Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization

Abstract

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.

Altered resting‐state functional connectivity of the putamen and internal globus pallidus is related to speech impairment in Parkinson's disease

“…Each resting‐state scan collected 212 volumes (8 min, 29 s). During all resting‐state scans, subjects were asked to relax, keep their eyes open, and to keep their mind free of thought (Van Dijk et al., 2010). …”

Strategy‐based reasoning training modulates cortical thickness and resting‐state functional connectivity in adults with chronic traumatic brain injury

“…In rsfMRI preprocessing, the quality of the preprocessed data was visually inspected at each step. After motion ‘scrubbing’, we confirmed that the total time of remaining frames after the ‘scrubbing’ exceeded 4 min, the minimum length required to reliably estimate rsFC (van Dijk et al., 2010). We also ensured that there were no MRI scans or neuropsychological measures that were acquired too late (i.e., outside the 2 SD band from the mean) for all time points.…”

“…These altered cortical morphometric properties are frequently associated with functional deficits (Gale et al., 2005; Palacios et al., 2013; Sidaros et al., 2009; Spitz et al., 2013; Warner et al., 2010; Zhou et al., 2013), and correspondences between MRI‐based and histological morphometric data of TBI individuals have been reported (Maxwell, MacKinnon, Stewart, & Graham, 2009). RsFC MRI measures the temporal coherency of blood oxygenation level‐dependent (BOLD) signal at rest and it allows us to identify how the brain's intrinsic functional networks are organized (see van Dijk et al., 2010 for review). Diffuse axonal injury (DAI) (Smith, Meaney, & Shull, 2003) is a primary injury mechanism of TBI, and rsFC has been a promising technique to identify DAI‐induced higher‐order cognitive impairments (e.g., reasoning, decision making, and selective attention) among individuals with TBI (Sharp, Scott, & Leech, 2014).…”

The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury

“…Several studies utilize larger scan time which may provide a better chance of observing dFNC patterns per subject (Hutchison et al., 2013). However, evidence suggests that 5 min is enough to acquire a stable connectivity signal (van Dijk et al., 2010) and is thought as minimum necessary (Allen et al., 2012). Another limitation is that we preferred a spike preprocessing method where spike repressors can be included with FWD repressors in one linear model.…”