fcMRI Data Research Articles

Adaptive cyclic physiologic noise modeling and correction in functional MRI

Physiologic noise in BOLD-weighted MRI data is known to be a significant source of the variance, reducing the statistical power and specificity in fMRI and functional connectivity analyses. We show a dramatic improvement on current noise correction methods in both fMRI and fcMRI data that avoids overfitting. The traditional noise model is a Fourier series expansion superimposed on the periodicity of parallel measured breathing and cardiac cycles. Correction using this model results in removal of variance matching the periodicity of the physiologic cycles. Using this framework allows easy modeling of noise. However, using a large number of regressors comes at the cost of removing variance unrelated to physiologic noise, such as variance due to the signal of functional interest (overfitting the data). It is our hypothesis that there are a small variety of fits that describe all of the significantly coupled physiologic noise. If this is true, we can replace a large number of regressors used in the model with a smaller number of the fitted regressors and thereby account for the noise sources with a smaller reduction in variance of interest. We describe these extensions and demonstrate that we can preserve variance in the data unrelated to physiologic noise while removing physiologic noise equivalently, resulting in data with a higher effective SNR than with current corrections techniques. Our results demonstrate a significant improvement in the sensitivity of fMRI (up to a 17% increase in activation volume for fMRI compared with higher order traditional noise correction) and functional connectivity analyses.

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

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.