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Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.

ABSTRACT: Permutation testing is a non-parametric method for obtaining the max null distribution used to compute corrected p-values that provide strong control of false positives. In neuroimaging, however, the computational burden of running such an algorithm can be significant. We find that by viewing the permutation testing procedure as the construction of a very large permutation testing matrix, T, one can exploit structural properties derived from the data and the test statistics to reduce the runtime under certain conditions. In particular, we see that T is low-rank plus a low-variance residual. This makes T a good candidate for low-rank matrix completion, where only a very small number of entries of T (?0.35% of all entries in our experiments) have to be computed to obtain a good estimate. Based on this observation, we present RapidPT, an algorithm that efficiently recovers the max null distribution commonly obtained through regular permutation testing in voxel-wise analysis. We present an extensive validation on a synthetic dataset and four varying sized datasets against two baselines: Statistical NonParametric Mapping (SnPM13) and a standard permutation testing implementation (referred as NaivePT). We find that RapidPT achieves its best runtime performance on medium sized datasets (50?n?200), with speedups of 1.5× - 38× (vs. SnPM13) and 20x-1000× (vs. NaivePT). For larger datasets (n?200) RapidPT outperforms NaivePT (6× - 200×) on all datasets, and provides large speedups over SnPM13 when more than 10000 permutations (2× - 15×) are needed. The implementation is a standalone toolbox and also integrated within SnPM13, able to leverage multi-core architectures when available.

SUBMITTER: Gutierrez-Barragan F

PROVIDER: S-EPMC5671355 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.

Gutierrez-Barragan Felipe F Ithapu Vamsi K VK Hinrichs Chris C Maumet Camille C Johnson Sterling C SC Nichols Thomas E TE Singh Vikas V

NeuroImage 20170715

Permutation testing is a non-parametric method for obtaining the max null distribution used to compute corrected p-values that provide strong control of false positives. In neuroimaging, however, the computational burden of running such an algorithm can be significant. We find that by viewing the permutation testing procedure as the construction of a very large permutation testing matrix, T, one can exploit structural properties derived from the data and the test statistics to reduce the runtime ...[more]

PMID: 28720551

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Project description:Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's disease neuroimaging initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains.

Dataset Information

Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.

Publications

Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.

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