Asynchronous Local Analysis of simultaneous BOLD ASL Multislice Acquisition (ALABAMA): Toward Whole-Brain Noninvasive Estimation of Resting-State Neuronal-Vascular Coupling
Vincent Schmithorst1, Vince Lee1, and Ashok Panigrahy1

1Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States


A method (ALABAMA) for regional estimation of the coupling between R2* fluctuations and log signal intensity (S0) (related to R2*-CBF coupling via a calibration factor) during the resting state is proposed. The method incorporates pseudo-continuous ASL (pCASL) labeling together with simultaneous multi-slice (SMS) and double-echo EPI acquisition for fast temporal resolution. The sequence yields robust positive ΔR2*-Δln S0 correlations in most resting-state networks, which may then be used to estimate resting-state neuronal-vascular coupling (NVC) with an additional calibration scan. However, negative correlations were found in posterior DMN and central executive network (CEN) regions, possibly reflective of altered flow-metabolism relationships.


The overall goal of this work is a non-invasive technique for regional estimation of neuronal-vascular coupling (NVC) over the whole brain during the resting state, feasible for pediatric populations. The first step towards this goal is robust estimation of the relationship between R2* fluctuations and CBF fluctuations, using pseudo-continuous ASL (pCASL) labeling and simultaneous multislice (SMS) double-echo acquisition (ALABAMA). The combination of pCASL with SMS results in much improved CNR for CBF estimation1. We combine this approach with a dual-echo EPI acquisition for the simultaneous estimation of BOLD contrast.


The ALABAMA sequence was implemented on a Siemens Skyra 3T scanner. The sequence consists of pCASL labeling, followed by SMS acquisition of two frames of double-echo data acquired in close temporal proximity (Figure 1). In order to increase the number of labeled frames acquired, there are no alternate control acquisitions, and only a very short post-inversion delay (PID), as estimation of absolute CBF is not required. The TR is made short enough so that spins labeled one TR ago have sufficient time (1.5 – 2 s) to traverse into the tissue compartment for the current acquisition.

The sequence was implemented on a 16-year girl and an adult male. Scan parameters were: label duration = 500 ms, PID = 50 ms, TR = 1400 ms, TE1/TE2 = 12/33 ms, flip angle = 60 degrees, SMS factor = 4X, voxel size = 5 mm isotropic, matrix = 48 X 48, four scan runs of approximately 4 minutes each. The datasets were spatially normalized into MNI space and the timeseries for each run were extracted from each of the 90 cortical regions in the automated anatomic labeling (AAL) parcellation2. The timeseries were band-pass filtered with 0.002 Hz < f < 0.08 Hz and quadratically corrected for scanner drift.

We estimate the coupling of ΔR2* to Δlog (unattenuated) signal intensity S0, as this is proportional to R2*-CBF coupling via a calibration factor. To avoid possible bias, the Deming regression procedure is used on ΔR2* and Δ ln S0 estimated from alternate datasets (e.g. R2* is estimated from the first dataset after the labeling, while ln S0 is estimated from the second dataset, and vice versa).


We present results from the 16-year-old girl; results from the adult subject were quite similar (data not shown). The long-echo log signal from the first and second acquisition after labeling were highly correlated (R = 0.95) with a slope very near unity (1.01), justifying the use of both acquisitions in the analysis.

Robust positive correlations of ΔR2* with Δln S0 co-localized to known resting-state networks (Figure 2) including: (1) portions of the default mode network (DMN) including the anterior cingulate and medial temporal lobe (hippocampus), (2) salience network (prefrontal/insular regions), (3) primary visual regions and (4) subcortical regions (basal ganglia and thalamus). The magnitudes of these effects are comparable to what would be expected given a ± 30% fluctuation in CBF and a ± 1% fluctuation in BOLD signal (Figure 3). In contrast, we noted that regions of the posterior DMN (precuneus) and regions of the central executive network (CEN) displayed robust negative correlations of ΔR2* with Δln S0 (Figures 2, 4).


Our results here suggest the ALABAMA approach may be a feasible method for whole-brain rs-NVC estimation, when combined with an alternate label-control acquisition for estimation of absolute pCASL signal change, and a non-invasive multi-gradient echo scan for calibration using R2’ estimation, as proposed previously3. Performance should improve by optimizing sequence parameters such as labeling duration, number of acquired frames after labeling, and SMS factor; and improved model estimation, possibly enabling estimation at the voxelwise level. Previously proposed methods using pulsed ASL (pASL) for combined BOLD-CBF acquisition typically do not cover the entire brain and have a significantly longer TR.

Positive ΔR2*-Δln S0 correlations were found in the anterior portions of the DMN and other networks including the visual system and salience network (insular), previously shown to have relatively high functional connectivity and regional blood flow4. The negative correlations found in posterior DMN and CEN regions could reflect altered resting-state flow-metabolism relationships5, inhibitory neuronal activity6, or varying metabolic demand7, 8.


A method for whole-brain estimation of flow-R2* coupling is proposed (ALABAMA) which can be used for non-invasive estimation of resting-state NVC over the whole brain in pediatric populations. Results suggest robust estimation of this parameter is possible within a clinically feasible scan time. We found robust ΔR2*-Δln S0 positive correlations in the anterior DMN, the primary visual system, the salience network and subcortical regions.


No acknowledgement found.


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Figure 1. Diagram of the ALABAMA pulse sequence. A relatively short PCASL labeling train (~500 ms) is followed by a very short PID (~50 ms) and successive double-echo SMS-EPI acquisitions at high temporal resolution. The short TR (< 1500 ms) enables sufficient time for labeled spins to traverse into the tissue compartment for the next TR.

Figure 2. Regions with significant positive (red) and negative (blue) correlations of fluctuations in R2* with log signal intensity. All regions significant at p < 0.001 Bonferroni corrected.

Figure 3. Scatterplot of ΔR2* vs. Δ log signal intensity for the left orbitofrontal cortex obtained from the ALABAMA sequence in a 16-year-old girl. The slope is 70 Hz, consistent with relative flow change of ± ~30%, absolute PCASL signal change of 1%, and R2* change of ± 0.2 Hz.

Figure 4. Scatterplot of ΔR2* vs. Δ log signal intensity for the right precuneus obtained from the ALABAMA sequence in a 16-year-old girl. The negative relation may reflect altered resting-state flow-metabolism relationships in the posterior DMN and parietal lobe.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)