White matter differences between healthy young APOE4 carriers and non-carriers identified with tractography and support vector machines
Open accessAbstract
The apolipoprotein E4 (ApoE4) is an established risk factor for Alzheimer's disease (AD). Previous work has shown that this allele is associated with functional (fMRI) changes as well structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess the diffusion characteristics and the white matter (WM) tracts of healthy young (20-38 years) ApoE4 carriers and non-carriers. No significant differences in diffusion indices were found between young carriers (ApoE4+) and non-carriers (ApoE4-). There were also no significant differences between the groups in terms of normalised GM or WM volume. A feature selection algorithm (ReliefF) was used to select the most salient voxels from the diffusion data for subsequent classification with support vector machines (SVMs). SVMs were capable of classifying ApoE4 carrier and non-carrier groups with an extremely high level of accuracy. The top 500 voxels selected by ReliefF were then used as seeds for tractography which identified a WM network that included regions of the parietal lobe, the cingulum bundle and the dorsolateral frontal lobe. There was a non-significant decrease in volume of this WM network in the ApoE4 carrier group. Our results indicate that there are subtle WM differences between healthy young ApoE4 carriers and non-carriers and that the WM network identified may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age.
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Data-driven tract-specific analysis uses machine learning techniques to perform tasks such as disease classification and prediction, as well as prediction of behavioral measures and traits.
“Another tract-specific analysis strategy is data-driven and uses machine learning techniques to perform tasks such as disease classification and prediction (Deng et al., 2019; O’Dwyer et al., 2012; Payabvash et al., 2019; Zhang et al., 2018a), as well as prediction of behavioral measures and traits (Seguin et al., 2020a; Tian et al., 2021).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as O’Dwyer et al., 2012
The Methods and Discussion describe using diffusion data and a support vector machine to classify healthy young APOE4 carriers and non-carriers, and the Introduction says selected voxels were used for tractography. The Discussion also reports related work classifying older subjects as having mild cognitive impairment or being healthy. This paper itself did not classify disease or predict its onset; the later citations in the citing sentence, not O’Dwyer et al., are attached to behavioral measures and traits. (LLM, conf=0.85)
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- Authors
- O'Dwyer, L., Lamberton, F., Matura, S., Scheibe, M., Miller, J., Rujescu, D., Prvulovic, D., & Hampel, H.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Goethe University Frankfurt (DE)
- Martin Luther University Halle-Wittenberg (DE)
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-04-25
- Volume
- 7
- Issue
- 4
- Pages
- e36024
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3338494
- OpenAlex
- W2096142952
- Cited by
- 34
- References
- 96
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 6 · 2022: 1 · 2021: 1 · 2020: 3 · 2018: 2 · 2017: 3 · 2016: 2 · 2015: 4 · 2014: 1 · 2013: 2 · 2012: 2
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Universitätsbibliothek der LMU
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- APOE ε4, white matter, diffusion MRI, tractography, Alzheimer's disease risk, young adults, white matter networks, cingulum, parietal lobe, support vector machine, ReliefF feature selection, white matter volume
- MeSH terms
- Adult, Brain, Cognition, Demography, Diffusion, Female, Health, Heterozygote, Humans, Male, Organ Size, Apolipoprotein E4, Young Adult, Diffusion Tensor Imaging, Support Vector Machine
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