Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging.

Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20 s vs. the expected 5-6 s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32 s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.

Coupling between cerebrovascular oscillations and CSF flow fluctuations during wakefulness: An fMRI study.

It is commonly believed that cerebrospinal fluid (CSF) movement is facilitated by blood vessel wall movements (i.e., hemodynamic oscillations) in the brain. A coherent pattern of low frequency hemodynamic oscillations and CSF movement was recently found during non-rapid eye movement (NREM) sleep via functional MRI. This finding raises other fundamental questions: 1) the explanation of coupling between hemodynamic oscillations and CSF movement from fMRI signals; 2) the existence of the coupling during wakefulness; 3) the direction of CSF movement. In this resting state fMRI study, we proposed a mechanical model to explain the coupling between hemodynamics and CSF movement through the lens of fMRI. Time delays between CSF movement and global hemodynamics were calculated. The observed delays between hemodynamics and CSF movement match those predicted by the model. Moreover, by conducting separate fMRI scans of the brain and neck, we confirmed the low frequency CSF movement at the fourth ventricle is bidirectional. Our finding also demonstrates that CSF movement is facilitated by changes in cerebral blood volume mainly in the low frequency range, even when the individual is awake.

Using carpet plots to analyze transit times of low frequency oscillations in resting state fMRI.

A "carpet plot" is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or "transit times", of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.

A novel method of quantifying hemodynamic delays to improve hemodynamic response, and CVR estimates in CO2 challenge fMRI.

Elevated carbon dioxide (CO2) in breathing air is widely used as a vasoactive stimulus to assess cerebrovascular functions under hypercapnia (i.e., "stress test" for the brain). Blood-oxygen-level-dependent (BOLD) is a contrast mechanism used in functional magnetic resonance imaging (fMRI). BOLD is used to study CO2-induced cerebrovascular reactivity (CVR), which is defined as the voxel-wise percentage BOLD signal change per mmHg change in the arterial partial pressure of CO2 (PaCO2). Besides the CVR, two additional important parameters reflecting the cerebrovascular functions are the arrival time of arterial CO2 at each voxel, and the waveform of the local BOLD signal. In this study, we developed a novel analytical method to accurately calculate the arrival time of elevated CO2 at each voxel using the systemic low frequency oscillations (sLFO: 0.01-0.1 Hz) extracted from the CO2 challenge data. In addition, 26 candidate hemodynamic response functions (HRF) were used to quantitatively describe the temporal brain reactions to a CO2 stimulus. We demonstrated that our approach improved the traditional method by allowing us to accurately map three perfusion-related parameters: the relative arrival time of blood, the hemodynamic response function, and CVR during a CO2 challenge.

Cerebral circulation time derived from fMRI signals in large blood vessels.

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.

The resting-state fMRI arterial signal predicts differential blood transit time through the brain.

Previous studies have found that aperiodic, systemic low-frequency oscillations (sLFOs) are present in blood-oxygen-level-dependent (BOLD) data. These signals are in the same low frequency band as the "resting state" signal; however, they are distinct signals which represent non-neuronal, physiological oscillations. The same sLFOs are found in the periphery (i.e. finger tips) as changes in oxy/deoxy-hemoglobin concentration using concurrent near-infrared spectroscopy. Together, this evidence points toward an extra-cerebral origin of these sLFOs. If this is the case, it is expected that these sLFO signals would be found in the carotid arteries with time delays that precede the signals found in the brain. To test this hypothesis, we employed the publicly available MyConnectome dataset (a two-year longitudinal study of a single subject) to extract the sLFOs in the internal carotid arteries (ICAs) with the help of the T1/T2-weighted images. Significant, but negative, correlations were found between the LFO BOLD signals from the ICAs and (1) the global signal (GS), (2) the superior sagittal sinus, and (3) the jugulars. We found the consistent time delays between the sLFO signals from ICAs, GS and veins which coincide with the blood transit time through the cerebral vascular tree.

Vascular effects of caffeine found in BOLD fMRI.

The blood oxygen level-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) measures neuronal activation indirectly. Previous studies have found aperiodic, systemic low-frequency oscillations (sLFOs, ~0.1 Hz) in BOLD signals from resting state (RS) fMRI, which reflects the non-neuronal cerebral perfusion information. In this study, we investigated the possibility of extracting vascular information from the sLFOs in RS BOLD fMRI, which could provide complementary information to the neuronal activations. Two features of BOLD signals were exploited. First, time delays between the sLFOs of big blood vessels and brain voxels were calculated to determine cerebral circulation times and blood arrival times. Second, voxel-wise standard deviations (SD) of LFOs were calculated to represent the blood densities. We explored those features on the publicly available Myconnectome data set (a 2-year study of an individual subject (Male)), which contains 45 RS scans acquired after the subject had coffee, and 45 coffee-free RS scans, acquired on different days. Our results showed that shorter time delays and smaller SDs were detected in caffeinated scans. This is consistent with the vasoconstriction effects of caffeine, which leads to increased blood flow velocity. We also compared our results with previous findings on neuronal networks from the same data set. Our finding showed that brain regions with the significant vascular effect of caffeine coincide with those with a significant neuronal effect, indicating close interaction. This study provides methods to assess the physiological information from RS fMRI. Together with the neuronal information, we can study simultaneously the underlying correlations and interactions between vascular and neuronal networks, especially in pharmacological studies.