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

Previous |

Home | Next next

Section: Software

Nipy

Participants : Bertrand Thirion [correspondant] , Virgile Fritsch, Gaël Varoquaux.

Nipy is an open-source Python library for neuroimaging data analysis, developed mainly at Berkeley, Stanford, MIT and Neurospin. It is open to any contributors and aims at developing code and tools sharing. Some parts of the library are completely developed by Parietal and LNAO (CEA, DSV, Neurospin). It is devoted to algorithmic solutions for various issues in neuroimaging data analysis. All the nipy project is freely available, under BSD licence. It is available in NeuroDebian.