Synopsis

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.

Purpose

Methods

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).

Results

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).

Discussion

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 demand^{7, 8}.

Conclusion

Acknowledgements

References

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