Large Brain Nets

Team Presentation

Large Brain Nets is a bi-lateral project focused on "Characterizing Large-scale Brain Networks in Typical and Atypical Populations Using Novel Computational Methods for dMRI and fMRI-based Connectivity". The team is funded by the Inria Associate Teams programme within the Inria@SiliconValley project.

Research Goals

The major goal of this project is to develop and validate sophisticated computational tools for identifying functional nodes at the whole-brain level and measuring structural and functional connectivity between them, using state-of-the-art human brain MR imaging techniques and open-source datasets such as Human Connectome Project data. Our proposed methods will reveal in unprecedented detail the structural and functional connectivity of the human brain. Furthermore, our innovative computational approach to brain connectomics will help create the building blocks for shaping the next generation of research on brain function and psychopathology.

Partners

Parietal, Inria, France

• Demian Wassermann, (CR Inria, Principal Investigator)
• Guillermo Gallardo (PhD Student, Athena Inria Team)
• Antonia Machlouzarides-Shalit (PhD Student)

Stanford Cognitive & Systems Neuroscience Lab, Stanford Medical School, USA

• Vinod Menon (Professor Stanford Medical School, Principal Investigator)
• Daniel Arthur Abrams (Instructor)
• Kaustub Supekar (Research Scientist)
• John Kolchalka (Research Scientist)
• Lang Chen (Post Doc)
• Weidong Cai (Post Doc)

External Collaborators

• Jing-Rebecca Li, (CR Inria, DEFI Inria Team, France)
• Van-Dang Nguyen, (Ph.D. in Applied Maths, Ph.D. Cand. in CS KTH Sweden)
• Mark A. Pinsk, Princeton University

Publications

• Wassermann D, Nguyen VD, Gallardo G, Li JR, Cai W, Vinon M. Sensing Von Economo Neurons in the Insula with Multi-shell Diffusion MRI. International Society for Magnetic Resonance in Medicine, 2018, Paris, France.
• Fick RHJ, Petiet A, Santin M, Philippe A-C, Lehericy S, Deriche R, Wassermann D Non-parametric Graphnet-regularized representation of dMRI in space and time. Medical Image Analysis 43:37–53. doi: 10.1016/j.media.2017.09.002
• Gallardo G, Deriche R, Wassermann D. A Novel Atlas of Human Cerebral Cortex based on Extrinsic Connectivity. Organization for Human Brain Mapping 2017 Annual Meeting,Vancouver, Canada.
• Gallardo G, Wells W III, Deriche R, Wassermann D (2017) Groupwise structural parcellation of the whole cortex: a logistic random effects model based approach. NImg. doi: 10.1016/j.neuroimage.2017.01.070
• Chen L, Wassermann D, Kochalka J, Menon V (2017) Concordance between white-matter pathways and functional circuits linking the VWFA and IPS. OHBM
• Taghia J, Ryali S, Chen T, Supekar KS, Cai W, Menon V (2017) Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI. NImg 155:271–290. doi: 10.1016/j.neuroimage.2017.02.083
• Chen, T., Cai, W., Ryali, S., Supekar, K., Menon, V. (2016). Distinct global brain dynamics and spatiotemporal organization of the salience network. PLOS Biology.
• Fick RHJ, Wassermann D, Caruyer E, Deriche R (2016) MAPL: Tissue microstructure estimation using Laplacian-regularized MAP-MRI and its application to HCP data. 134:365–385
• Gallardo G, Fick R, Wells W W III, Deriche R, Wassermann D (2016) Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models. Computational Diffusion MRI. Athens, Greece.
• Gallardo G, Fick R, III WW, Deriche R, Wassermann D (2016) Efficient Population-Representative Whole-Cortex Parcellation Based on Tractography, Human Brain Mapping, Geneva, Switzerland.