Project-Team:ARAMIS

Project-Team Aramis

Members

Overall Objectives

Introduction

Research Program

Application Domains

New Software and Platforms

New Results

Highlights of the Year
Detection of volume loss in hippocampal layers in Alzheimer's disease using 7 T MRI
White matter lesions in patients with frontotemporal lobar degeneration due to progranulin mutations
Template-based morphometry using diffeomorphic iterative centroids
Structural connectivity differences in left and right temporal lobe epilepsy
Morphometry of anatomical shape complexes with dense deformations and sparse parameters
Iconic-Geometric Nonlinear Registration of a Basal Ganglia Atlas for Deep Brain Stimulation Planning
Evaluation of morphometric descriptors of deep brain structures for the automatic classification of patients with Alzheimer's disease, mild cognitive impairment and elderly controls
A Prototype Representation to Approximate White Matter Bundles with Weighted Currents
Non-parametric resampling of random walks for spectral network clustering
Graph analysis of functional brain networks: practical issues in translational neurosciences
Hierarchy of neural organisation in the zebra fish spinal cord: causality analysis of in-vivo calcium imaging data
2D harmonic filtering of MR phase images in multicenter clinical setting: towards a magnetic signature of cerebral microbleeds

Bilateral Contracts and Grants with Industry

Bilateral Contracts with Industry

Partnerships and Cooperations

Dissemination

Bibliography

Publications of the year

Inria | Raweb 2014 | Presentation of the Project-Team ARAMIS | ARAMIS Web Site


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2014 Project-Team Activity Report

ARAMIS

Algorithms, models and methods for images and signals of the human brain

Research centre: Paris - Rocquencourt

In partnership with: CNRS, INSERM, Université Pierre et Marie Curie (Paris 6)

Field: Digital Health, Biology and Earth
Theme: Computational Neuroscience and Medecine

Keywords: Neuroimaging, Image Processing, Signal Processing, Networks, Medical Images, Statistical Learning