Cerebral circulation time derived from fMRI signals in large blood vessels

Abstract

Background The systemic low‐frequency oscillation (sLFO) functional (f)MRI signals extracted from the internal carotid artery (ICA) and the superior sagittal sinus (SSS) are found to have valuable physiological information. Purpose 1) To further develop and validate a method utilizing these signals to measure the delay times from the ICAs and the SSS. 2) To establish the delay time as an effective perfusion biomarker that associates with cerebral circulation time (CCT). 3) To explore within subject variations, and the effects of gender and age on the delay times. Study Type Prospective. Subjects In all, 100 healthy adults (Human Connectome Project [HCP], age range 22–36 years, 54 females and 46 males), 56 healthy children (Adolescent Brain Cognitive Development project) were included. Field Strength/Sequence Echo planar imaging (EPI) sequence at 3T. Assessment The sLFO fMRI signals from the ICAs and the SSSs were extracted from the resting state fMRI data. The maximum cross‐correlation coefficients and their corresponding delay times were calculated. The gender and age differences of delay times were assessed statistically. Statistical Tests T‐tests were conducted to measure the gender differences. The Kruskal–Wallis test was used to detect age differences. Results Consistent and robust results were found from 80% of the 400 HCP scans included. Negative correlations (–0.67) between the ICA and the SSS signals were found with the ICA signal leading the SSS signal by ∼5 sec. Within subject variation was 2.23 sec at the 5% significance level. The delay times were not significantly different between genders (P = 0.9846, P = 0.2288 for the left and right ICA, respectively). Significantly shorter delay times (4.3 sec) were found in the children than in the adults (P < 0.01). Data Conclusion We have shown that meaningful perfusion information (ie, CCT) can be derived from the sLFO fMRI signals of the large blood vessels. Level of Evidence: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1504–1513.

Origin of the Time Lag Phenomenon and the Global Signal in Resting-State fMRI

“…All other types of time lag maps in Experiments 1 and 2 were similarly computed by using either the GMS or the task-induced BOLD signal as the reference signal. All data were up-sampled to a resolution of 0.18 s (1/4 TR) for the analysis ( Tong et al, 2017 , 2019b ; Yao et al, 2019 ). The magnitude map of each IC was then computed as the Pearson’s correlation coefficients between each voxel’s time-series and the IC time-series that was shifted as much as t .…”

“…For visualization and superimposition of the time lag maps, each IC time lag was adjusted so that the mean of the IC time lag range equals that of the GMS for each subject’s data. To minimize the bias in the time lag estimation for each signal, the time-series correlation threshold was set at the Pearson’s correlation coefficient of r > 0.3 ( Tong et al, 2017 , 2019b ; Yao et al, 2019 ), which overall corresponded to the adjusted Z-score of >3 ( p < 0.0027) computed by the xDF approach with the “adaptive truncation” methods for the regularization of the autocorrelation function ( Afyouni et al, 2019 ). All the ICs whose average magnitude map had voxels that consistently survived the time-series correlation threshold across 100 runs were included in the analysis.…”

“…The time lag of the arterial signal was also measured in the extracranial internal carotid arteries following the procedure described in Tong et al (2019b) and Yao et al (2019) . The regions of interest (ROIs) were semi-automatically obtained by setting an intensity threshold within the areas surrounding each carotid artery using MPRAGE T1-weighted images, in which large arteries show high signal intensity due to the time-of-flight in-flow effect.…”

“…Lastly, to address the question regarding the origin of the GMS, we examined the internal carotid artery signal using the rs-fMRI data acquired in Experiment 2. In addition to the time lag analysis following the procedure in previous studies ( Tong et al, 2019b ; Yao et al, 2019 ), we included some evaluation of the characteristics of the arterial signal to further clarify the origin of the phenomenon.…”

Low Frequency Systemic Hemodynamic “Noise” in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications

“…In this case any correlations between the timecourses are known to be spurious; a distribution of spurious correlations can be calculated to find various significance thresholds. We have employed this method in many of our analyses where the data permits (most recently here, Yao et al, 2019).…”