Denis Le Bihan1, Luisa Ciobanu1, Yukiko Masaki1,2, and Erwan Selingue1
1NeuroSpin, Gif-sur-Yvette, France, 2Shionogi & Co., Ltd., Osaka, Japan
Synopsis
BOLD and diffusion fMRI signal response time courses following rat visual stimulation differ in time, amplitude and shape.
Introduction
Blood
oxygenation level-dependent (BOLD) fMRI is widely used to investigate brain
activity. However, BOLD fMRI, relying on the neurovascular coupling, is only “remotely”
related to neuronal activation, spatially and temporally, and may fail in some
conditions which impair neurovascular coupling. In contrast, diffusion fMRI1
has been shown to be more directly linked to neuronal activation exhibiting
neuronal responses even when neurovascular coupling is abolished2-3,
although its putative mechanism (neuromechanical coupling and cell swelling)
remains disputed. To better compare BOLD and DfMRI temporal patterns
we implemented a linescan approach which allows high temporal resolution acquisitions.Methods - Protocol
11
male Sprague Dawley rats (250-350 g) were scanned under medetomidine
anesthesia (s.c.) and control of body temperature and physiological parameters.
Two visual stimulation protocols (blue led light flashing in front of the right
eye) were implemented: a block paradigm design (BP) with a 15s stimulation
window (n=6) and an event-related design (ER) with a 3s stimulation (n=5) (Fig.1).
All animal procedures used were
approved by an institutional Ethic Committee and were conducted in strict
accordance with the recommendations and guidelines of the European Union. Methods - fMRI
All
fMRI acquisitions were conducted using a 17.2T scanner (Bruker, Germany) equipped
with a pair of surface/volume coils (Rapid
Biomedical, Germany). RARE (TR/TE = 2000/14 ms, in plane resolution = 90 x 90 µm2, slice thickness = 1mm, 5 slices) and GE
BOLD fMRI images (TR/TE = 1000/10 ms, in plane resolution= 240 x 240 mm2, slice thickness = 1mm, 5 slices) were
first acquired to identify activation loci, and especially the left superior
colliculus. Linescan diffusion-weighted spin echo images were subsequently acquired along a
vertical line set across the superior colliculus (Fig.2) with the following
parameters: flip angles = 110°/180° (empirically optimized), TR/TE = 250/15 ms,
slice/line thickness=1.2/1 mm, in line resolution 100 µm, b = 0, 1000 and 1800 s/mm². Runs of 1440
signals were acquired, resulting of 8 blocks of 180 time points for BP and 40
blocks of 36 time points for ER. 4-5 runs were acquired for each b value to
increase SNR.
Methods - Processing and Analysis
After
Fourier transform signal profiles were displayed for each time point, resulting
in 2D or 3D “space-time” color maps (Fig.2). The analysis was performed for each subject on
an individual run basis and after run averaging at each b value using a homemade
software (Matlab; MathWorks, Natick, MA). To avoid any bias with prior
response function models4, regions of interest centered on the
activation spots were drawn of the 2D space-time maps. The degree of activation
was estimated for those ROIs and tested for significance using the General
Linear Model after denoising5, detrending and correction for time autocorrelation. The peak
amplitude and time to peak within the activation spots were also estimated for
each b value, for each subject and after averaging of the time courses across
subjects. Group statistical analysis was performed using a Wilcoxon-ranked-sum
test using p=0.05 as significance threshold. Results
An
example of 3D and 2D space-time activation map for BP is shown on Fig.2 (average of 4 runs for b=0 and b=1800s/mm², respectively) with the
corresponding ROI activation time course
shown on Fig.2. The amplitude of the response is larger for b=1800s/mm²
and bimodal with a first peak ahead of the b=0 response peak by 0.5s and a
shoulder at the end of the stimulation (the location of this shoulder has been
found close to that corresponding to the main activation spot, but not always
exactly colocalized). This triple pattern (earlier response for b=1800s/mm²,
peak amplitude and shoulder increasing with b value) wa consistent and
significant at group level (Fig.3) both for BP and ER. For ER the mean peak
amplitude reached 0.64% and 1.73% for b=0 and 1800s/mm², respectively (p=0.016)
and the mean time-to-peak 2.65s and 2.15s for b=0 and 1800s/mm², respectively
(p=0.032). Discussion
The
mechanisms underlying the diffusion fMRI response have been debated6,7.
Here, the difference in the response patterns confirm that BOLD and diffusion
fMRI rely on different mechanisms. The increase of the diffusion fMRI response
with b value, as reported earlier1, also points to a genuine
diffusion mechanism, ie a change in tissue microstructure occurring upon
activation1,2,3,8, possibly cell swelling. However, the shape of the
diffusion response remains intriguing. A very similar response pattern has also
been observed in the sensory cortex following forepaw stimulation9 (Fig.2 in reference 9). While the first peak likely corresponds to the neuronal
activity1,2,3,,8,9 one might speculate that the second could be linked
to astrocyte activity. Schummers et al.10 have reported stimulus
locked astrocyte response in the visual cortex of ferrets occurring right at
the end of the stimulus (Fig.4B in reference 10) and diffusion MRI has been
shown to be sensitive to volume changes associated to astrocyte activity status11.Conclusion
High
temporal resolution diffusion fMRI using linescan shows that the signal response
time course following visual stimulation in rat visual system significantly
differs from the BOLD response, in time, amplitude and shape, suggesting
different mechanisms for the two approaches (neurovascular coupling for BOLD
and neuromechanical coupling for DfMRI). Acknowledgements
No acknowledgement found.References
1.Le Bihan, D.,
Urayama, S., Aso, T., et al. Direct and fast detection of
neuronal activation in the human brain with diffusion MRI. Proc Natl Acad Sci U
S A. 2006; 103: 8263-8268.
2.Abe, Y., Tsurugizawa,
T., Le Bihan, D.. Water diffusion closely reveals neural
activity status in rat brain loci affected by anesthesia. PLoS Biol 2017; 15: e2001494.
3.Tsurugizawa, T., Ciobanu, L., Le Bihan, D.
Water diffusion in brain cortex closely tracks underlying neuronal activity.
Proc Natl Acad Sci U S A 2013; 110: 11636-11641.
4.Aso, T., Urayama, S.,
Poupon, C., et al. An intrinsic diffusion response function
for analyzing diffusion functional MRI time series. Neuroimage 2009; 47:
1487-1495.
5.Veraart, J. Fieremans, E, Novikov, D.S. Diffusion MRI
noise mapping using random matrix theory. Magn. Res. Med. 2016; 76: 1582-1593.
6.Miller, K.L., Bulte,
D.P., Devlin, et al. Evidence for a vascular contribution to
diffusion FMRI at high b value. Proc Natl Acad Sci U S A 2007; 104: 20967-20972.
7.Bai, R., Stewart,
C.V., Plenz, et al. Assessing the sensitivity of diffusion MRI
to detect neuronal activity directly. Proc Natl Acad Sci U S A 2016; 113: E1728-1737.
8.Nunes, D., Ianus, A., Shemesh, N.
Layer-specific connectivity revealed by diffusion-weighted functional MRI in
the rat thalamocortical pathway. Neuroimage 2019; 184: 646-657.
9.Nunes D, Gil, R, Shemesh N. A rapid-onset
diffusion functional MRI signal reflects neuromorphological coupling dynamics.
2020; arXiv: 2001.08508.
10.Schummer J, Hongbo Y, Sur M. Tuned
Responses of Astrocytes and Their Influence on Hemodynamic Signals in the
Visual Cortex. Science 2008; 320: 1638-1643.
11.Debaker C, Djemai B, Ciobanu L et al. Diffusion
MRI reveals in vivo and non invasively changes in astrocyte function induced by
an aquaporin-4 inhibitor. PloSONE 2020; 15(5): e0220702.