T1, T2(*) relaxometry mapping and axial diffusion MRI were performed on ten healthy volunteers with a whole-body 7T system using a commercial prototype coil-array dedicated to cervical spinal cord imaging. Automatic cord segmentation and multi-parametric data registration to spinal cord templates enabled robust regional studies within atlas-based WM tracts and GM horns at the C3 cervical level. T1 value, cross-sectional area and GM/WM ratio evolutions along the cervical cord were also reported. An original correction method for B1(+)-biased T1 mapping sequence was additionally proposed and validated on phantom. As a result, relaxometry and diffusion parameters derived from high-resolution quantitative MRI acquizitions were reported at 7T for the first time. Obtained images, with unmatched resolutions compared to lower field investigations, provided exquisite anatomical details and clear delineation of the spinal cord substructures within an acquisition time of 30min, compatible with clinical investigations. Regional statistically significant differences were highlighted between WM and GM based on T1 and T2* maps (p<10(-3)), as well as between sensory and motor tracts based on diffusion tensor imaging maps (p<0.05). The proposed protocol demonstrates that ultra-high field spinal cord high-resolution quantitative MRI is feasible and lays the groundwork for future clinical investigations of degenerative spinal cord pathologies.
    Mots-clés : crmbm, diffusion tensor imaging, Quantitative MRI, Relaxometry mapping, Spinal Cord, Template-based segmentation, Ultra-high field.


Journal Article

  • BESSON P., LOPES R., LECLERC X., DERAMBURE P., TYVAERT L. “Intra-subject reliability of the high-resolution whole-brain structural connectome.”. NeuroImage [En ligne]. 2014. Vol. 102 Pt 2, p. 283-293. Disponible sur : < > (consulté le no date)
    Résumé : Recent advances in diffusion weighted image acquisition and processing allow for the construction of anatomically highly precise structural connectomes. In this study, we introduce a method to compute high-resolution whole-brain structural connectome. Our method relies on cortical and subcortical triangulated surface models, and on a large number of fiber tracts generated using a probabilistic tractography algorithm. Each surface triangle is a node of the structural connectivity graph while edges are fiber tract densities across pairs of nodes. Surface-based registration and downsampling to a common surface space are introduced for group analysis whereas connectome surface smoothing aimed at improving whole-brain network estimate reliability. Based on 10 datasets acquired from a single healthy subject, we evaluated the effects of repeated probabilistic tractography, surface smoothing, surface registration and downsampling to the common surface space. We show that, provided enough fiber tracts and surface smoothing, good to excellent intra-acquisition reliability could be achieved. Surface registration and downsampling efficiently established triangle-to-triangle correspondence across acquisitions and high inter-acquisition reliability was obtained. Computational time and disk/memory usages were monitored throughout the steps. Although further testing on large cohort of subjects is required, our method presents the potential to accurately model whole-brain structural connectivity at high-resolution.
    Mots-clés : Adult, Algorithms, Brain, Connectome, diffusion tensor imaging, Female, Humans, Image Interpretation, Computer-Assisted, Reproducibility of Results.

  • LOPES R., MOELLER F., BESSON P., OGEZ F., SZURHAJ W., LECLERC X., SINIATCHKIN M., CHIPAUX M., DERAMBURE P., TYVAERT L. “Study on the Relationships between Intrinsic Functional Connectivity of the Default Mode Network and Transient Epileptic Activity.”. Frontiers in Neurology [En ligne]. 2014. Vol. 5, p. 201. Disponible sur : < > (consulté le no date)
    Résumé : RATIONALE: Simultaneous recording of electroencephalogram and functional MRI (EEG-fMRI) is a powerful tool for localizing epileptic networks via the detection of hemodynamic changes correlated with interictal epileptic discharges (IEDs). fMRI can be used to study the long-lasting effect of epileptic activity by assessing stationary functional connectivity during the resting-state period [especially, the connectivity of the default mode network (DMN)]. Temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) are associated with low responsiveness and disruption of DMN activity. A dynamic functional connectivity approach might enable us to determine the effect of IEDs on DMN connectivity and to better understand the correlation between DMN connectivity changes and altered consciousness. METHOD: We studied dynamic changes in DMN intrinsic connectivity and their relation to IEDs. Six IGE patients (with generalized spike and slow-waves) and 6 TLE patients (with unilateral left temporal spikes) were included. Functional connectivity before, during, and after IEDs was estimated using a sliding window approach and compared with the baseline period. RESULTS: No dependence on window size was observed. The baseline DMN connectivity was decreased in the left hemisphere (ipsilateral to the epileptic focus) in TLEs and was less strong but remained bilateral in IGEs. We observed an overall increase in DMN intrinsic connectivity prior to the onset of IEDs in both IGEs and TLEs. After IEDs in TLEs, we found that DMN connectivity increased before it returned to baseline values. Most of the DMN regions with increased connectivity before and after IEDs were lateralized to the left hemisphere in TLE (i.e., ipsilateral to the epileptic focus). CONCLUSION: RESULTS suggest that DMN connectivity may facilitate IED generation and may be affected at the time of the IED. However, these results need to be confirmed in a larger independent cohort.
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