Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

Benoit Da Mota 1 Radu Tudoran 2 Alexandru Costan 2 Gaël Varoquaux 1 Goetz Brasche 3 Patricia J. Conrod 4 Hervé Lemaitre 5 Tomáš Paus 6, 7 Marcella Rietschel 8 Vincent Frouin 9 Jean-Baptiste Poline 9, 10 Gabriel Antoniu 2 Bertrand Thirion 1
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
2 KerData - Scalable Storage for Clouds and Beyond
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE, Inria Rennes – Bretagne Atlantique
3 Cloud Team
EMIC - European Microsoft Innovation Center
4 Institute of Psychiatry, Psychology & Neuroscience, King's College London
5 Adolescent psychopathology and Medicine
6 MNI - McConnell Brain Imaging Centre
7 School of Psychology [Nottingham]
8 Department of Genetic Epidemiology in Psychiatry [Mannhein]
9 NEUROSPIN - Service NEUROSPIN
10 Henry H. Wheeler Jr. Brain Imaging Center [Berkeley]
Abstract : Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a two weeks deployment on hundreds of virtual machines.
Keywords : heritability machine learning neuroimaging-genetic cloud computing fMRI heritability.
Type de document :
Article dans une revue
Frontiers in Neuroinformatics, Frontiers, 2014, Recent advances and the future generation of neuroinformatics infrastructure, 8, 〈10.3389/fninf.2014.00031〉
Liste complète des métadonnées

Littérature citée [35 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-01057325
Contributeur : Bertrand Thirion <>
Soumis le : vendredi 22 août 2014 - 11:37:40
Dernière modification le : lundi 14 janvier 2019 - 10:44:05
Document(s) archivé(s) le : jeudi 27 novembre 2014 - 13:41:03

Fichier

frontiers.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

CEA | INRIA | IRISA | IRISA-INSA-R | IRISA-D1 | UR1-UFR-ISTIC | INSA-RENNES | JOLIOT | CEA-DRF | NEUROSPIN | UNIV-RENNES

Citation

Benoit Da Mota, Radu Tudoran, Alexandru Costan, Gaël Varoquaux, Goetz Brasche, et al.. Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud. Frontiers in Neuroinformatics, Frontiers, 2014, Recent advances and the future generation of neuroinformatics infrastructure, 8, 〈10.3389/fninf.2014.00031〉. 〈hal-01057325〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

1352

Téléchargements de fichiers

462

Contact


archive-ouverte@inria.fr