Hypercapnia task response suggests ADC-fMRI is not sensitive to vascular effects
Poster No:
1987
Submission Type:
Abstract Submission
Authors:
Arthur Spencer1, Inès de Riedmatten1, Jean-Baptiste Perot1, Jasmine Nguyen-Duc1, Filip Szczepankiewicz2, Ileana Jelescu1
Institutions:
1Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 2Lund University, Lund, Sweden
First Author:
Arthur Spencer
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Co-Author(s):
Inès de Riedmatten
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Jean-Baptiste Perot
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Jasmine Nguyen-Duc
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Ileana Jelescu
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)
Lausanne, Switzerland
Introduction:
Diffusion functional MRI (dfMRI) offers a functional contrast sensitive to microstructural changes during neural activity, thus has the potential to be more specific to neuronal activity than BOLD (Le Bihan, 2006). However, the diffusion-weighted MRI signal is sensitive to vascular contrast due to T2 weighting (Miller, 2007). To increase specificity to neuromorphological coupling by reducing vascular contributions, apparent diffusion coefficient (ADC)-fMRI uses interleaved pairs of b-values to calculate an ADC timeseries (De Luca, 2019; Nicolas, 2017). An isotropic ADC-fMRI sequence capable of detecting activity in both grey and white matter, in response to visual and motor tasks, was previously introduced (Spencer, 2024). We performed a breath-hold hypercapnia task (Zvolanek, 2023) to demonstrate that this task induces a positive BOLD response due to the vasodilating effects of accumulated CO2, but does not impact the ADC-fMRI signal.
Methods:
The task consisted of four epochs of breath-hold between periods of resting breathing (Fig 1A). For each subject, we acquired two runs with both ADC-fMRI and BOLD-fMRI. End-tidal CO2 (pETCO2) was calculated from gas analyser recordings by interpolating between the peak CO2 at each exhale (Fig 1C).
N=15 healthy volunteers were scanned using a 3T Siemens Prisma MRI system. ADC-fMRI timeseries were calculated from alternating b-values of 200 and 1000 s mm-2 (Fig 1B) acquired using a spin-echo sequence with isotropic diffusion encoding compensated for cross-terms with background field gradients (Szczepankiewicz, 2021), to minimise sensitivity to underlying fibre orientations and BOLD contributions. BOLD-fMRI data were acquired with a multi gradient echo sequence. Acquisition parameters are shown in Fig 1D. Functional data underwent standard preprocessing (Spencer, 2024), followed by high-pass filtering (>0.01Hz). After rejecting runs with poor task compliance or unreliable gas analyser measurements, 20 runs were included from 11 subjects for ADC-fMRI (and associated b200- and b1000-dfMRI time courses), and 22 runs from 12 subjects for BOLD-fMRI.
We measured association between pETCO2 traces (convolved with the haemodynamic response function and detrended up to 3rd order polynomials) and functional timeseries for BOLD-fMRI, ADC-fMRI, and each separate b-value timeseries. To account for heterogeneity in haemodynamic delays and gas analyser delays, this association was measured voxelwise using cross-correlation over a range of lags [-24,0] s in steps of 1 s. Voxelwise peak correlation (rmax) values were converted to z-scores and transformed to MNI standard space. In each subject, variance explained by pETCO2 was measured by rmax2, averaged across voxels. Intra- and intersubject similarity in z-score maps were measured with z-transformed Pearson correlation coefficient between z-score maps. We compared the intra- and intersubject similarity, and variance explained between contrasts with two-tailed unpaired Mann-Whitney U-tests with Bonferroni correction.
N=15 healthy volunteers were scanned using a 3T Siemens Prisma MRI system. ADC-fMRI timeseries were calculated from alternating b-values of 200 and 1000 s mm-2 (Fig 1B) acquired using a spin-echo sequence with isotropic diffusion encoding compensated for cross-terms with background field gradients (Szczepankiewicz, 2021), to minimise sensitivity to underlying fibre orientations and BOLD contributions. BOLD-fMRI data were acquired with a multi gradient echo sequence. Acquisition parameters are shown in Fig 1D. Functional data underwent standard preprocessing (Spencer, 2024), followed by high-pass filtering (>0.01Hz). After rejecting runs with poor task compliance or unreliable gas analyser measurements, 20 runs were included from 11 subjects for ADC-fMRI (and associated b200- and b1000-dfMRI time courses), and 22 runs from 12 subjects for BOLD-fMRI.
We measured association between pETCO2 traces (convolved with the haemodynamic response function and detrended up to 3rd order polynomials) and functional timeseries for BOLD-fMRI, ADC-fMRI, and each separate b-value timeseries. To account for heterogeneity in haemodynamic delays and gas analyser delays, this association was measured voxelwise using cross-correlation over a range of lags [-24,0] s in steps of 1 s. Voxelwise peak correlation (rmax) values were converted to z-scores and transformed to MNI standard space. In each subject, variance explained by pETCO2 was measured by rmax2, averaged across voxels. Intra- and intersubject similarity in z-score maps were measured with z-transformed Pearson correlation coefficient between z-score maps. We compared the intra- and intersubject similarity, and variance explained between contrasts with two-tailed unpaired Mann-Whitney U-tests with Bonferroni correction.
·Figure 1
Results:
Group-average z-score maps (Fig 2A) and variance explained (Fig 2B) confirm strong association of BOLD-fMRI with pETCO2. Both b200- and b1000-dfMRI had moderate association with pETCO2 due to T2 weighting of the signal, while ADC-fMRI showed little association. Intra- and intersubject similarity in z-score maps were lower for ADC-fMRI than dfMRI and BOLD-fMRI (Fig 2C&D). This suggests the correlations found between ADC-fMRI and pETCO2 are not reflecting true associations with physiological signals, but spurious correlations or respiration-related movement.
It is possible that the low association of ADC-fMRI with respiration is due in part to lower SNR (Spencer, 2024). This must be assessed in future with SNR-matched data.
It is possible that the low association of ADC-fMRI with respiration is due in part to lower SNR (Spencer, 2024). This must be assessed in future with SNR-matched data.
·Figure 2
Conclusions:
ADC-fMRI is insensitive to purely vascular signals. Thus, previously reported activation using ADC-fMRI likely reflects neuromorphological coupling rather than BOLD contribution.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Non-BOLD fMRI 1
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Cerebral Blood Flow
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
MRI
Other - Diffusion MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Le Bihan, D. (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.
Miller, K. L. (2007). Evidence for a vascular contribution to diffusion FMRI at high b value. Proceedings of the National Academy of Sciences, 104(52), 20967-20972.
Nicolas, R. (2017). Comparison of BOLD, diffusion-weighted fMRI and ADC-fMRI for stimulation of the primary visual system with a block paradigm. Magnetic resonance imaging, 39, 123-131.
Spencer, A. P. (2024). Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI. bioRxiv, 2024-10.
Zvolanek, K. M. (2023). Comparing end-tidal CO2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI. NeuroImage, 272, 120038.