Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

Benoit da Mota; Radu Tudoran; Alexandru Costan; Gaël Varoquaux; Goetz Brasche; Patricia J. Conrod; Hervé Lemaitre; Tomáš Paus; Marcella Rietschel; Vincent Frouin; Jean-Baptiste Poline; Gabriel Antoniu; Bertrand Thirion

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

NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France

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.

Document type :

Journal articles

Domain :

Computer Science [cs] / Bioinformatics [q-bio.QM]

Life Sciences [q-bio] / Quantitative Methods [q-bio.QM]

Complete list of metadatas

Cited literature [35 references] Display Hide Download

https://hal.inria.fr/hal-01057325
Contributor : Bertrand Thirion <>
Submitted on : Friday, August 22, 2014 - 11:37:40 AM
Last modification on : Friday, June 28, 2019 - 3:02:50 PM
Long-term archiving on : Thursday, November 27, 2014 - 1:41:03 PM

frontiers.pdf

Files produced by the author(s)

Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

File

Identifiers

Collections

Citation

Export

Metrics

1523

571

Contact

Machine Learning Patterns for Neuroimaging-Genetic Studies in the Cloud

File

Identifiers

Collections

Citation

Export

Share

Metrics

1523

571

Contact