De Riedmatten, I. | Search bioRxiv

bioRxiv2025

CATERPillar: A Flexible Framework for Generating White Matter Numerical Substrates with incorporated Glial Cells

Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is the use of numerical phantoms that accurately represent the target tissue, which is in this case, cerebral white matter (WM). This study introduces CATERPillar (Computational Axonal Threading Engine for Realistic Proliferation), a novel method that simulates the mechanic of axonal growth using overlapping spheres as elementary units. CATERPillar facilitates parallel axon development while preventing collisions, offering user control over key structural parameters such as cellular density, undulation, beading and myelination. Its uniqueness lies in its ability to generate not only realistic axonal structures but also realistic glial cells, enhancing the biological fidelity of simulations. We showed that our grown substrates feature distributions of key morphological parameters that agree with those from histological studies. The structural realism of the astrocytic components was quantitatively validated using Sholl analysis. Furthermore, the time-dependent diffusion in the extra- and intra-axonal compartments accurately reflected expected characteristics of short-range disorder, as predicted by theoretical models. CATERPillar is open source and can be used to (a) develop new acquisition schemes that sensitise the MRI signal to unique tissue microstructure features, (b) test the accuracy of a broad range of analytical models, and (c) build a set of substrates to train machine learning models on. HighlightsO_LICATERPillar generates realistic synthetic voxels of white matter containing packed axons and glial cells. C_LIO_LISynthetic axons had similar morphologies to those segmented from human electron microscopy in previous works. C_LIO_LIThe morphological features of synthetic astrocytes closely matched those observed in histology. C_LIO_LIThe functional form of diffusion and kurtosis time-dependence in the intraand extra-axonal spaces agreed with experimentally observed disorder power laws in voxels composed of synthetic axons. C_LI

neuroscience↗

bioRxiv2024

Mapping activity and functional organisation of the motor and visual pathways using ADC-fMRI in the human brain

In contrast to blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the Apparent Diffusion Coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC-fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function. To demonstrate the specificity and value of ADC-fMRI, both ADC-and BOLD-fMRI data were collected at 3T in human subjects during visual stimulation and motor tasks. The first aim of this study was to identify an acquisition design for ADC that minimises BOLD contributions. By examining the timings in responses, we report that ADC 0/1 timeseries (acquired with b-values of 0 and 1 ms/{micro}m2) exhibit residual vascular contamination while ADC 0.2/1 timeseries (with b-values of 0.2 and 1 ms/{micro}m2) show minimal BOLD influence and higher sensitivity to neuromorphological coupling. Second, a General Linear Model was employed to identify activation clusters for ADC 0.2/1 and BOLD, from which average ADC and BOLD responses were calculated. The negative ADC response exhibited a significantly reduced delay relative to the task onset and offset as compared to BOLD. This early onset further supports the notion that ADC is sensitive to neuromorphological rather than neurovascular coupling. Remarkably, in the group-level analysis, positive BOLD activation clusters were detected in the visual and motor cortices, while the negative ADC clusters mainly highlighted pathways in white matter connected to the motor cortex. In the averaged individual level analysis, negative ADC activation clusters were also present in the visual cortex. This finding confirmed the reliability of negative ADC as an indicator of brain function, even in regions with lower vascularisation such as white matter. Finally, we established that ADC-fMRI timecourses yield the expected functional organisation of the visual system, including both gray and white matter regions of interest. Functional connectivity matrices were used to perform hierarchical clustering of brain regions, where ADC-fMRI successfully reproduced the expected structure of the dorsal and ventral visual pathways. This organisation was not replicated with the b=0.2 ms/{micro}m2 diffusion-weighted time courses, which can be seen as a proxy for BOLD (via T2-weighting). These findings underscore the robustness of ADC time courses in functional MRI studies, offering complementary insights to BOLD-fMRI regarding brain function and connectivity patterns. KeypointsO_LIThe functional time course of the Apparent Diffusion Coefficient (ADC), specifically measured with alternating b-values of 0.2 and 1 ms/{micro}m2 at 3T, appears to be minimally affected by BOLD contamination. C_LIO_LIIn the activity maps, the location of negative ADC clusters suggests neural activity in WM tracts that are connected to the motor cortex, which is not detected with positive BOLD. C_LIO_LIFunctional Connectivity analysis utilising ADC is better able to detect the organisation of the dorsal and ventral visual streams than diffusion- and T2-weighted time courses. C_LI

neuroscience↗