PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Correlation Based Multivariate Analysis of Genetic Influence on Brain Volume
David Hardoon, Ulrich Ettinger, Janaina Mourao-Miranda, Elena Antonova, David Collier, Veena Kumari, Steven Williams and Michael Brammer
Neuroscience Letters Volume 450, Number 3, pp. 281-286, 2009.

Abstract

Considerable research effort has focused on achieving a better understanding of the genetic individual differences in volumetric and morphological brain measures. The importance of these is underlined by evidence suggesting that brain changes in a number of neuropsychiatric disorders are at least partly genetic in origin. The currently used methods to study these relationships are mostly based on single-genotype univariate analysis techniques. These methods are limited as multiple genes are likely to interact with each other in their influences on brain structure and function. In this paper we present a feasibility study wherewe showthat by using kernel correlation analysis, with a new genotypes representation, it is possible to analyse the relative associations of several genetic polymorphisms with brain structure. The implementation of the method is demonstrated on genetic and structural magnetic resonance imaging (MRI) data acquired from a group of 16 healthy subjects by showing the multivariate genetic influence on grey and white matter.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Multimodal Integration
ID Code:4384
Deposited By:David Hardoon
Deposited On:13 March 2009