Complementary BOLD- and ADC-fMRI activation maps in the rat visual system at 14T
Poster No:
1988
Submission Type:
Abstract Submission
Authors:
Jean-Baptiste Perot1, Andreea Hertanu1, Arthur Spencer1, Jasmine Nguyen-Duc1, Nikolaos Molochidis2, Matthew Budde3, Maxime Yon4, Ileana Jelescu1
Institutions:
1Lausanne University Hospital, Lausanne, Switzerland, 2University of Geneva, Geneva, Switzerland, 3Medical College of Wisconsin, Milwaukee, WI, 4Université de Rennes, Rennes, France
First Author:
Co-Author(s):
Introduction:
While showing high sensitivity to neuronal activation in grey matter, BOLD-fMRI remains an indirect contrast due to its vascular origin. Diffusion fMRI could serve as a more direct contrast sensitive to cellular deformations during firing (Le Bihan, 2006). However, isolating this effect from BOLD contribution remains a challenge. Acquisition strategies to minimize vascular contributions to Apparent Diffusion Coefficient (ADC) time series have been recently shown to yield a distinct fMRI contrast (Nguyen-Duc, 2024) with increased sensitivity to white matter (Spencer, 2024).
Here, we compared BOLD and ADC-fMRI responses in the rat brain using a visual stimulation paradigm at different frequencies inducing either positive or negative BOLD in the superior colliculus (SC) (Gil, 2024).
Here, we compared BOLD and ADC-fMRI responses in the rat brain using a visual stimulation paradigm at different frequencies inducing either positive or negative BOLD in the superior colliculus (SC) (Gil, 2024).
Methods:
Sprague Dawley rats (n= 16, 10 females, 200-225g) were scanned on a 14T Bruker MRI system under medetomidine sedation (fig 1A). A T2-weighted image was acquired for anatomical reference (fig 1B). BOLD-fMRI (GRE-EPI) and ADC-fMRI (SE-EPI with spherical tensor encoding and interleaved b-values) runs were acquired twice per stimulation frequency. Bilateral visual stimulation involved 24s of flashing light at 1Hz or 25Hz followed by 16s of rest, repeated 12 times.
Preprocessing included MP-PCA denoising, Gibbs unringing, distortion and motion corrections. A multivariate template was generated from all animals. To reduce assumptions on the ADC-fMRI response function, subject-level general linear model (GLM) was performed on ADC timeseries with boxcar function convolved with Finite Impulse Response (FIR, 4 impulses, 16s window) (Goutte, 2000). Responses in significant voxels were pooled across rats, clustered using k-means and averaged per cluster. BOLD timeseries underwent both a GLM analysis using a boxcar function followed by group-level GLM, as well as FIR analysis and clustering, for comparability with ADC-fMRI.
Preprocessing included MP-PCA denoising, Gibbs unringing, distortion and motion corrections. A multivariate template was generated from all animals. To reduce assumptions on the ADC-fMRI response function, subject-level general linear model (GLM) was performed on ADC timeseries with boxcar function convolved with Finite Impulse Response (FIR, 4 impulses, 16s window) (Goutte, 2000). Responses in significant voxels were pooled across rats, clustered using k-means and averaged per cluster. BOLD timeseries underwent both a GLM analysis using a boxcar function followed by group-level GLM, as well as FIR analysis and clustering, for comparability with ADC-fMRI.
·Figure 1: Experimental design.
Results:
The two BOLD analyses resulted in similar activation maps. At the group-level, 1Hz visual stimulation showed positive BOLD response in the SC, primary visual cortex (V1), and dorsolateral geniculate nucleus (DLGn) (fig 2A). Higher stimulation frequency elicited a negative BOLD response in V1 and in the lateral SC, and a positive response in the medial SC and in the DLGn (fig 2B). Interestingly, in Long Evans rats used in Gil et al., a similar pattern appeared at 15 Hz while 25 Hz stimulation showed only negative response in the SC, suggesting that rat strains present different frequency thresholds for continuous vision.
K-means clustering of the response of voxels responsive to the task with ADC-fMRI showed spatial coherence both at 1Hz (fig 2C) and 25Hz (fig 2E). Clusters were mainly located around the SC, DLGn, and V1, but also in the hippocampus (HC) and retrosplenial cortex (RSC). In regions with high BOLD response (see clusters 2-3 in fig 2D & 2F), such as in the SC, ADC-fMRI response was akin to BOLD, suggesting a vascular contribution. However, other clusters showed negative ADC response in areas of weak positive BOLD response (see cluster 1 in fig 2D & 2F). These clusters were located in the HC and RSC and could reflect activity in the projections from the medial SC to these regions as part of the visual attention process (Benavidez, 2021). Voxels within the same clusters also overlap with the external capsule which drives the projections between V1 and the lateral SC (Oh, 2014).
K-means clustering of the response of voxels responsive to the task with ADC-fMRI showed spatial coherence both at 1Hz (fig 2C) and 25Hz (fig 2E). Clusters were mainly located around the SC, DLGn, and V1, but also in the hippocampus (HC) and retrosplenial cortex (RSC). In regions with high BOLD response (see clusters 2-3 in fig 2D & 2F), such as in the SC, ADC-fMRI response was akin to BOLD, suggesting a vascular contribution. However, other clusters showed negative ADC response in areas of weak positive BOLD response (see cluster 1 in fig 2D & 2F). These clusters were located in the HC and RSC and could reflect activity in the projections from the medial SC to these regions as part of the visual attention process (Benavidez, 2021). Voxels within the same clusters also overlap with the external capsule which drives the projections between V1 and the lateral SC (Oh, 2014).
·Figure 2: BOLD and ADC-fMRI response to 1 Hz and 25 Hz visual stimulation.
Conclusions:
We confirmed that visual stimulation with high frequency could elicit a complex response of the visual network with negative BOLD in V1 and in the lateral SC.
Comparing BOLD-fMRI with ADC-fMRI, we found that despite our efforts to reduce vascular contribution, BOLD response in the SC was still observed in the ADC time series due to strong magnetic susceptibility effects at 14T. However, negative ADC-fMRI response in areas with low BOLD response and in WM support the potential of ADC as a complementary fMRI contrast, particularly at lower field strengths.
Comparing BOLD-fMRI with ADC-fMRI, we found that despite our efforts to reduce vascular contribution, BOLD response in the SC was still observed in the ADC time series due to strong magnetic susceptibility effects at 14T. However, negative ADC-fMRI response in areas with low BOLD response and in WM support the potential of ADC as a complementary fMRI contrast, particularly at lower field strengths.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Novel Imaging Acquisition Methods:
Non-BOLD fMRI 1
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
ANIMAL STUDIES
fMRI CONTRAST MECHANISMS
Vision
Other - Diffusion MRI
1|2Indicates the priority used for review
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2. Gil, R., Valente, M., & Shemesh, N. (2024). Rat superior colliculus encodes the transition between static and dynamic vision modes. Nature Communications, 15(1), Article 1. https://doi.org/10.1038/s41467-024-44934-8
3. Goutte, C., Nielsen, F. A., & Hansen, L. K. (2000). Modeling the haemodynamic response in fMRI using smooth FIR filters. IEEE Transactions on Medical Imaging, 19(12), 1188‑1201. https://doi.org/10.1109/42.897811
4. Le Bihan, D., Urayama, S., Aso, T., Hanakawa, T., & Fukuyama, H. (2006). Direct and fast detection of neuronal activation in the human brain with diffusion MRI. Proceedings of the National Academy of Sciences, 103(21), 8263‑8268. https://doi.org/10.1073/pnas.0600644103
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7. Spencer, A. P. C., Nguyen-Duc, J., Riedmatten, I. de, Szczepankiewicz, F., & Jelescu, I. O. (2024). Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI (p. 2024.10.01.615823). bioRxiv. https://doi.org/10.1101/2024.10.01.615823