UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Secure multivariate large-scale multi-centric analysis through on-line learning: an imaging genetics case study

Lorenzi, M; Gutman, B; Thompson, PM; Alexander, DC; Ourselin, S; Altmann, A; (2017) Secure multivariate large-scale multi-centric analysis through on-line learning: an imaging genetics case study. In: Proceedings of the 12th International Symposium on Medical Information Processing and Analysis. (pp. p. 101601). Society of Photo-Optical Instrumentation Engineers Green open access

[img]
Preview
Text
Lorenzi_1016016.pdf - ["content_typename_Published version" not defined]

Download (489kB) | Preview

Abstract

State-of-the-art data analysis methods in genetics and related fields have advanced beyond massively univariate analyses. However, these methods suffer from the limited amount of data available at a single research site. Recent large-scale multi-centric imaging-genetic studies, such as ENIGMA, have to rely on meta-analysis of mass univariate models to achieve critical sample sizes for uncovering statistically significant associations. Indeed, model parameters, but not data, can be securely and anonymously shared between partners. We propose here partial least squares (PLS) as a multivariate imaging-genetics model in meta-studies. In particular, we propose an online estimation approach to partial least squares for the sequential estimation of the model parameters in data batches, based on an approximation of the singular value decomposition (SVD) of partitioned covariance matrices.We applied the proposed approach to the challenging problem of modeling the association between 1,167,117 genetic markers (SNPs, single nucleotide polymorphisms) and the brain cortical and sub-cortical atrophy (354,804 anatomical surface features) in a cohort of 639 individuals from the Alzheimer’s Disease Neuroimaging Initiative. We compared two different modeling strategies (sequential- and meta-PLS) to the classic non-distributed PLS. Both strategies exhibited only minimal approximation errors of model parameters. The proposed approaches pave the way to the application of multivariate models in large scale imaging-genetics meta-studies, and may lead to novel understandings of the complex brain phenotype-genotype interactions.

Type: Proceedings paper
Title: Secure multivariate large-scale multi-centric analysis through on-line learning: an imaging genetics case study
Event: SIPAIM 2016: 12th International Symposium on Medical Information Processing and Analysis, 5-7 December, Tandil, Argentina
Location: Tandil, Argentina
Dates: 05 December 2016 - 07 December 2016
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2256799
Publisher version: http://dx.doi.org/10.1117/12.2256799
Language: English
Additional information: Copyright © 2017 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Keywords: imaging-genetics, partial least squares, online learning, meta analysis
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: http://discovery.ucl.ac.uk/id/eprint/1531135
Downloads since deposit
91Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item