Jelle A van Dijk1,2, Alessio Fracasso1,2,3, and Serge O Dumoulin1,2,4
1Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, 2Experimental Psychology, Utrecht University, Utrecht, Netherlands, 3Radiology, University Medical Centre Utrecht, Utrecht, Netherlands, 4Applied and Experimental Psychology, VU University, Amsterdam, Netherlands
Synopsis
Nearly all fMRI
analysis methods assume a linear relationship between local neuronal activity
and the BOLD signal. This assumption is supported for fMRI at conventional
resolutions (>1 mm isotropic). We assess whether linearity of the BOLD
signal holds at sub-millimetre resolution, over cortical depth. We acquired
functional GE 3D-EPI data at 0.7 mm isotropic resolution (TR/TE = 57/28 ms).
Stimuli consisted of moving circular sine gratings at 80%, 20%, and 5%
contrast. Our results suggest that response profiles for one contrast are linearly
scaled response profiles of any other contrast.
Introduction
A fundamental
assumption of nearly all fMRI analysis methods is that the relationship between
local neuronal activity and the blood oxygenation dependent (BOLD) signal is
linear. Experimental evidence supports this notion for fMRI at conventional
resolutions (>1mm isotropic). Recent advances in ultra-high field MRI (7T)
allowed for high-resolution (sub-millimetre) fMRI1. A novel and
promising application of sub-millimetre fMRI is laminar imaging, i.e. measuring
BOLD (and other responses) across the thickness of the cortex, in particular to
dissociate feed forward and feedback signals as they arrive at different lamina2.
However, blood supply over cortical depth is mainly dependent on diving vessels
that strongly affect the BOLD response3. This may also affect the
BOLD linearity over cortical depth. In this study, we collected 0.7 mm
isotropic fMRI data to assess the linearity of the BOLD response over cortical
depth. Methods
The data were acquired
using a gradient echo 3D echo planar imaging sequence (EPI, 2 shots per slice, 34 slices) on a Philips Achieva 7T scanner with
custom-built 32-channel surface receive coils. Imaging parameters: TR/TE = 57/28 ms, flip angle: 20°,
FOV = 131 x 131 x 24 mm (34 slices). We collected 91 dynamics per run, and 6-7
functional runs per participant, per task condition, acquiring a total of 72
minutes of fMRI data per subject. For each run, we additionally collected 5 volumes with the opposite phase encoding
direction to correct for geometrical distortions induced by the B0 field.
Participants (5, all male, age range 23-44 years) viewed sine-wave gratings in
a circular aperture (4.75 deg radius) for 12 seconds, with 5, 20, or 80% luminance
contrast, alternated with 12 seconds rest. Stimuli are shown in Figure 1, left
bottom corner. All contrasts were shown for 5 blocks per run. Subjects were
asked to either passively view the stimulus while fixating the centre, or
perform a one-back task (grating orientation). We computed percentage BOLD
amplitude change for each contrast. The statistical maps were
distortion-corrected using the top up method4, and co-registered to
a high-resolution (0.65 mm isotropic) MP2RAGE5 (TI1 = 900 ms, TI2 = 2750 ms, TR =
5000 ms, TE = 2.45 ms) whole brain
anatomy (Figure 1C). Computational cortical layers were defined using a
volume-preserving distance estimation between the white matter-grey matter
border, and the grey matter-cerebrospinal fluid border, using CBS tools6 (Figure
1B). Early visual cortex regions of
interest were defined using separate population receptive field (pRF) mapping7
sessions (Figure 1A, V1). Results & Discussion
We obtained strong
(>1%) BOLD responses throughout early visual cortex for all contrasts and
for all subjects (for an example see Figure 1D). Signal amplitudes systematically
increased with increasing stimulus contrast (Figure 1E and F). This increase
was non-linear, as expected from the known non-linear contrast response
function in visual cortex. The BOLD amplitude also increased consistently as a
function of cortical depth, where BOLD amplitude systematically increased
towards the pial surface (example subject Figure 1F). Average results are shown in Figure 2A-C.
Increases in BOLD signal amplitude towards the pial surface were largest in V1
and smaller in V2 and V3, though not significantly different. The BOLD response
profile over cortical depth for a specific contrast, could be accurately
modelled (variance explained of approximately 98%) by applying a single, linear
scaling factor to the response profile of any other contrast, as shown by the
dashed lines in Figure 2D-F. This linear dependence strongly suggests a laminar
response profile that is independent of the stimulus strength. We found no
significant differences between the passive viewing and one-back task
conditions. Our results provide evidence for a linear response function across cortical
depth, despite known blood supply differences across lamina. Conclusion
Our results suggests
that the assumption that BOLD responses are linear holds not only for standard
resolution fMRI, but also for sub-millimetre fMRI, and in particular for
laminar imaging.Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641805. References
1. Dumoulin, S. O., Fracasso, A., van der Zwaag, W., Siero, J.
C. W., & Petridou, N. (2017). Ultra-high
field MRI: Advancing systems neuroscience towards mesoscopic human brain
function. NeuroImage, (September 2016), 0–1.
https://doi.org/10.1016/j.neuroimage.2017.01.028
2. Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierachical processing in the
primatecerebral cortex. Cerebral Cortex, 1(1), 1–47. https://doi.org/10.1093/cercor/1.1.1
3. Siero, J. C. W., Petridou, N., Hoogduin, H., Luijten, P.
R., & Ramsey, N. F. (2011). Cortical
depth-dependent temporal dynamics of the BOLD response in the human brain. Journal
of Cerebral Blood Flow and Metabolism : Official Journal of the International
Society of Cerebral Blood Flow and Metabolism, 31(10), 1999–2008.
https://doi.org/10.1038/jcbfm.2011.57
4. Truong, T. K., Chen, B., & Song, A. W. (2008).
Integrated SENSE DTI with correction of susceptibility- and eddy
current-induced geometric distortions. NeuroImage, 40(1), 53–58.
https://doi.org/10.1016/j.neuroimage.2007.12.001
5. Marques, J. P., Kober, T., Krueger, G., Van Der Zwaag, W.,
Van De Moortele, P.-F., & Gruetter, R. (2009). MP2RAGE, a self bias-field corrected sequence for
improved segmentation and T 1 -mapping at high field. NeuroImage, 49,
1271–1281. https://doi.org/10.1016/j.neuroimage.2009.10.002
6. Bazin, P. L., Weiss, M., Dinse, J., Schäfer, A.,
Trampel, R., & Turner, R. (2014). A computational framework for ultra-high
resolution cortical segmentation at 7 Tesla. NeuroImage, 93,
201–209. https://doi.org/10.1016/j.neuroimage.2013.03.077
7. Dumoulin, S. O., & Wandell, B. A. (2008).
Population receptive field estimates in human visual cortex. NeuroImage,
39(2), 647–660. https://doi.org/10.1016/j.neuroimage.2007.09.034