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

Abstract

Background and Purpose-Changes in the blood-oxygen-level-dependent (BOLD) signal provide a noninvasive measure of blood flow, but a detailed comparison with established perfusion parameters in acute stroke is lacking. We investigated the relationship between BOLD signal temporal delay and dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) in stroke patients. Methods-In 30 patients with acute (<24 hours) ischemic stroke, we performed Pearson correlation and multiple linear regression between DSC-MRI parameters (time to maximum [Tmax], mean transit time, cerebral blood flow, and cerebral blood volume) and BOLD-based parameters (BOLD delay and coefficient of BOLD variation). Prediction of severe hypoperfusion (Tmax >6 seconds) was assessed using receiver-operator characteristic (ROC) analysis. Results-Correlation was highest between Tmax and BOLD delay (venous sinus reference; time shift range 7; median r=0.60; interquartile range=0.49-0.71). Coefficient of BOLD variation correlated with cerebral blood volume (median r= 0.37; interquartile range=0.24-0.51). Mean R 2 for predicting BOLD delay by DSC-MRI was 0.54 (SD=0.2) and for predicting coefficient of BOLD variation was 0.37 (SD=0.17). BOLD delay (whole-brain reference, time shift range 3) had an area under the curve of 0.76 for predicting severe hypoperfusion (sensitivity=69.2%; specificity=80%), whereas BOLD delay (venous sinus reference, time shift range 3) had an area under the curve of 0.76 (sensitivity=67.3%; specificity=83.5%). Conclusions-BOLD delay is related to macrovascular delay and microvascular hypoperfusion, can identify severely hypoperfused tissue in acute stroke, and is a promising alternative to gadolinium contrast agent-based perfusion assessment in acute stroke. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. Unique identifier: NCT00715533 and NCT02077582.

Detecting Perfusion Pattern Based on the Background Low-Frequency Fluctuation in Resting-State Functional Magnetic Resonance Imaging Data and Its Influence on Resting-State Networks: An Iterative Postprocessing Approach

“…Some other groups also tried to calculate the time delay of the lesion area in ischemic stroke and Moyamoya patients (Christen et al, 2015;Khalil et al, 2017;Siegel et al, 2016). These studies used the averaged time-series of BOLD signal within a whole-brain mask or manually picked ROI as the template of fluctuation pattern to measure the time delay.…”

One‐step analysis of brain perfusion and function for acute stroke patients after reperfusion: A resting‐state fMRI study

“…Previous studies on the rs-fMRI BOLD delay found spatial similarities between perfusion deficits and time-delayed areas in acute stroke, subacute stroke, and chronic ischemic patients. [10][11][12]21 Our study focused on AIS patients who received reperfusion treatment. We found that the AIS patients who exhibited complete reperfusion after the therapy showed neither perfusion defects on the TTP maps nor timedelayed areas in the TSA.…”

Altered physiological brain variation in drug‐resistant epilepsy

“…This assumption of identical signal distributions may not be true, since recently BOLD signal noise characteristics have been shown to be altered by disease (Khalil et al., 2017; Makedonov, Black, & MacIntosh, 2013; Makedonov, Chen, Masellis, & MacIntosh, 2016; Tuovinen et al., 2017). A recently emerged metric to assess BOLD signal properties is the coefficient of variation (CV), which has previously been used to reflect stability of a measured process.…”

“…Interestingly, CV has also been shown to reflect changes in physiological fluctuations in BOLD signal in white matter. Moreover, altered values of CV have been detected in acute ischemic stroke (Khalil et al., 2017), Alzheimer's disease (Makedonov et al., 2016; Tuovinen et al., 2017), and small vessel disease (Makedonov et al., 2013). …”