@inproceedings{8f5539f76f74461baeb8b967c603c9d8,
title = "Partial least squares modelling for imaging-genetics in Alzheimer's disease: Plausibility and generalization",
abstract = "In this work we evaluate the ability of PLS in generalizing to unseen clinical cohorts when applied to the analysis of the joint variation between genotype and phenotype in Alzheimer's disease (AD). The model is trained on single-nucleotide polymorphisms (SNPs) and brain volumes obtained from the ADNI database for a large cohort of healthy individuals and AD patients, and validated on the ADNI MCI and ENIGMA cohorts. The experimental results confirm the ability of PLS in providing a meaningful description of the joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on clinically heterogeneous cohorts.",
keywords = "Alzheimer's disease, GWA, genotype, imaging-genetics, machine learning, phenotype",
author = "Marco Lorenzi and Boris Gutman and Hibar, {Derrek P.} and Andre Altmann and Neda Jahanshad and Thompson, {Paul M.} and Sebastien Ourselin",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",
year = "2016",
month = jun,
day = "15",
doi = "10.1109/ISBI.2016.7493396",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "838--841",
booktitle = "2016 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}