Silver, Matt, Montana, Giovanni and Nichols, Thomas E. (2011) False positives in neuroimaging genetics using voxel-based morphometry data. NeuroImage, Vol.54 (No.2). pp. 992-1000. doi:10.1016/j.neuroimage.2010.08.049 ISSN 10538119.

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Abstract

Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In “imaging genetics”, such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of ‘null’ SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken.

We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9–5.6%), using a relatively high cluster-forming threshold, αc = 0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (αc = 0.01, 0.05), and for images smoothed using a 6 mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions.
While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	10538119
Official Date:	19 August 2011
Dates:	Date Event 19 August 2011 Published
Volume:	Vol.54
Number:	No.2
Number of Pages:	9
Page Range:	pp. 992-1000
DOI:	10.1016/j.neuroimage.2010.08.049
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Wellcome Trust , National Institutes of Health , National Institute on Aging , National Institute of Biomedical Imaging and Bioengineering , Dana Foundation
Grant number:	NIH (U01 AG024904), (P30 AG010129), (K01 AG030514)
Version or Related Resource:	Also presented at 6th International Imaging Genetics Conference, Irvine, 18-19 January, 2010

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