Sparse Sampling of Silence Type I Errors With an Emphasis on Primary Auditory Cortex

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Original languageEnglish
Article number516
Journal / PublicationFrontiers in Neuroscience
Online published31 May 2019
Publication statusPublished - May 2019



Sparse sampling functional MRI (ssfMRI) enables stronger primary auditory cortex blood oxygen level-dependent (BOLD) signal by acquiring volumes interspersed with silence, reducing the physiological artifacts associated with scanner noise. Recent calculations of type I error rates associated with resting-state fMRI suggest that the techniques used to model the hemodynamic response function might be resulting in higher false positives than is generally acceptable. In the present study, we analyze ssfMRI to determine type I error rates associated with whole brain and primary auditory cortex voxel-wise activation patterns. Study participants (n = 15, age 27.62 ± 3.21 years, range: 22 to 33 years; 6 females) underwent ssfMRI. Three variables were optimized to determine the hemodynamic response function to auditory stimuli, which was then substituted for silent stimuli to ascertain false positives. 1) The gap delay occurring between the end of the 3 s stimuli and beginning of the acquisition time (TA) was 1 to 3.5 s of silence, in 0.25 or 0.5 s intervals. 2) The duration of the silent period occurring after the TA and prior to the new stimulus presentation was 5 or 8 s. 3) The repetition time (TR) was 10 to 15 s. We report that common techniques used for analyzing ssfMRI result in high type I error rates. The whole brain and primary auditory cortex voxel-wise analysis resulted in similar error distributions. The approximate number of type I errors for the whole brain and auditory cortex analyses for P < 0.05, P < 0.01, and P < 0.001 was 9.02%, 2.95% and 0.58% respectively. When conducting a ssfMRI analysis, conservative α level should be employed (α < 0.001) to bolster the results in the face of false positive results.

Research Area(s)

  • Auditory cortex, False positives, Null hypothesis, Sparse sampling fMRI, Type I error rates

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