Investigating microstructural determinants of propagation delays: a multimodal MEG-MRI study
Poster No:
1216
Submission Type:
Abstract Submission
Authors:
Svetla Manolova1, Carolyn McNabb1, Eirini Messaritaki1, Marco Palombo1, Krish Singh1, Derek Jones1, Mara Cercignani1, Matteo Mancini2
Institutions:
1Cardiff University, Cardiff, United Kingdom, 2Centro Ricerche Enrico Fermi, Rome, Italy
First Author:
Co-Author(s):
Introduction:
Timing in region-to-region propagation is fundamental for brain function itself, and its characterisation is key for better brain models and to understand changes in pathologies. Segregated neuronal populations in grey matter (GM) interact through white matter (WM), and resulting delays are constrained by both WM and GM properties. Tract length, axonal radius and myelination directly influence microscopic conduction, but their role in macroscopic propagation is less clear (Mancini et al. 2021). To shed more light on the determinants of these end-to-end propagation phenomena, we combined MEG with MRI, respectively estimating propagation delays (PD) with neuronal avalanches (Sorrentino et al. 2022), and computing WM and GM properties.
Methods:
60 healthy participants (mean age[SD]:20.97[1.65];F/M=36/24) had MRI (Siemens Connectom 3T) and MEG (275-channel CTF radial gradiometer) (McNabb et al. 2024). MRI included an anatomical (T1w) scan, 2 diffusion-weighted imaging (DWI) protocols (multi-shell DWI and AxCaliber) (Assaf et al. 2008), and multi-component relaxometry (MCDESPOT) (Deoni et al. 2013). MEG data (sampling:1.2kHz) were acquired at rest for 10 minutes with eyes open and fixated on a cross.
T1w data were processed with FreeSurfer for atlas parcellation and cortical thickness (CT) extraction. DWI underwent denoising, artifact corrections and registration. Tractograms obtained with multi-tissue spherical deconvolution and anatomically constrained tractography were filtered using COMMIT (stick-zeppelin-ball model) (Daducci et al. 2014). We also fitted two microstructural models: NODDI - for neurite density (NDI) and isotropic fraction (ISO) - and SANDI - for myelinated neurite (FN), soma fraction (FS) and soma radius (RS) (Palombo et al., 2020). AxCaliber data were fitted with COMMIT-AxSize, to estimate bundle-specific axon radiuses (Barakovic et al. 2021).
MCDESPOT data were processed using QUIT to estimate myelin water fraction (MWF). We then combined MWF, ISO and NDI to compute the g-ratio (i.e. ratio between inner and outer axon diameters) (Stikov et al. 2015).
Weighted connectivity matrices (structural, streamline length, g-ratio, radius) were obtained and thresholded in two steps: hard threshold (>4 streamlines/connection); group-consensus mask (60% prevalence). Combining radius, g-ratio and length matrices according to the Rushton model (Rushton, 1951), we estimated a conduction delay-weighted (CD) matrix. For each pair of regions and each GM metric (CT, FS, FN, RS), we also summed their values to quantify their end-to-end contribution.
MEG data were filtered (1-150Hz), epoched (2s) and downsampled (512Hz) before source-localisation and computing mean timeseries for each DK region. After that, neuronal avalanches were estimated as the timeseries' z-score, thresholded by 3 standard deviations and binned according to the branching parameter. Avalanches were defined as windows of continuous over-threshold activity. For each of them, PD were computed counting the number of timesteps within the avalanche, and averaged to build PD connectivity matrices. Comparisons were made on group-averaged matrices.
T1w data were processed with FreeSurfer for atlas parcellation and cortical thickness (CT) extraction. DWI underwent denoising, artifact corrections and registration. Tractograms obtained with multi-tissue spherical deconvolution and anatomically constrained tractography were filtered using COMMIT (stick-zeppelin-ball model) (Daducci et al. 2014). We also fitted two microstructural models: NODDI - for neurite density (NDI) and isotropic fraction (ISO) - and SANDI - for myelinated neurite (FN), soma fraction (FS) and soma radius (RS) (Palombo et al., 2020). AxCaliber data were fitted with COMMIT-AxSize, to estimate bundle-specific axon radiuses (Barakovic et al. 2021).
MCDESPOT data were processed using QUIT to estimate myelin water fraction (MWF). We then combined MWF, ISO and NDI to compute the g-ratio (i.e. ratio between inner and outer axon diameters) (Stikov et al. 2015).
Weighted connectivity matrices (structural, streamline length, g-ratio, radius) were obtained and thresholded in two steps: hard threshold (>4 streamlines/connection); group-consensus mask (60% prevalence). Combining radius, g-ratio and length matrices according to the Rushton model (Rushton, 1951), we estimated a conduction delay-weighted (CD) matrix. For each pair of regions and each GM metric (CT, FS, FN, RS), we also summed their values to quantify their end-to-end contribution.
MEG data were filtered (1-150Hz), epoched (2s) and downsampled (512Hz) before source-localisation and computing mean timeseries for each DK region. After that, neuronal avalanches were estimated as the timeseries' z-score, thresholded by 3 standard deviations and binned according to the branching parameter. Avalanches were defined as windows of continuous over-threshold activity. For each of them, PD were computed counting the number of timesteps within the avalanche, and averaged to build PD connectivity matrices. Comparisons were made on group-averaged matrices.
·Figure 1
Results:
Fig.2 shows the relationships between the PD and the WM-GM metrics. For WM, longer tracts require longer PD. PD also increases with CD, but as CD is estimated dividing length by conduction velocity, this trend is likely due to length as well. PD shows little variation with axonal radius and with g-ratio - suggesting a different role compared to simple conduction and likely affected by the bias towards larger axons.
For GM, PD increases with CT and FS, and decreases with FN, so thicker layers (higher CT) could increase PD, while myelinated neurites (higher FN) could shorten them. FS-PD trend requires further study but, together with flat RS, suggests that more somas would increase PD.
For GM, PD increases with CT and FS, and decreases with FN, so thicker layers (higher CT) could increase PD, while myelinated neurites (higher FN) could shorten them. FS-PD trend requires further study but, together with flat RS, suggests that more somas would increase PD.
·Figure 2
Conclusions:
Our results show that GM-WM microstructure provides additional insights into propagation delays and can potentially aid their prediction.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
EEG/MEG Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
Computational Neuroscience
Cortex
MEG
Myelin
STRUCTURAL MRI
Structures
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Barakovic, M., Girard, G., Schiavi, S., et al. (2021). Bundle-specific axon diameter index as a new contrast to differentiate white matter tracts. Frontiers in neuroscience, 15, 646034
3. Daducci, A., Dal Palù, A., Lemkaddem, A., et al. (2014). COMMIT: convex optimization modeling for microstructure informed tractography. IEEE transactions on medical imaging, 34(1), 246-257.
4. Deoni, S. C., Matthews, L., Kolind, S. H. (2013). One component? Two components? Three? The effect of including a nonexchanging “free” water component in multicomponent driven equilibrium single pulse observation of T1 and T2. Magnetic resonance in medicine, 70(1), 147-154.
5. Mancini, M., Tian, Q., Fan, Q., et al. (2021). Dissecting whole-brain conduction delays through MRI microstructural measures. Brain Structure and Function, 226(8), 2651-2663.
6. McNabb, C. B., Driver, I. D., Hyde, V., et al. (2024). The Welsh Advanced Neuroimaging Database: an opensource state-of-the-art resource for brain research. In Proceedings of the ISMRM & ISMRT Annual Meeting & Exhibition, Singapore.
7. Palombo, M., Ianus, A., Guerreri, et al. (2020). SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage, 215, 116835.
8. Rushton, W. A. H. (1951). A theory of the effects of fibre size in medullated nerve. The Journal of physiology, 115(1), 101.
9. Sorrentino, P., Petkoski, S., Sparaco, M., et al. (2022). Whole-brain propagation delays in multiple sclerosis, a combined tractography-magnetoencephalography study. Journal of Neuroscience, 42(47), 8807-8816.
10. Stikov, N., Campbell, J. S., Stroh, T., et al. (2015). In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage, 118, 397-405.