As part of metaMRI, Jérome Dockès is spending 2 months with Poldracklab to discuss text mining and cognitive ontologies: how do we extract information from neuroimaging literature and associate activation foci with these concepts ?
Elvis Dohmatob (Parietal) and Martin Perez Guevara (Neurospin) are staying there to work on BIDS, fmriprep, nistats with Poldracklab and the open science brain iamging community of the west cost.
More information here: http://reproducibility.stanford.edu/2nd-annual-crn-coding-sprint-open-call-for-applications/
Russ Poldrack is visiting Parietal for the News workshop on June 8-9, 2017
See you there: https://news2017.sciencesconf.org/
Joke Durnez will present our activities during this workshop.
BIS’2017 event page: https://project.inria.fr/siliconvalley/workshops/bis2017/
MetaMRI partners will present several works at NIPS:
- Learning brain regions via large-scale online structured sparse dictionary learning
Elvis DOHMATOB*, Inria; Arthur Mensch, inria; Gaël Varoquaux, ; Bertrand Thirion,
- Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
Timothy Rubin*, Indiana University; Sanmi Koyejo, UIUC; Michael Jones, Indiana University; Tal Yarkoni, University of Texas at Austin
The project aims at broaden the knowledge about accurate effect sizes and their variability in neuroimaging analysis and to provide intuition about effect sizes for different tasks and brain regions. It also aims at increasing the availability and ease the use of a variety of power analyses for neuroimaging data.
The project fund Joke Durnez (Inria Parietal, Stanford University).
This project funds the PhD thesis of J. Dockès.
The purpose of this thesis is to learn a semantic structure in cognitive terms from their occurrence in brain activations. This structure will simplify massive multi-label statistical-learning problems that arise in brain mapping by providing compact representations of cognitive concepts while capturing the imprecision on the definition these concepts.
The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Work supported by MetaMRI. See http://www.hal.inserm.fr/inserm-01345616
Our paper in Plos Computational biology is out:
We contribute a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two datasets by model-based generation of synthetic activity maps from recombination of shared network topographies. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks.
See https://hal.archives-ouvertes.fr/hal-01338307 for more information