PARIETAL - 2011 - Annual activity report

PARIETAL

PARIETAL - 2011

Project Team Parietal

Members

Overall Objectives

Scientific Foundations

Human neuroimaging data and its use
High-field MRI
Technical challenges for the analysis of neuroimaging data

Application Domains

Application Domains

Software

Mayavi
Nipy
MedINRIA
Scikit learn

New Results

A supervised clustering approach for fMRI-based inference of brain states
Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
Total variation regularization for fMRI-based prediction of behaviour
Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom.
Multi-subject dictionary learning (MSDL) to segment an atlas of brain spontaneous activity
Functional brain imaging with M/EEG using structured sparsity in time-frequency dictionaries
A probabilistic framework to infer brain functional connectivity from anatomical connections
M/EEG source reconstruction based on Gabor thresholding in the source space
Multifractal Analysis of Resting State Networks in Functional MRI
Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity
Detecting Outlying Subjects in High-Dimensional Neuroimaging Datasets with Regularized Minimum Covariance Determinant
Connectivity-informed fMRI Activation Detection
Beyond brain reading: identify and predict with clustering and randomized sparsity
Joint T1 and Brain Fiber Diffeomorphic Registration Using the Demons

Contracts and Grants with Industry

Grants with Industry: Abrain project

Partnerships and Cooperations

Regional Initiatives
National Initiatives
International Initiatives

Dissemination

Animation of the scientific community
Teaching

Bibliography

Major publications
Publications of the year

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Section: Software

MedINRIA

Participants : Pierre Fillard [correspondant] , Sergio Medina, Viviana Siless.

MedINRIA is a free collection of softwares developed within the Asclepios, Athena and Visages research projects. It aims at providing to clinicians state-of-the-art algorithms dedicated to medical image processing and visualization. Efforts have been made to simplify the user interface, while keeping high-level algorithms. MedINRIA is available for Microsoft windows XP/Vista, Linux Fedora Core, MacOSX, and is fully multithreaded.

See also the web page http://med.inria.fr/ .

Version: 2.0

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