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Paul A Taylor

Publications and source records attributed to Paul A Taylor.

2 recordsLinked to original sources
bioRxiv2016

AFNI and Clustering: False Positive Rates Redux

In response to reports of inflated false positive rate (FPR) in FMRI group analysis tools, a series of replications, investigations, and software modifications were made to address this issue. While these investigations continue, significant progress has been made to adapt AFNI to fix such problems. Two separate lines of changes have been made. First, a long-tailed model for the spatial correlation of the FMRI noise characterized by autocorrelation function (ACF) was developed and implemented into the 3dClustSim tool for determining the cluster-size threshold to use for a given voxel-wise threshold. Second, the 3dttest++ program was modified to do randomization of the voxel-wise t-tests and then to feed those randomized t-statistic maps into 3dClustSim directly for cluster-size threshold determination-without any spatial model for the ACF. These approaches were tested with the Beijing subset of the FCON-1000 data collection. The first approach shows markedly improved (reduced) FPR, but in many cases is still above the nominal 5%. The second approach shows FPRs clustered tightly about 5% across all per-voxel p-value thresholds [≤] 0.01. If t-tests from a univariate GLM are adequate for the group analysis in question, the second approach is what the AFNI group currently recommends for thresholding. If more complex per-voxel statistical analyses are required (where permutation/randomization is impracticable), then our current recommendation is to use the new ACF modeling approach coupled with a per-voxel p-threshold of 0.001 or below. Simulations were also repeated with the now infamously \"buggy\" version of 3dClustSim: the effect of the bug on FPRs was minimal (of order a few percent).

Neuroscience↗
bioRxiv2016

Is the Statistic Value All We Should Care about in Neuroimaging?

Here we address an important issue that has been embedded within the neuroimaging community for a long time: the absence of effect estimates in results reporting in the literature. The statistic value itself, as a dimensionless measure, does not provide information on the biophysical interpretation of a study, and it certainly does not represent the whole picture of a study. Unfortunately, in contrast to standard practice in most scientific fields, effect (or amplitude) estimates are usually not provided in most results reporting in the current neuroimaging publications and presentations. Possible reasons underlying this general trend include: 1) lack of general awareness, 2) software limitations, 3) inaccurate estimation of the BOLD response, and 4) poor modeling due to our relatively limited understanding of FMRI signal components. However, as we discuss here, such reporting damages the reliability and interpretability of the scientific findings themselves, and there is in fact no overwhelming reason for such a practice to persist. In order to promote meaningful interpretation, cross validation, reproducibility, meta and power analyses in neuroimaging, we strongly suggest that, as part of good scientific practice, effect estimates should be reported together with their corresponding statistic values. We provide several easily adaptable recommendations for facilitating this process.

Neuroscience↗