UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines

Sato, JR; Rondina, JM; Mourao-Miranda, J; (2014) Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines. Frontiers in Neuroscience , 6 , Article 300. 10.3389/fnins.2012.00178. Green open access

[img]
Preview
Text
Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines.pdf - ["content_typename_Published version" not defined]

Download (1MB) | Preview

Abstract

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed.

Type: Article
Title: Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fnins.2012.00178
Publisher version: http://dx.doi.org/10.3389/fnins.2012.00178
Language: English
Additional information: © 2012 Sato, Rondina and Mourão-Miranda. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
Keywords: Framingham score, cardiovascular risk factors, magnetic resonance imaging, pattern recognition, multivariate analysis, machine learning, SVM, one-class, neuroimaging, pattern recognition
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences
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
URI: http://discovery.ucl.ac.uk/id/eprint/1381791
Downloads since deposit
46Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item