Mapping columnar axis of motion in human area MT using SI-SS-VASO at 7T

Poster No:


Submission Type:

Abstract Submission 


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

First Author:

Alessandra Pizzuti  
Maastricht University
Maastricht, Netherlands


Amaia Benitez-Andonegui  
Maastricht University
Maastricht, Netherlands
Laurentius Huber  
Maastricht University
Maastricht, Netherlands
Omer Faruk Gulban  
Maastricht University
Maastricht, Netherlands
Sriranga Kashyap  
Maastricht University
Maastricht, Netherlands
Luca Vizioli  
CMRR, University of Minnesota
Minneapolis , MN
Steen Moeller  
CMRR, University of Minnesota
Minneapolis , MN
Mehmet Akcakaya  
CMRR, University of Minnesota
Minneapolis , MN
Kamil Ugurbil  
CMRR, University of Minnesota
Minneapolis , MN
Judith Peters  
Maastricht University
Maastricht, Netherlands
Rainer Goebel  
Maastricht University
Maastricht, Netherlands


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 [1]. 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 [5] (0.7mm iso; TR/TE=6000ms/2.39ms, TI=800ms/2750ms, FA=4°/5°, GRAPPA=3), a functional localizer run [6] (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 [8] 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 [3]) 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.
Supporting Image: Fig1.png
Supporting Image: Fig2.png


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:


Perception, Attention and Motor Behavior:

Perception: Visual 2


Cortical Columns
Cortical Layers
Other - VASO, 7T fMRI, Axis of Motion

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
Structural MRI

For human MRI, what field strength scanner do you use?


Which processing packages did you use for your study?

Brain Voyager
Other, Please list  -   LAYNII, ITK-SNAP

Provide references using author date format

[1] 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
[2] 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

[3] Huber L. et al. (2020), ‘LAYNII: A software suite for layer-fMRI’. bioRxiv. doi:10.1101/2020.06.12.148080

[4] 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:

[5] Marques JP. et al. (2010), ‘MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.’ Neuroimage. vol. 49, no. 2, pp. 1271-1281. doi:10.1016/j.neuroimage.2009.10.002

[6] 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

[7] 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

[8] 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

[9] 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