Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI

Abstract

Resting state fMRI (rs-fMRI) provides imaging biomarkers of task-independent brain function that can be associated with clinical variables or modulated by interventions such as behavioral training or pharmacological manipulations. These biomarkers include time-averaged regional brain function as manifested by regional cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion MRI and correlated temporal fluctuations of function across brain networks with either ASL or blood oxygenation level dependent (BOLD) fMRI. Resting-state studies are typically carried out using just one of several prescribed state conditions such as eyes closed (EC), eyes open (EO), or visual fixation on a cross-hair (FIX), which may affect the reliability and specificity of rs-fMRI. In this study, we collected test-retest ASL MRI data during 4 resting-state task conditions: EC, EO, FIX and PVT (low-frequency psychomotor vigilance task), and examined the effects of these task conditions on reliability and reproducibility as well as trait specificity of regional brain function. We also acquired resting-state BOLD fMRI under FIX and compared the network connectivity reliabilities between the four ASL conditions and the BOLD FIX condition. For resting-state ASL data, EC provided the highest CBF reliability, reproducibility, trait specificity, and network connectivity reliability, followed by EO, while FIX was lowest on all of these measures. PVT demonstrated lower CBF reliability, reproducibility and trait specificity than EO and EC. Overall network connectivity reliability was comparable between ASL and BOLD. Our findings confirm ASL CBF as a reliable, stable, and consistent measure of resting-state regional brain function and support the use of EC or EO over FIX and PVT as the resting-state condition.

Keywords: Arterial spin labeled (ASL) perfusion MRI; Network connectivity; Reliability; Resting state conditions; Trait specificity.

Fig. 1

Latent State-trait model. (A). The Latent State-trait model (LST) (Steyer et al., 2015) decomposes measured CBF Y_ik (ith measure at occasion k) into measurements errors (ε_ik) and latent State_k. It then further decomposes the latent State_k into state residual (ζ_ik) and trait components (ξ_ik). The measurement error, the state residual, and the trait are un-correlated with each other. A restrictive LST model used in this study further assumes equal measurement error variance and equal state residual variance. (B). The grouped LST model nests four models for the four conditions and assumes equal measurement error variance and equal state residual variance across the conditions.

Fig. 2

Evaluation of global CBF (mean, wsCV and ICC) across scan sessions (Top row) and across task conditions (Bottom row). The global mean CBF was computed for each subject, session and condition, and the within-subject coefficient of variance (wsCV) and the intraclass correlation coefficient (ICC) were then calculated for each session and condition. Fig. 2A and 2D show the global mean CBF and standard deviation across each session and condition, respectively. Fig. 2B and 2E show the wsCV computed across each session and condition, respectively, along with their 95% confidence intervals. Similarly, Fig 2C and 2E how the ICC and theirs 95% confidence interval. Significant differences between each session/condition pairs assessed with permutation tests with a paired sample design are marked with *: p < 0.05; **:p < 0.01; ***: p < 0.001.

Fig. 3

tSNR of global CBF and the GM-WM ratio for the scan sessions and conditions. Temporal signal-to-noise-ratio (tSNR) of global mean CBF and the gray matter-white matter (GM-WM) contrast ratio across the two scan sessions (Top Row A and B) and across the four task conditions (Bottom Row C and D). The error bars show the standard deviations for each session/condition. Significant differences between each session/condition pairs assessed with permutation tests with a paired sample design are marked with *: p < 0.05; **: p < 0.01.

Fig. 4

Trait specificity of four task conditions: region of interest results. Mean CBF measurements were analyzed separately using the Latent state-trait (LST) model for each of the resting conditions (each column) and each region of interest (ROI, each row). The colored cells are the regions that accepted the restrictive LST model (assuming equal measurement error variance for the measurements and equal state residual variance across the occasions). The labeled values and color scale show the trait specificity of mean CBF estimated by the LST model for each corresponding condition and ROI. Trait specificity ranges from 0 to 1, and is unitless.

Fig. 5

Comparison of network connectivity between ASL and BOLD task conditions. (A): the mean connectivity strength of within-network connections. Results are presented across all within-network connections, as well as broken down by networks. The bars and error bars represent the across subject mean and standard deviation of the mean connectivity strength of all within-network connections of the two scan sessions. Significant differences between the conditions were assessed using the paired T test and the Bonferroni corrected p values are marked with *: p < 0.05; **: p < 0.01; ***: p < 0.001. (B): the averaged ICC of within-network connectivity. Results are presented across all within-network connections, as well as broken down by networks. The bars and error-bars show the mean and standard deviation of the averaged ICC of all within-network connections for each task condition using Jackknife leave-one-out resampling method. Significant differences between the conditions were assessed using the paired Jackknife resampling method. Most pairs showed significant differences after Bonferroni correction. For clarity, pairs showing Bonferroni corrected p > 0.05 are instead marked as ‘ns’, i.e., non-significant.