Microstructure and Axon Radius Mapping using dMRI at 7T: Relationship with Conduction Speed
Poster No:
1278
Submission Type:
Abstract Submission
Authors:
Saina Asadi1,2, Arthur Spencer1,2, Yasser Alemán-Gómez1,2, Hélène Lajous1,2, Erick J. Canales-Rodriguez1,3,4, Emeline Mullier1,5, Louise de Wouters5, Isotta Rigoni5, Maciej Jedynak6, Michael Chan7, Alexandre Cionca7, Dimitri Van De Ville7,8, Serge Vulliémoz4,5, Olivier David6,9, Patric Hagmann1,2, Ileana Jelescu1,2
Institutions:
1Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland, 2Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland, 3Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Center for Biomedical Imaging (CIBM), Switzerland, 5EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland, 6Institute de Neurosciences des Systèmes, Aix Marseille University, Marseille, France, 7Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland, 8Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland, 9Fondation Lenval, Department of Pediatric Neurosurgery, Nice, France
First Author:
Saina Asadi
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Co-Author(s):
Arthur Spencer
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Yasser Alemán-Gómez
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Hélène Lajous
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Erick J. Canales-Rodriguez
Department of Radiology, Lausanne University Hospital (CHUV)|Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL)|Center for Biomedical Imaging (CIBM)
Lausanne, Switzerland|Lausanne, Switzerland|Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL)|Center for Biomedical Imaging (CIBM)
Lausanne, Switzerland|Lausanne, Switzerland|Switzerland
Emeline Mullier
Department of Radiology, Lausanne University Hospital (CHUV)|EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Lausanne, Switzerland|Geneva, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Lausanne, Switzerland|Geneva, Switzerland
Louise de Wouters
EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Isotta Rigoni
EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Chun Hei Michael Chan
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)
Geneva, Switzerland
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)
Geneva, Switzerland
Alexandre Cionca
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)
Geneva, Switzerland
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)
Geneva, Switzerland
Dimitri Van De Ville
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)|Department of Radiology and Medical Informatics, University of Geneva
Geneva, Switzerland|Geneva, Switzerland
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL)|Department of Radiology and Medical Informatics, University of Geneva
Geneva, Switzerland|Geneva, Switzerland
Serge Vulliémoz
Center for Biomedical Imaging (CIBM)|EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Switzerland|Geneva, Switzerland
Center for Biomedical Imaging (CIBM)|EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva
Switzerland|Geneva, Switzerland
Olivier David
Institute de Neurosciences des Systèmes, Aix Marseille University|Fondation Lenval, Department of Pediatric Neurosurgery
Marseille, France|Nice, France
Institute de Neurosciences des Systèmes, Aix Marseille University|Fondation Lenval, Department of Pediatric Neurosurgery
Marseille, France|Nice, France
Patric Hagmann
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Ileana Jelescu
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)|Faculty of Biology and Medicine, University of Lausanne
Lausanne, Switzerland|Lausanne, Switzerland
Introduction:
The conduction speed of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can inform on conduction delays in limited regions during presurgical evaluations (Lemaréchal, 2022), there is currently no efficient, non-invasive method to estimate whole-brain conduction speed in vivo. Evidence links conduction speed to axonal microstructure properties such as radius and myelination (Drakesmith, 2019). Biophysical models of the diffusion MRI (dMRI) signal in white matter (WM) enable sub-voxel analysis of microstructure properties associated with conduction speed. Therefore, mapping a reliable metric from dMRI linked to conduction speed could fill the gap in estimating conduction delays across the brain.
Methods:
Healthy participants (n=12) were scanned on a clinical 7T Siemens Terra.X MRI system. A multi-shell dMRI protocol was acquired for Standard Model Imaging (SMI) microstructure estimation in WM (Coelho, 2022; Novikov, 2018). A multi-TE dMRI protocol was acquired for axon radius estimation using an intra-axonal T2 surface-based relaxation model (Barakovic, 2023; Canales-Rodríguez, 2024). dMRI pre-processing included NORDIC complex denoising, Gibbs unringing, susceptibility and eddy current distortion correction. The WM microstructure maps estimated using the SMI framework (Fig 1A) were registered to standard MNI space, where average parametric maps were computed across subjects. Axon radii were estimated voxel-wise from the intra-axonal T2 fit (extra-axonal water is assumed suppressed at b=5000) to the directionally-averaged dMRI signal as a function of TE, using priors on cytoplasmic T2 and surface relaxivity calibrated with histology (Barakovic, 2023). The model fit was reliable in 10 subjects, for which the radius maps were estimated in template space, averaged across subjects and registered to standard MNI space. Finally, group-average microstructure properties (SMI + axon radius) were estimated for all WM bundles of the scale 1 MultiConn atlas with 95 nodes (Alemán-Gómez, 2022). In each bundle, we computed the Pearson correlations of WM microstructure parameters with bundle length, as well as with conduction speed, computed as length over delay. Conduction delays were provided by the F-TRACT database (https://f-tract.eu, Trebaul, 2018) measured by intracranial EEG (50 ms cut-off; Fig 1C) in a subset of bundles.
Results:
SMI: Conduction speed was positively correlated only with axonal water fraction [f ; r=0.30, p<.001] and fibre alignment [p2 ; r=0.25, p<.001]. However, these correlations were indirectly associated with fibre length rather than the conduction delay between region pairs [f ; r=0.62, p<.001] [p2 ; r=0.25, p<.001] (Fig 2a). The strong correlation between f and fibre length suggests that longer fibres may exhibit higher axon density to support efficient long-range connectivity, though this may also reflect tractography reconstruction biases.
Axon radius: Estimated axon radii were consistent with histology, with the corticospinal tract exhibiting the highest axon radius (Fig 1B; Huang, 2020). Consistent with previous findings (Drakesmith, 2019; Ritchie, 1982), our data showed a positive correlation between conduction speed and axon radius [r=0.32, p<.001], which was not driven by the fibre length.
Axon radius: Estimated axon radii were consistent with histology, with the corticospinal tract exhibiting the highest axon radius (Fig 1B; Huang, 2020). Consistent with previous findings (Drakesmith, 2019; Ritchie, 1982), our data showed a positive correlation between conduction speed and axon radius [r=0.32, p<.001], which was not driven by the fibre length.
Conclusions:
Our findings reveal a significant correlation between conduction speed and MRI-derived axon radius independent from the fibre length, emphasising the direct role of axon caliber on conduction delay. In contrast, a similar correlation of speed with axon water fraction and fibre alignment, seems mediated by fiber length and suggests indirect contributions to conduction speed. In the future, finer parcellation scales will be used to increase sensitivity to axon radius variations across the brain, as well as an improved sampling of conduction delays.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Acquisition
Electroencephaolography (EEG)
HIGH FIELD MR
MRI
STRUCTURAL MRI
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Microstructure Imaging, Connectomics
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Barakovic, M. (2023). Estimating axon radius using diffusion-relaxation MRI: Calibrating a surface-based relaxation model with histology. Frontiers in Neuroscience, 17.
Canales-Rodríguez, E. J. (2024). Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI. Magnetic Resonance in Medicine, 91(6), 2579–2596.
Coelho, S. (2022). Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems. NeuroImage, 257, 119290.
Drakesmith, M. (2019). Estimating axon conduction velocity in vivo from microstructural MRI. NeuroImage, 203, 116186.
Huang, S. Y. (2020). High-gradient diffusion MRI reveals distinct estimates of axon diameter index within different white matter tracts in the in vivo human brain. Brain Structure & Function, 225(4), 1277–1291.
Lemaréchal, J.-D. (2022). A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials. Brain, 145(5), 1653.
Novikov, D. S. (2018). Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. NeuroImage, 174, 518–538.
Ritchie, J. M. (1982). On the relation between fibre diameter and conduction velocity in myelinated nerve fibres. Proceedings of the Royal Society of London. Series B, Biological Sciences, 217(1206), 29–35.
Trebaul, L. (2018). Probabilistic functional tractography of the human cortex revisited. NeuroImage, 181, 414–429.