Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction

@article{Bennett2009NeuralCO,
  title={Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction},
  author={CM Bennett and MB Miller and GL Wolford},
  journal={NeuroImage},
  year={2009},
  volume={47}
}
With the extreme dimensionality of functional neuroimaging data comes extreme risk for false positives. Across the 130,000 voxels in a typical fMRI volume the probability of a false positive is almost certain. Correction for multiple comparisons should be completed with these datasets, but is often ignored by investigators. To illustrate the magnitude of the problem we carried out a real experiment that demonstrates the danger of not correcting for chance properly. GLM RESULTS 

Figures from this paper

Evaluating methods of correcting for multiple comparisons implemented in SPM12 in social neuroscience fMRI studies: an example from moral psychology
TLDR
Using moral judgment fMRI data, voxelwise thresholding with familywise error correction based on Random Field Theory provides a more precise overlap than either clusterwiseresholding, Bonferroni correction, or false discovery rate correction methods.
Evaluating Methods of Correcting for Multiple Comparisons Implemented in SPM12 in Social Neuroscience fMRI Studies: An Example from Moral Psychology
TLDR
Using moral judgment fMRI data, voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap than either clusterwiseresholding, Bonferroni correction, or false discovery rate correction methods.
The principled control of false positives in neuroimaging.
TLDR
This commentary argues in favor of a principled approach to the multiple testing problem--one that places appropriate limits on the rate of false positives across the whole brain gives readers the information they need to properly evaluate the results.
Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research
TLDR
This study evaluated three methods for multiple comparison correction, Statistical non-Parametric Mapping, 3DClustSim, and Threshold Free Cluster Enhancement, by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images.
Test–retest reliability in fMRI: Or how I learned to stop worrying and love the variability
TLDR
One of the first studies of whole brain, single subject variability in functional Magnetic Resonance Imaging (fMRI), and the choices made in performing the experiment as they did are recounted.
How to Deal with Multiplicity in Neuroimaging? A Case for Global Calibration
TLDR
How Stein’s paradox (1956) motivates a Bayesian multilevel (BML) approach that, rather than fighting multiplicity, embraces it to the authors' advantage through a global calibration process among spatial units is reviewed.
Multiple testing corrections, nonparametric methods, and random field theory
  • T. Nichols
  • Medicine, Computer Science
    NeuroImage
  • 2012
TLDR
A narrative aims to give the methodological researcher a historical perspective on this important aspect of fMRI data analysis by drawing connections with the older modalities, PET in particular, and how software implementations have tracked (or lagged behind) theoretical developments.
Scanning the Horizon: Towards transparent and reproducible neuroimaging research
TLDR
How the field should evolve is described to produce the most meaningful answers to neuroscientific questions, and current and suggested best practices are outlined.
Cluster failure or power failure? Evaluating sensitivity in cluster-level inference
TLDR
Assessment of the sensitivity of gold-standard nonparametric cluster correction by resampling real data from five tasks in the Human Connectome Project and comparing results with those from the full "ground truth" datasets found that sensitivity after correction is lower than may be practical for many fMRI applications.
Scanning the horizon: towards transparent and reproducible neuroimaging research
TLDR
How the field of functional MRI should evolve is described to produce the most meaningful and reliable answers to neuroscientific questions.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 26 REFERENCES
Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate
TLDR
This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR) and demonstrates this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.
A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data
TLDR
A novel and efficient semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems in fMRI data, including the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation.
Circular analysis in systems neuroscience: the dangers of double dipping
TLDR
It is argued that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection, and 'double dipping' the use of the same dataset for selection and selective analysis is suggested.
Controlling the familywise error rate in functional neuroimaging: a comparative review
TLDR
It is found that Bonferroni-related tests offer little improvement over Bonferronsi, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom.
A unified statistical approach for determining significant signals in images of cerebral activation
TLDR
A unified statistical theory for assessing the significance of apparent signal observed in noisy difference images is presented and an estimate of the P‐value for local maxima of Gaussian, t, χ2 and F fields over search regions of any shape or size in any number of dimensions is estimated.
An evaluation of spatial thresholding techniques in fMRI analysis
TLDR
Two aspects of spatial thresholding procedures applied to single subject fMRI analysis through simulation are studied, indicating that smoothing has the highest sensitivity to modest magnitude signals, but tend to overestimate the size of the activation region.
Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition 1
TLDR
It is argued that, in some cases, other analysis problems likely created entirely spurious correlations and the data from these studies could be reanalyzed with unbiased methods to provide accurate estimates of the correlations in question and urge authors to perform such reanalyses.
Detecting Activations in PET and fMRI: Levels of Inference and Power
TLDR
It is envisaged that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.
False discovery rate revisited: FDR and topological inference using Gaussian random fields
TLDR
In brief, inference based on conventional voxel-wise FDR procedures is not appropriate for inferences on the topological features of a statistical parametric map (SPM), such as peaks or regions of activation.
Assessing the significance of focal activations using their spatial extent
TLDR
The results mean that detecting significant activations no longer depends on a fixed threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region.
...
1
2
3
...