The influence of physiological noise correction on test-retest reliability of resting-state functional connectivity

Abstract

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

Keywords: cardiac; functional connectivity; physiological noise; reliability; respiration; resting state.

FIG. 1.

The fraction of variance accounted for by each of the correction methods, relative to no correction. The greatest amount of variance is explained in and near large blood vessels.

FIG. 2.

The variance as accounted for by various physiological noise correction techniques, averaged over the entire brain, all gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

FIG. 3.

The fraction of variance accounted for by each of the correction methods that incorporate respiration volume and/or heart rate, relative to RETROICOR.

FIG. 4.

Functional connectivity maps for a seed in the posterior cingulate for no correction, and nine different postprocessing corrections: RETROICOR (Glover et al., 2000); respiration volume per time (RVT)+derivative of RVT; RVT at 2 lags (RVT-2); RVT at 8 lags (RVT-8); RVT at 41 different lags, but only a single lag in each voxel (RVTcor), RVHRcor (Chang et al., 2009); regression of average WM and CSF (WM/CSF); and regression of average WM, CSF, and global signal (WM/CSF+global). Top row: thresholded at p<10⁻¹⁰; bottom row: thresholded at p<10⁻³⁰ to obtain connected regions of a thresholded spatial extent similar to that from WM/CSF+global signal regression.

FIG. 5.

Voxel-wise t-test of differences in functional connectivity for a posterior cingulated seed relative to no physiological corrections.

FIG. 6.

Top: mean intraclass correlation coefficient (ICC), over all 16,110 connections, for different postprocessing corrections—left, within a session (30 min separation between runs); right, between sessions (2–3 months between sessions). *Corrections that show a significant reduction relative to no correction (p<0.05). Bottom: mean within-subject/between-session variance (red bars), and between-subject variance (green bars), for different postprocessing corrections—left, within a session (30 min separation between runs); right, between sessions (2–3 months between sessions).

FIG. 7.

Top: mean ICC, over the 200 most significant connections (as based on “No correction”), for different postprocessing corrections—left, within a session (30 min separation between runs); right, between sessions (2–3 months between sessions). *Corrections that show a significant reduction relative to no correction (p<0.05). Bottom: mean within-subject/between-session variance (red bars), and between-subject variance (green bars), for different postprocessing corrections. Results are averaged over the 200 most significant connections (as based on “No correction”). Bottom left: within a session (30 min separation between runs); bottom right: between sessions (2–3 months between sessions).

FIG. 8.

Mean test–retest reliability (as measured by the intraclass correlation coefficient) for the fraction of variance (R²) explained by each correction. Mean is computed over all 180 seed regions of interest. Test–retest reliability is quite high for most correction techniques, indicating much smaller within-subject between-session differences in physiological noise variance compared to between-subject differences.

FIG. 9.

The absolute value of connectivity strength, averaged over CSF, WM, and all other seeds within the respective network, for different seed regions: posterior cingulate (PCC, default mode network); left posterior occipital lobe (lpOL, visual network); left precentral gyrus (lPCG, motor network); left amygdala (lAMY, affective network).