Tracking cerebral blood flow in BOLD fMRI using recursively generated regressors

Abstract

BOLD fMRI data is dominated by low frequency signals, many of them of unclear origin. We have recently shown that some portion of the low frequency oscillations found in BOLD fMRI are systemic signals closely related to the blood circulation (Tong, et al., 2013). They are commonly treated as physiological noise in fMRI studies. In the present study, we propose and test a novel data-driven analytical method that uses these systemic low frequency oscillations in the BOLD signal as a tracer to follow cerebral blood flow dynamically. Our findings demonstrate that: (1) systemic oscillations pervade the BOLD signal; (2) the temporal traces evolve as the blood propagates though the brain; and, (3) they can be effectively extracted via a recursive procedure and used to derive the cerebral circulation map. Moreover, this method is independent from functional analyses, and thus allows simultaneous and independent assessment of information about cerebral blood flow to be conducted in parallel with the functional studies. In this study, the method was applied to data from the resting state scans, acquired using a multiband EPI sequence (fMRI scan with much shorter TRs), of 7 healthy participants. Dynamic maps with consistent features resembling cerebral blood circulation were derived, confirming the robustness and repeatability of the method.

Cerebral circulation time derived from fMRI signals in large blood vessels

“…They are widely presented in the fMRI signals and are in the same frequency band as most of the resting state BOLD signals of neuronal origins. Highly correlated sLFOs are found in the fMRI data throughout the brain with delays . The delay times between these sLFOs in different cerebral regions are primarily consistent with blood arrival time differences obtained from bolus tracking, ie, dynamic susceptibility contrast MRI .…”

“…Highly correlated sLFOs are found in the fMRI data throughout the brain with delays. 12 The delay times between these sLFOs in different cerebral regions are primarily consistent with blood arrival time differences obtained from bolus tracking, ie, dynamic susceptibility contrast MRI. 13 In a previous study, 14 a novel method was developed to study the sLFO fMRI signals in the large arteries (ie, internal carotid artery: ICA) from resting state data.…”

“…However, these signals are widely presented in the BOLD throughout the whole brain. Previous fMRI studies have validated the finding and used the sLFOs to track cerebral blood flow throughout the brain . The small within‐subjects delay time variation indicates that the delay time is a consistent parameter that does not change with time under the same condition due to the autoregulation of the cerebral blood flow .…”

Cerebrovascular reactivity mapping without gas challenges

“…Further effort to obtain the signal component that is most relevant to CO 2 fluctuation was undertaken by identifying the frequency band (0.02–0.04 Hz) that shows the higher correlation with end-tidal CO 2 . A few studies have also been reported to use resting-state fMRI data to estimate blood transit time in the brain (Amemiya et al, 2014; Christen et al, 2015; Tong and Frederick, 2014). We have tried to obtain the voxel-wise delay following a previous study (Christen et al, 2015) and apply it in resting-state CVR mapping.…”

Relationship Between Changes in the Temporal Dynamics of the Blood-Oxygen-Level-Dependent Signal and Hypoperfusion in Acute Ischemic Stroke

“…36 It may improve the quality of the reference time course used to assess BOLD delay and has the added advantage of reducing the effects of head motion on the data. 37,38 Multiband sequences, which allow the acquisition of rsfMRI data at a high temporal resolution, 39 may be useful for a more refined assessment of hypoperfusion in acute stroke.…”