The problem of low variance voxels in statistical parametric mapping; a new hat avoids a 'haircut'

Gerard R Ridgway, Vladimir Litvak, Guillaume Flandin, Karl J Friston, Will D. Penny

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Statistical parametric mapping (SPM) locates significant clusters based on a ratio of signal to noise (a 'contrast' of the parameters divided by its standard error) meaning that very low noise regions, for example outside the brain, can attain artefactually high statistical values. Similarly, the commonly applied preprocessing step of Gaussian spatial smoothing can shift the peak statistical significance away from the peak of the contrast and towards regions of lower variance. These problems have previously been identified in positron emission tomography (PET) (Reimold et al., 2006) and voxel-based morphometry (VBM) (Acosta-Cabronero et al., 2008), but can also appear in functional magnetic resonance imaging (fMRI) studies. Additionally, for source-reconstructed magneto- and electro-encephalography (M/EEG), the problems are particularly severe because sparsity-favouring priors constrain meaningfully large signal and variance to a small set of compactly supported regions within the brain. (Acosta-Cabronero et al., 2008) suggested adding noise to background voxels (the 'haircut'), effectively increasing their noise variance, but at the cost of contaminating neighbouring regions with the added noise once smoothed. Following theory and simulations, we propose to modify--directly and solely--the noise variance estimate, and investigate this solution on real imaging data from a range of modalities.

Original languageEnglish
Pages (from-to)2131-2141
Number of pages11
JournalNeuroImage
Volume59
Issue number3
Early online date17 Oct 2011
DOIs
Publication statusPublished - 1 Feb 2012

Keywords

  • Algorithms
  • Brain
  • Brain Mapping
  • Computer Simulation
  • Statistical Data Interpretation
  • Electroencephalography
  • Head
  • Humans
  • Computer-Assisted Image Processing
  • Magnetic Resonance Imaging
  • Magnetoencephalography
  • Models, Statistical
  • Positron-Emission Tomography
  • Signal-To-Noise Ratio
  • Software

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