Alessandra Pizzuti1, Amaia Benitez-Andonegui1, Laurentius Huber1, Omer Faruk Gulban1, Sriranga Kashyap1, Luca Vizioli2, Steen Moeller2, Mehmet Akcakaya2, Kamil Ugurbil2, Judith Peters1, Rainer Goebel1
1Maastricht University, Maastricht, Netherlands, 2CMRR, University of Minnesota, Minneapolis , MN
Ultra-high field (>=7T) functional magnetic resonance imaging (fMRI) provides a unique opportunity to investigate the columnar and laminar organization of the human brain in vivo. The middle temporal area in humans (hMT) shows a selective columnar response to directions or axes of motion using conventional BOLD-fMRI contrast with rapid gradient-echo (GE) imaging [7,9]. Although GE-BOLD has been widely applied in fMRI studies, its spatial specificity is reduced by oxygenation changes in draining veins . Alternatives like SS-SI-VASO offer higher spatial specificity to the microvasculature and improved quantifiability, at the cost of reduced signal sensitivity and increased acquisition time with respect to GE-BOLD [2,4]. Here, we investigated for the first time if axes of motion in hMT can be mapped using SS-SI-VASO. Furthermore, we investigated the effect of thermal noise suppression on the sensitivity to detect axes of motion responses.
On a Magnetom 7T (Siemens; 32-head NOVA coil; 1 male), we acquired an anatomical MP2RAGE  (0.7mm iso; TR/TE=6000ms/2.39ms, TI=800ms/2750ms, FA=4°/5°, GRAPPA=3), a functional localizer run  (2mm iso; TR/TE/FA=1000ms/22ms/55°, 57 slices and multi band factor=3) to locate area hMT [28 repetitions of outward/inward motion of dots (10s), followed by static dots (10s)], and 4 runs to map the axes of motion [4 repetitions of moving dots (24s) were presented in 4 directions (0°-180°, 45°-225°, 90°-270°, 135°-315°), alternated with flickering dots (24-29s)]. Stimuli were presented in a central aperture (diameter 10° visual angle). High-resolution functional data of GE-BOLD and cerebral blood volume were acquired with SS-SI-VASO with a 3D EPI readout, TR/TE/FA=2410ms/25ms/26°, TI=50ms/650ms, 0.8mm iso resolution, 26 slices, GRAPPA=3 and in-plane partial Fourier 6/8.
Anatomical data were coregistered to the high-resolution functional data (ITK-SNAP), upsampled to 0.15mm iso resolution (AFNI) and segmented (ITK-SNAP). Functional localizer data were motion corrected (SPM12), upsampled to 0.8mm iso (AFNI), coregistered to functional space (ITK-SNAP) and high-pass filtered (BrainVoyager 22). We ran a general linear model (GLM) in BrainVoyager to define a region of interest (ROI) in the left hMT by selecting voxels that showed a significant response [p<0.00001] to the flickering vs static dots contrast. We applied NORDIC denoising algorithm for thermal noise reduction  to the high-resolution functional data after motion correction (SPM12), and explored its impact compared to data without denoising (here on referred to as Standard). A GLM restricted to the hMT-ROI was used to obtain activation maps for each axis of motion (by contrasting each condition vs flickering dots) for both BOLD and VASO time series. Layer analysis (LAYNII ) was done in the main activated area (see red box, Fig.1A). Axis of motion selectivity tuning and preference maps were computed using custom code (Matlab 2020), by assigning to each voxel the direction to which its response (t-value) was maximal (see Fig. 2).
Fig.1 shows example activation maps based on the all motion directions vs flickering contrast, after averaging all runs. As expected, NORDIC increases t-values, especially for activation maps obtained with VASO contrast. As for the layer profiles, unlike BOLD signal, the percent signal change for VASO data does not increase for superficial layers, indicating that the draining vein effects are mitigated. This result is visible for Standard and NORDIC cases. As reported in Fig.2, we found voxel-specific selectivity for the different axes of motion, confirming results from previous studies.
Although more subjects are scheduled to get a more conclusive picture, this study shows for the first time the feasibility of measuring axis of motion in extrastriate area hMT using SS-SI-VASO, as well as the signal sensitivity improvement in VASO data due to NORDIC pre-processing.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Novel Imaging Acquisition Methods:
Non-BOLD fMRI 1
Perception, Attention and Motor Behavior:
Perception: Visual 2
Other - VASO, 7T fMRI, Axis of Motion
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 Beckett AJ. et al. (2019), ‘Comparison of BOLD and CBV using 3D EPI and 3D GRASE for cortical layer fMRI at 7T’. bioRxiv. doi:10.1101/778142
 Huber L. et al. (2019), ‘Non-BOLD contrast for laminar fMRI in humans: CBF, CBV, and CMRO2’. Neuroimage. vol.197, pp. 742-760. doi:10.1016/j.neuroimage.2017.07.041
 Huber L. et al. (2020), ‘LAYNII: A software suite for layer-fMRI’. bioRxiv. doi:10.1101/2020.06.12.148080
 Lu H et al. (2003), ‘Functional magnetic resonance imaging based on changes in vascular space occupancy’. Magnetic Resonance in Medicine. vol. 50, no. 2, pp. 263-274. doi:https://doi.org/10.1002/mrm.10519
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 Moeller S. et. al (2010), ‘Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI’. Magn Reson Med., vol. 63, no. 5, pp. 1144-1153. doi:10.1002/mrm.22361
 Schneider M. et al. (2019), ‘Columnar clusters in the human motion complex reflect consciously perceived motion axis’. PNAS. vol. 116, no. 11. doi:10.1073/pnas.1814504116
 Vizioli L. et al. (2020), ‘A Paradigm Change in Functional Brain Mapping: Suppressing the Thermal Noise in fMRI.’ bioRxiv. doi:10.1101/2020.11.04.368357
 Zimmermann J. et al. (2011), ‘Mapping the Organization of Axis of Motion Selective Features in Human Area MT Using High-Field fMRI’. PLOS ONE. vol. 6, no. 12. doi:10.1371/journal.pone.0028716