Calibrated fMRI techniques estimate task-induced changes in the cerebral metabolic rate of oxygen consumption (CMRO₂) based on simultaneous measurements of cerebral blood flow (CBF) and blood-oxygen-level-dependent (BOLD) signal changes evoked by stimulation (1). To calibrate the BOLD signal, a calibration factor M (corresponding to the maximum possible signal increase) is measured using blood gas manipulation techniques, involving CO₂ or O₂ gas mixture breathing. This measurement is quite sensitive to arterial spin labeling (ASL) noise, especially when using the hypercapnia (HC) method. Nevertheless, data acquisition is commonly done using double-echo EPI, which is suboptimal for ASL, due to the difficulty of implementing background suppression (BS) in 2D-multislice readouts (2). A background-suppressed double acquisition sequence has previously been proposed (3) using two separate EPI readouts. However, 3D readouts are desirable for ASL since they can be optimally combined with BS (4).

The goal of this study was to implement a dual-acquisition sequence for simultaneous measurement of BOLD and CBF and evaluate its potential for M-mapping and quantification of CMRO₂ changes in response to a motor task.

Pulse sequence: The sequence (Fig. 1) consists of a pseudo-continuous ASL (PCASL) module with a background-suppressed single-shot 3D-GRASE readout for acquisition of ASL images and multi-slice 2D-EPI for acquisition of BOLD images. The two readouts are separated by a delay of 1.3s to allow for recovery of the longitudinal magnetization.

Subjects: 5 healthy subjects (34±6 years) participated in the study, after signing written informed consent.

Scanning protocol: The study was performed on a 3T Siemens Trio using a 32-channel head-array. The scanning session included an anatomic T₁-weighted image. Functional data were acquired with the following parameters: PCASL labeling time=1.6s, post-labeling delay=1.5s, TE (GRASE/EPI)=29.3/30 ms, TR=6s, in-plane resolution=4x4 mm², 16 slices, slice thickness=6mm. First, data were acquired throughout a gas mixture breathing protocol (5 min room air (baseline), 5 min breathing 5% CO₂ in room air (HC) and 5 min room air (recovery)) (5), followed by one run with a motor activation paradigm (3 blocks of bilateral finger tapping alternated with 3 rest blocks of 1 min duration).

Data pre-processing (MATLAB scripts and SPM8): GRASE and EPI images were separately pre-processed. EPI images were realigned and smoothed (6-mm kernel). GRASE images were realigned, followed by subtraction of label and control to obtain perfusion-weighted images and smoothing.

Gas manipulation data analysis: EPI images obtained during gas manipulation were entered into a general linear model (GLM) with two conditions (room air and hypercapnia), excluding the first minute after each transition. The model involved a regressor to account for the image being acquired during control or label conditions and a linear drift term. The regression coefficients were used to compute signal baseline level (BOLD₀) and signal change induced by HC (ΔBOLD). Perfusion-weighted images were entered into a similar GLM without regressors to compute perfusion difference signal at baseline and during HC (ΔS₀ and ΔS₁) and HC-induced signal change. M-maps were calculated via Eq. 1 (α=0.18, β=1.5) (6).

$$M=\frac{\frac{\triangle BOLD}{BOLD_{0}}}{1-\left(\frac{\triangle S_{1}}{\triangle S_{0}}\right)^{\left(\alpha-\beta\right)}} [1]$$

Task activation data analysis: EPI images obtained during the activation paradigm were entered into a GLM with two conditions (rest and task) and a regressor to model the ASL effect. Perfusion-weighted images were analogously, excluding the regressor. Contrast images comparing task and rest were obtained. Activated regions were identified using uncorrected p-value<0.005. Regions of interest (ROI) were defined in the left and right motor cortex (MC), including voxels that appeared active in both analyses, and used to extract signal time-courses. Relative CMRO₂ maps were computed via Eq. 2.

$$rCMRO_{2}=\left(\frac{\triangle S_{1}}{\triangle S_{0}}\right)^{1-\frac{\alpha}{\beta}}\left(1-\frac{\frac{\triangle BOLD}{BOLD_{0}}}{M}\right)^{\frac{1}{\beta}} [2]$$

Fig. 2 shows EPI and background-suppressed GRASE images, signal difference images and M-map, computed from the gas manipulation data. Gray matter M values averaged across the 5 subjects (Table 1) are in very good agreement with reported results (6). Fig. 3 shows maps of signal change induced by bilateral finger-tapping, revealing activation in bilateral MC. Relative CMRO₂ time-courses in the activated regions are shown in Fig. 4. Group-averaged calibrated fMRI parameters, including mean M values for the left and right MC ROIs, as well as task-evoked signal changes (%BOLD, %ΔS and rCMRO₂) are shown in Table 1. Mean evoked CMRO₂ estimates are 12 and 13% for left and right MC, respectively. Assuming a resting state value for gray matter CMRO₂ of 146 mmol/100g/min (7), these relative values would correspond to absolute changes of 17 and 19 mmol/100g/min.This work demonstrates the feasibility of measuring task-evoked CMRO₂ changes using a dual-acquisition PCASL sequence that combines a background-suppressed 3D readout optimized for ASL with 2D-EPI.