Integrated strategy for improving functional connectivity mapping using multiecho fMRI

Abstract

Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T*2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.

Keywords: human neuroimaging; resting state fMRI; time series.

Fig. 1.

(A) Comparison of two datasets (subjects 1 and 2) with increasing levels of in-scanner head motion. For each subject, four panels show (from top to bottom) rigid-body MP traces in millimeter units; FD traces; a comparison of DVARS for raw data (black trace) and motion regressed data; and comparison of DVARS for raw data (black trace) and high and low-κ time series (blue and red traces, respectively) from ME-ICA. The MP traces show that subjects have (left to right) repeated small movements atop a more substantial tilt (>3 mm maximum) and a series of large abrupt head movements of >3 mm in some directions and >1–2 mm in FD. Subject 2 is a worst-case dataset that would ordinarily be discarded, but is studied here as a test case. For all subjects, DVARS traces show that linear regression of motion parameters (and first derivatives) does not effectively remove most motion-related signals. In contrast, low-κ time series capture the majority of motion-related signals, leaving a comparatively flat DVARS trace from high-κ time series without the use of motion parameter regression or band pass filtering. (B) Comparison of acquisition parameters (i) and signal quality after preprocessing for conventional methods vs. ME acquisition and ME-ICA denoising (ii). ME acquisition has larger voxels and longer repetition time (TR), but weighted combination gives greater than expected increases in tSNR (190 theoretical based on voxel size increases). ME-ICA denoising nearly quadruples tSNR while explaining nearly all (97%) combined ME variance. Number of high-κ components differs significantly between high- and low-motion subject groups (iii).

Fig. 2.

(A) Maps for seed-based correlation analysis after conventional denoising and functional connectivity estimation (top row) and ME-ICR (bottom row). Conventional connectivity maps are thresholded to R > 0.5 and ME-ICR maps are thresholded to P < 0.05. Connectivity is shown for the default mode network (Left) and right hand area (Right) in two subjects with moderate and high levels of motion, respectively (1 and 2 from Fig. 1). (B) Probability densities of functional connectivity values (Z) for seed-based connectivity analysis after conventional (blue), ME-ICA analysis (green), and a standard normal distribution (red) for comparison. Left side of axial images correspond to anatomical left.

Fig. 3.

(A) Unthresholded maps of ME-ICR and conventional connectivity maps using seeds: right DL-PFC and left cerebellar motor area seeds, to assess cortical and subcortical-cortical connectivity respectively. (B) Mean connectivity maps for both estimators and both seeds. (C) Consistency analysis of ME-ICR and conventional connectivity. Consistency is assessed using ICC of connectivity for individual seeds over 32 subjects. ICC = 1 is ideal, indicating seed connectivity is identical over subjects. Specificity is assessed using ICC of connectivity for individual subjects over 32 random seeds. ICC = 0 is ideal, indicating random connectivity maps are not consistent. Left side of axial images correspond to anatomical left.

Fig. 4.

(A) Group-level connectivity maps using ME-ICR (P < 0.001, FDR q < 0.005) and conventional connectivity (P < 10⁻⁷, FDR q < 10⁻⁶) for four different seeds: posterior cingulate, right hand, Brocas area, and left V1. ME-ICR connectivity shows for the PCC, the canonical default mode network; for the right hand, ipsilateral motor and premotor areas, bilateral sensory cortices, ipsilateral thalamus, and contralateral cerebellum; for Broca’s area, premotor, middle temporal, supramarginal areas, and ipsilateral dorsal striatum; and for visual seed, bilateral visual cortices bounded by parieto-occiptal junction and the pulvinar of the thalamus (black arrow). Conventional connectivity shows for PCC, connectivity to the motor cortex; for the right hand to the insula; for Brocas area, bilateral connectivity; and for primary visual cortex, the whole cortex. (B) For regions in A, plus dorsolateral, ventromedial prefrontal cortices, caudate, and insula, type I error control for contrasts between subgroupings: random (Left); motion biased (Center); high vs. low movers (Right). For each seed, observed error (y axis) compared with expected error (x axis). Comparisons are made at a series of significance values (lines, 0.0001–0.05), for ME-ICR (blue) and conventional (yellow) connectivity. Lines below and above y = x (black line) denote nominal and failed type I error control, respectively. ME-ICA consistently demonstrates nominal type I error control, whereas in biased and extreme cases, conventional connectivity fails with up to five times greater type I error than expected. (C) Maps of false-positive connectivity differences between high vs. low movers after thresholding to P < 0.01 and familywise error (cluster) correction to <0.05. All conventional FC maps are populated by false-positive clusters. All ME-ICR maps are empty. Left side of axial images correspond to anatomical left.