Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis
arxiv(2024)
摘要
Bayesian Image-on-Scalar Regression (ISR) offers significant advantages for
neuroimaging data analysis, including flexibility and the ability to quantify
uncertainty. However, its application to large-scale imaging datasets, such as
found in the UK Biobank, is hindered by the computational demands of
traditional posterior computation methods, as well as the challenge of
individual-specific brain masks that deviate from the common mask typically
used in standard ISR approaches. To address these challenges, we introduce a
novel Bayesian ISR model that is scalable and accommodates inconsistent brain
masks across subjects in large scale imaging studies. Our model leverages
Gaussian process priors and integrates salience area indicators to facilitate
ISR. We develop a cutting-edge scalable posterior computation algorithm that
employs stochastic gradient Langevin dynamics coupled with memory mapping
techniques, ensuring that computation time scales linearly with subsample size
and memory usage is constrained only by the batch size. Our approach uniquely
enables direct spatial posterior inferences on brain activation regions. The
efficacy of our method is demonstrated through simulations and analysis of the
UK Biobank task fMRI data, encompassing 8411 subjects and over 120,000 voxels
per image, showing that it can achieve a speed increase of 4 to 11 times and
enhance statistical power by 8
with zero-imputation in various simulation scenarios.
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