Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks

Highlights

• Statistical Comparison of functional connectivity (FC) in BOLD and pCASL.

• BOLD provided highly reliable known Resting State Networks (RBNs).

• ASL provided similar RBNs with lower but still sufficient reliability.

• ASL further provided highly reproducible network CBF.

• Combination of ASL and BOLD provides a powerful tool to characterize RBNs.

Abstract

Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain's intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. This study systematically investigated the similarity and reliability of resting brain networks (RBNs) in BOLD and ASL. A 2 × 2 × 2 factorial design was employed where each subject underwent repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. Both independent and joint FC analyses revealed common RBNs in ASL and BOLD rs-fcMRI with a moderate to high level of spatial overlap, verified by Dice Similarity Coefficients. Test–retest analyses indicated more reliable spatial network patterns in BOLD (average modal Intraclass Correlation Coefficients: 0.905 ± 0.033 between-sessions; 0.885 ± 0.052 between-scanners) than ASL (0.545 ± 0.048; 0.575 ± 0.059). Nevertheless, ASL provided highly reproducible (0.955 ± 0.021; 0.970 ± 0.011) network-specific CBF measurements. Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners.

Introduction

Since the seminal work by Biswal et al. in 1995 (Biswal et al., 1995), the study of resting brain networks (RBN) based on functional connectivity (FC) in resting state fMRI (rs-fcMRI) has experienced an upsurge from basic to clinical neuroscience. The majority of rs-fcMRI studies have used blood oxygen level dependent (BOLD) contrast due to its technical simplicity, high sensitivity and temporal resolution. Recently, a growing number of rs-fcMRI studies have employed arterial spin labeled (ASL) perfusion MRI (Chuang et al., 2008, Dai et al., 2013, Jann et al., 2013, Liang et al., 2011, Liang et al., 2012, Zou et al., 2009), which measures cerebral blood flow (CBF) using magnetically labeled arterial blood water as an endogenous tracer (Detre et al., 1992). Compared to BOLD, perfusion-based FC analysis provides more direct and quantitative measures of the physiology and metabolism of specific networks (Buxton et al., 2004). The inherently quantitative nature of ASL allows for the assignment of biologically meaningful values to the networks, thus may complement BOLD by providing a more interpretable biomarker.

To date, however, the application of perfusion-based rs-fcMRI in clinical neuroscience has been hampered by the relatively low sensitivity and temporal resolution of ASL compared to BOLD. The recent development of pseudo-continuous ASL (pCASL) with background suppressed (BS) 3D acquisitions (e.g. GRASE — a hybrid of spin and gradient echo and Stack-of-Spirals) has dramatically improved the sensitivity and temporal SNR of perfusion imaging series (Alsop et al., 2014, Fernandez-Seara et al., 2008), allowing the detection of CBF based RBNs while minimizing potential BOLD contaminations (Du et al., 2012, Liang et al., 2012). Another appealing feature of perfusion based rs-fMRI using pCASL with 3D BS GRASE or Stack-of-Spirals is the improved visualization of RBNs involving brain regions affected by susceptibility artifacts at the tissue–air interfaces (Fernandez-Seara et al., 2005).

Given the complementary nature of BOLD and perfusion rs-fcMRI — higher sampling rate/temporal resolution in BOLD and absolute CBF quantification in ASL, the combination of the two contrasts may offer a powerful tool for rs-fcMRI studies to fully characterize the spatiotemporal and quantitative properties of RBNs. The primary purpose of this study was to present a framework for independent and joint FC analyses of BOLD and perfusion based rs-fcMRI data to identify common and modality specific RBNs, using rigorous statistical approaches. For future applications of BOLD and perfusion-based functional connectivity analyses in clinical studies, it is critical to establish the reliability of RBNs across time (Meindl et al., 2010, Shehzad et al., 2009, Zuo et al., 2010), scanner platforms (Van Dijk et al., 2010) and modalities as well as their dependencies on imaging parameters (Birn et al., 2013, Patriat et al., 2013, Van Dijk et al., 2010). For this purpose, a 2 × 2 × 2 factorial design was employed in the present study using repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. We hypothesized that BOLD and ASL rs-fcMRI should show common RBNs that are reproducible across time and scanners. The overall FC in BOLD RBNs is stronger than that of ASL RBNs, yet ASL networks show higher FC in specific brain regions (e.g. orbitofrontal cortex). Finally, network specific quantitative CBF measured by ASL may indicate the baseline metabolic activity and may be associated with (or underlie) the strength of functional connectivity of the corresponding network (Aslan et al., 2011, Liang et al., 2013, Tomasi et al., 2013).