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Voxel-wise and spatial modelling of binary lesion masks : comparison of methods with a realistic simulation framework
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Kindalova, Petya, Kosmidis, Ioannis and Nichols, Thomas E. (2021) Voxel-wise and spatial modelling of binary lesion masks : comparison of methods with a realistic simulation framework. NeuroImage, 236 . 118090. doi:10.1016/j.neuroimage.2021.118090 ISSN 1053-8119.
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WRAP-Voxel-wise-spatial-modelling-binary-lesion-comparison-methods-realistic-simulation-framework-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1865Kb) | Preview |
Official URL: https://doi.org/10.1016/j.neuroimage.2021.118090
Abstract
Objectives
White matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence.
Methods
We review three regression approaches for modelling binary lesion masks including mass-univariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we propose matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting.
Results
Our findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates.
Conclusions
Bias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | NeuroImage | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 1053-8119 | ||||||||
Official Date: | 1 August 2021 | ||||||||
Dates: |
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Volume: | 236 | ||||||||
Article Number: | 118090 | ||||||||
DOI: | 10.1016/j.neuroimage.2021.118090 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 6 January 2022 | ||||||||
Date of first compliant Open Access: | 6 January 2022 | ||||||||
Open Access Version: |
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