Resting-state diffusion fMRI is robust to respiration-induced fluctuations, unlike BOLD-fMRI
Poster No:
1986
Submission Type:
Abstract Submission
Authors:
Inès de Riedmatten1,2, Arthur Spencer1,2, Jasmine Nguyen-Duc1,2, Jean-Baptiste Perot1,2, Filip Szczepankiewicz3, Ileana Jelescu1,2
Institutions:
1University of Lausanne (UNIL), Lausanne, Switzerland, 2Lausanne University Hospital (CHUV), Lausanne, Switzerland, 3Lund university, Lund, Sweden
First Author:
Inès de Riedmatten
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
Co-Author(s):
Arthur Spencer
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
Jasmine Nguyen-Duc
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
Jean-Baptiste Perot
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
Ileana Jelescu
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
University of Lausanne (UNIL)|Lausanne University Hospital (CHUV)
Lausanne, Switzerland|Lausanne, Switzerland
Introduction:
BOLD fMRI indirectly probes neuronal activity via neurovascular coupling, but can be influenced by non-neuronal contributions such as respiration, as shown in breath-hold task experiments (Murphy, 2011; Zvolanek, 2023). Even spontaneous changes in breathing depth or rate can cause substantial BOLD changes driven by arterial CO2 variations (vasodilator), and can obscure true neuronal signals (Birn, 2008; Chang, 2009). Conversely, diffusion fMRI (dfMRI) has been designed to more closely reflect neuronal activity. This contrast is sensitive to transient changes in apparent diffusion coefficient (ADC) resulting from brain activity induced alterations in tissue microstructure (neuromorphological coupling). ADC-fMRI has been shown to reliably capture functional connectivity, in particular detecting white matter (WM) connectivity more robustly than BOLD (de Riedmatten, 2024). Here, we show that dfMRI is more robust to resting breathing signals than BOLD, and therefore has potential to be more specific to resting-state neuronal activity.
Methods:
Two 15-minute resting-state datasets were acquired on a 3T Siemens Prisma MRI system on each subject: 1) multi-echo gradient echo BOLD (11 subjects), 2) dfMRI with alternating b-values (200 and 1000 s mm-2), combined into ADC timeseries (Fig 1A), using a spin echo sequence with isotropic diffusion encoding and cross-term background gradient compensation (Szczepankiewicz, 2019) (10 subjects). Acquisition parameters are summarized in Fig 1B. End-tidal CO2 (PETCO2) was obtained by interpolating between peak CO2 at each exhale from gas analyzer (GA) recordings. After standard pre-processing (Spencer, 2024), a high-pass filter (0.01 Hz) was applied. PETCO2 timeseries were detrended (3rd order polynomial) and convolved with the hemodynamic response function (HRF) (Murphy, 2011). Voxelwise PETCO2-fMRI Pearson correlations were computed for BOLD and dfMRI timeseries with lags from -24 to 0 s (1 s steps) to compensate for the subject-specific GA delay and HRF heterogeneity. Significant PETCO2-fMRI association (p<0.05) was determined by approximating a null distribution using surrogate analysis. For each subject, we generated 5000 surrogate PETCO2 traces by shuffling the phases of the Fourier transformed PETCO2, and ran cross-correlation analysis with 5000 randomly selected voxels from the other subjects. The final null distribution was pooled across all subjects. Individual PETCO2-fMRI r_max correlation maps were converted to z-scores and optimal lag maps (yielding r_max) were generated. Optimal lags were demeaned by each subject's mean gray matter (GM) lag. The percentage variance explained of fMRI timeseries by PETCO2 was calculated as r_max2. The z-score and lag maps were registered to MNI space to calculate group mean maps.
Results:
There is a distinct association between PETCO2 and BOLD, as shown by the correlation profiles peaking coherently at a similar lag (Fig 2A-B) and high z-scores (Fig 2F). Interestingly, PETCO2 is also associated with BOLD in WM, despite reduced vasculature (Biswal, 2017; Gore, 2019). As expected, BOLD shows a longer lag in WM (Fig 2E) (Zvolanek, 2023), consistent with BOLD response heterogeneity (Li, 2019), which challenges simultaneous mapping of GM and WM activity.
For dfMRI, b200 and b1000 timeseries suppress blood water via diffusion-weighting but are still T2-weighted, while ADC substantially attenuates both blood contribution and BOLD T2-weighting. Strategies to mitigate the vascular contribution to the dfMRI contrast make it increasingly more robust to respiration, as shown in gradually flatter correlation profiles (Fig 2A-B), and lower variance explained by PETCO2 (Fig 2C-D). No GM-WM contrast is visible in dfMRI lag maps (Fig 2E).
For dfMRI, b200 and b1000 timeseries suppress blood water via diffusion-weighting but are still T2-weighted, while ADC substantially attenuates both blood contribution and BOLD T2-weighting. Strategies to mitigate the vascular contribution to the dfMRI contrast make it increasingly more robust to respiration, as shown in gradually flatter correlation profiles (Fig 2A-B), and lower variance explained by PETCO2 (Fig 2C-D). No GM-WM contrast is visible in dfMRI lag maps (Fig 2E).
Conclusions:
BOLD is strongly associated with resting breathing, while dfMRI is not and is thus more robust to respiration-induced vascular changes in resting-state, which makes it a promising tool to probe neuronal activity more directly.
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Non-BOLD fMRI 1
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
Neurological
Other - diffusion fMRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Gore, J. C. (2019). Functional MRI and resting state connectivity in white matter - a mini-review. Magnetic Resonance Imaging, 63, 1–11.
Li, M. (2019). Characterization of the hemodynamic response function in white matter tracts for event-related fMRI. Nature Communications, 10(1), 1140.
Murphy, K. (2011). Robustly measuring vascular reactivity differences with breath-hold: Normalising stimulus-evoked and resting state BOLD fMRI data. NeuroImage, 54 (1), 369–379.
de Riedmatten, I. (2024). Apparent Diffusion Coefficient fMRI shines a new light on white matter resting-state connectivity, as compared to BOLD. bioRxiv 2024.07.03.601842
Szczepankiewicz, F. (2019). Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems. PLOS ONE, 14 (3), e0214238.
Spencer, A. P. C. (2024). Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI. bioRxiv 2024.10.01.615823
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.