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Arterial Spin Labeling fMRI in White Matter at 7 Tesla
Leonardo Greco1 and Olivier Reynaud1

1CIBM, EPFL, Lausanne, Switzerland

Synopsis

To date, the White Matter (WM) tracts functionality is never directly assessed using fMRI, but only inferred indirectly via healthy/impaired cortical connectivity. In this study, we use Arterial Spin Labeling (ASL), a non-invasive, quantitative, reproducible fMRI technique, to investigate WM Cerebral Blood Flow (CBF) dynamics at high field. We first show that WM CBF can be measured using standard 2D-EPI-PASL at 7T; and quantify CBF changes in GM and WM during finger-tapping. While the BOLD signal was only found elevated in GM, a net CBF increase was observed in GM and contralateral (but not ipsilateral) WM during the task (+77/25%).

PURPOSE:

To date, the White Matter (WM) tracts functionality has never been directly assessed using fMRI, but only inferred indirectly via healthy/impaired cortical connectivity. WM BOLD fMRI (1–3) remain controversial and had very limited impact on the fMRI community, due to low sensitivity (lesser blood flow compared to gray matter (GM) (4)) and poorly understood mechanisms. Here, we use Arterial Spin Labeling (ASL), a non-invasive, quantitative, reproducible fMRI technique (5,6), to investigate Cerebral Blood Flow (CBF) dynamics in WM at high field. The WM CBF was recently measured in short times using optimal sampling strategy ASL at 7T (7) and pcASL at 3T (8). Here we extend this claim to standard 2D-EPI PASL at 7T before quantifying CBF changes in GM and WM during finger-tapping.

METHODS:

Experiments were performed on a 7T head-only Siemens MR scanner and 32 ch. array receiver coil (Nova). ASL data was acquired with a FAIR-QUIPSSII (9,10) 2D-EPI sequence and recommended parameters (11): TR/TE=3000/17ms, BW=2004 Hz/pix, FOV=192x192, matrix 64x64 (res. 3x3mm), 5mm thickness (6 slices), TI1/TI2=700/1800ms. 125 {control/tag} pairs were recorded per session (acquisition time 12min33s). CBF maps were calculated based on simple kinetics modeling and PASL scheme (12,13) using T1,blood=2170ms (14) and T2* (tissue/blood) = 25/12ms (15).

Voxel-wise perfusion-weighted measurements in GM/WM (n=4 volunteers) were compared to the noise distribution using one-tailed student t-tests as in (7,8) to determine whether WM CBF could be measured using our protocol. The finger-tapping task consisted in 12 blocks of [30s ON/OFF] (left-hand, 1 Hz). ASL fMRI data was acquired once (n=1) or twice (n=5) in right-handed healthy volunteers (total: 11 runs). ASL data were motion-corrected, smoothed (FWHM=3mm) and processed using spm12 (16) to extract the 10 most activated GM voxels (GM10) based on perfusion-weighted images generated after control/tag signal interpolation at each TR.

Two ROIs were drawn in contralateral/ipsilateral WM (WMc/WMi) in the Corona Radiata below the motor cortex. CBF and BOLD signals were extracted from the raw ASL signal (motion-corrected, unsmoothed) and averaged over all blocks. The BOLD and CBF increase in GM10, WMc and WMi were tested for statistical significance (one-tailed paired t-tests, 2 metrics, 3 ROIs; Bonferroni threshold p=0.05/6=0.008).

RESULTS:

Representative CBF maps, WM/GM masks and distributions are illustrated in Fig.1A-C. Most WM voxels presented measurable CBF based on uncorrected p-values/Z-scores (Fig.1D). Results after Bonferroni correction leveled with (8), despite their higher SNR (pcASL, larger voxel size).

The finger-tapping task elicited a robust cerebral response (Fig.2A) in BOLD and perfusion-weighted contrast in the motor cortex (Fig.2B-C, single-subject, FWE p=0.05 threshold). Based on the CBF and BOLD signal in GM10 (Fig.3, n=11 runs), the last 27/21s of stimulation/rest period were averaged to quantify CBF in GM10, WMc and WMi.

During stimulation, BOLD and CBF increased in GM10 (Fig.4A-D, p=3.10-7/3.10-8), and CBF increased in WMc (Fig.4E, p=0.005). No increase was found for BOLD in WMc/WMi (p>0.2), nor for CBF in WMi (p>0.008). CBF changes in WMc and GM10 masks (Fig.5A) during stimulation were respectively +27±25% and +77±8% (Fig.5B).

DISCUSSION:

CBF could be quantified in most of WM using 2D-EPI ASL sequence at 7 Tesla in 12min. A net CBF increase was measured in contralateral WM and GM regions during finger-tapping. Some ipsilateral cortical regions exhibited a small positive BOLD response (although below the FWE threshold) making ipsilateral WM regions imperfect controls for ASL fMRI.

Although WM activation has been previously reported in the internal capsule (IC) during finger-tapping (2,3), we did not observe BOLD changes in WM (that could not be explained by partial-volume effects), potentially due to the lower CBF increase in WM versus GM. Our CBF results are consistent with signals following the corticospinal tract that originates in the primary motor cortex, passes through the Corona Radiata, IC, pons, pyramids, before dividing into the anterior/lateral corticospinal tracts connected to the proximal/distal muscles.

We demonstrate that quantitative fMRI can be performed in WM. Due to low SNR, longer fMRI paradigms and/or further sequence improvements are needed at high field – such as background suppression (17) / optimized inversion pulses (18,19) - before considering single-voxel analysis. Further analyses shall include increased brain coverage and refined sub-sections of the Corona Radiata. Combining diffusion, BOLD, VASO and ASL fMRI (20) and reproducing results in WM tracts involved in other cognitive processes is also of prime importance to better understand the mechanisms at play.

CONCLUSION:

Quantitative WM fMRI hast the potential to improve our understanding of fMRI mechanisms in various brain tissues, bridge the existing gap between structural and functional connectivity; as well as better characterizing neurodegenerative, inflammatory or vascular diseases that affect WM.

Acknowledgements

This work was supported by the Centre d'Imagerie Biomédicale (CIBM) of the University of Lausanne (UNIL), the Swiss Federal Institute of Technology Lausanne (EPFL), the University of Geneva (UniGe), the Centre Hospitalier Universitaire Vaudois (CHUV), the Hôpitaux Universitaires de Genève (HUG) and the Leenaards-Jeantet Foundations.

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Figures

Figure 1. Cerebral Blood Flow (CBF) map (A), WM/GM masks (B, white/gray area) and CBF distribution in WM and GM (C) in a single subject using the FAIR-QUIPSII ASL protocol detailed in the methods section. D. Comparison of the proportion of WM voxels with measurable CBF with literature at 3T (8) and 7T (7). Similar results are obtained at uncorrected level (fourth column) and after Bonferroni correction (last column).

Figure 2. A.Raw timecourse in cortical gray matter during finger-tapping (single subject, n=10 voxels). Note that the difference between control and tag signal is higher during stimulation (TASK:ON) than resting periods (TASK:OFF). B.Activation maps (FWE, p<0.05) for BOLD (top row) and ASL (bottom row) overlayed on proton-density weighted signal and CBF map, respectively. Compared to ASL, BOLD activation maps highlighted cerebral activity in additional areas, including ipsilateral motor cortex and supplementary motor areas (white arrows). Activated clusters were found larger on BOLD contrast and presented higher T scores. Very few individual voxels were found activated in WM areas.

Figure 3. A. Average BOLD signal in the 10 most activated voxels in the motor cortex (GM10), normalized to the BOLD signal at rest and averaged between runs and subjects (n=11). B. Corresponding CBF changes in GM10 during finger-tapping (gray area) and rest. Both BOLD and CBF reach a plateau in the last 27s and 21s of stimulation and rest, respectively (blue arrows).

Figure 4. ROI analysis of BOLD (A-C, blue) and CBF signal (D-F, orange) in contralateral gray and white matter (GM10/WMc, left/middle columns) and ipsilateral white matter (WMi, right column) during finger-tapping. As expected, BOLD and CBF increased in GM10 (A-D, p=3.10-7/3.10-8). A significant CBF increase was also measured in WMc (E, Bonferroni threshold p<0.008) but not in WMi. There was no BOLD increase in WMc or WMi at ROI level (C-F). Error bars represent standard deviations.

Figure 5. A.CBF map and masks in contralateral GM (before selection of 10 most activated voxels for GM10 ROI), contralateral WM (WMc) and ipsilateral WM (WMi). B. BOLD (blue) and CBF (orange) changes in GM10, WMc and WMi during fMRI. Only GM10 presented elevated BOLD signal during finger-tapping (+2.2±0.3%, p<0.001). Both contralateral GM and WM showed elevated CBF during finger-tapping (+77±8%, p<0.001; and +27±25%, p<0.01) at ROI level. The black dotted line represents the BOLD and CBF level at rest. [** p<0.01; *** p<0.001]. Errors bars represent standard deviations.

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