Statistical power and prediction accuracy in multisite resting-state fMRI connectivity

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Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

Koene R. A. Van Dijk, Trey Hedden, Archana Venkataraman … (2010)

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.

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Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space

D. Collins, Peter Neelin, Terrence M. Peters … (1994)

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Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses.

John Desmond, Gary H Glover (2002)

Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80% power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power. Copyright 2002 Elsevier Science B.V.

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Title: NeuroImage

Abbreviated Title: NeuroImage

Publisher: Elsevier BV

ISSN (Print): 10538119

Publication date Created: April 2017

Publication date (Print): April 2017

Volume: 149

Issue:

Pages: 220-232

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DOI: 10.1016/j.neuroimage.2017.01.072

PubMed ID: 28161310

SO-VID: 53540f69-abf7-4572-b255-d64a08a90d2c

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Statistical power and prediction accuracy in multisite resting-state fMRI connectivity

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Core Readings in Statistical Mediation Analysis

Most cited references 26

Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space

Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses.

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