Biology subjects

Rafael-Patino, J.

Publications and source records attributed to Rafael-Patino, J..

9 recordsLinked to original sources

bioRxiv2026

Validating the Standard Model of diffusion MRI in white matter with Numerical Substrates

The non-invasive estimation of intra- and extracellular microstructural parameters using biophysical models has been a major focus in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter (WM) provides a unifying framework for various modelling approaches, representing axons as impermeable narrow cylinders embedded within a locally anisotropic extra-axonal space. However, the SM relies on simplifying assumptions that may not hold in realistic WM tissue, as they do not take into account axonal undulations, beading, the presence of glial cells, or membrane permeability. In this work, we investigate how SM-derived estimates behave when the model is applied to realistic numerical WM substrates generated by the CATERPillar tool. Specifically, we vary (i) axonal morphological features such as beading and undulations, (ii) axonal packing density, (iii) orientation dispersion, (iv) membrane permeability of axons and astrocytes separately, (v) myelin volume fraction, and (vi) diffusion time. In each part of the analysis, different noise levels are introduced. Overall, according to our results, the relative changes in SM estimates show that the intra-axonal volume fraction f increased with stronger beading, higher packing density, and greater myelin volume, and was strongly influenced by axonal and astrocytic permeability. The orientation dispersion index p2 was affected by undulation, but was substantially biased at low packing densities, with stronger beading and when astrocytes were impermeable. The effective intra-axonal diffusivity Da decreased with stronger beading and undulation and tended to be overestimated in most scenarios. The parallel extra-axonal diffusivity De|| was strongly influenced by axonal permeability, as well as packing density, dispersion, and undulation, and was the most noise-sensitive parameter, showing systematic overestimation at low SNR. Finally, the effective perpendicular extra-axonal diffusivity De{perp} was the most stable parameter relative to the effective ground truth across the tested conditions, while remaining sensitive to packing density, axonal permeability, myelin volume fraction, and undulation. These findings enable users to identify potential biases introduced by varying conditions and to adjust their interpretations accordingly.

neuroscience↗

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

Do you see what I see? Diffusion imaging protocol heterogeneity biases ischemic core volume, location, and clinical associations in acute stroke

PurposeDiffusion-weighted magnetic resonance imaging (DWI) is essential for diagnosing ischemic stroke and identifying targets for emergency revascularization. Apparent diffusion coefficient (ADC) maps derived from DWI are commonly used to locate the infarct core, but they are not strictly quantitative and can vary across platforms and sites due to technical factors. This retrospective study was conducted to examine how differences in ADC map generation, resulting from varied protocols across platforms and sites, affect the determination of infarct core size, location, and related clinical outcomes in acute stroke. MethodsIn this retrospective study, 726 acute anterior circulation stroke patients admitted to the Lausanne University Hospital between May 2018 and January 2021 were selected. DWI data were used to generate ADC maps as they would appear from different protocols: two simulated with low and medium angular resolution (4 and 12 diffusion gradient directions) and one with high angular resolution (20 directions). Using a DEFUSE like criteria and image post-processing, ischemic cores were localized; core volume, location, and associations to National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores were compared between the two imaging sequences. ResultsSignificant differences were observed in the ADC distribution within white matter, particularly in the kurtosis and skewness, with the segmented infarct core volume being higher in protocols with reduced angular resolution compared to the 20-directions data (7.63 ml vs. 3.78 ml). The volumetric differences persisted after correcting for age, sex, and type of intervention. Infarcted voxels locations varied significantly between the two protocols. This variability affected associations between infarct core volume and clinical scores, with lower associations observed for 4-directions data compared to 20-directions data for NIHSS at admission and after 24 hours, and mRS after 3 months, further confirmed by multivariate regression. ConclusionsImaging protocol heterogeneity leads to significant changes in the ADC distribution, ischemic core location, size, and association with clinical scores. Work is needed in standardizing imaging protocols to improve the reliability of ADC as an imaging biomarker in stroke management.protocols to improve the reliability of ADC as an imaging biomarker in stroke management. Key ResultsO_LIOrientation changes in diffusion imaging significantly impact ADC distribution and threshold-based infarct core volume determination, affecting multi-centric studies. C_LIO_LILower number of directions in DWI acquisitions weakens associations between infarct volume measurements and clinical scores. C_LIO_LIFindings emphasize the impact of DWI acquisition protocol heterogeneity and image processing on acute stroke workups. C_LI

neuroscience↗

bioRxiv2023

Molecular imaging with aquaporin-based reporter genes: quantitative considerations from Monte Carlo diffusion simulations

Aquaporins provide a new class of genetic tools for imaging molecular activity in deep tissues by increasing the rate of cellular water diffusion, which generates magnetic resonance contrast. However, distinguishing aquaporin contrast from the tissue background is challenging because water diffusion is also influenced by structural factors such as cell size and packing density. Here, we developed and experimentally validated a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on time-dependent changes in diffusivity can improve specificity by unambiguously isolating aquaporin-driven contrast from the tissue background. Finally, we used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin, and established a simple mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. This study creates a framework for broad applications of aquaporins, particularly in biomedicine and in vivo synthetic biology, where quantitative methods to measure the location and performance of genetic devices in whole vertebrates are necessary.

bioengineering↗

bioRxiv2023

Susceptibility-induced internal gradients reveal axon morphology and cause anisotropic effects in the dMRI signal

Diffusion-weighted MRI is our most promising method for estimating microscopic tissue morphology in vivo. The signal acquisition is based on scanner-generated external magnetic gradients. However, it will also be affected by susceptibility-induced internal magnetic gradients caused by interaction between the tissue and the static magnetic field of the scanner. With 3D in silico experiments, we show how internal gradients cause morphology-, compartment-, and orientation-dependence of spin-echo and pulsed-gradient spin-echo experiments in myelinated axons. These effects are unseen in previous 2D modelling. For an ex vivo monkey brain, we observe the orientation-dependency generated only when including non-circular cross-sections in the in silico morphological configurations, and find orientation-dependent deviation of up to 17% for diffusion tensor metrics. Our findings underline the importance of accounting for realistic 3D axon morphology in modelling. Interestingly, the morphology-specific orientation-dependency trends show potential for a novel sensitivity to morphology, which is not attainable by the theoretical diffusion-weighted MRI signal itself.

neuroscience↗

bioRxiv2021

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

neuroscience↗

bioRxiv2020

Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

neuroscience↗

bioRxiv2020

Axon morphology is modulated by the local environment and impacts the non-invasive investigation of its structure-function relationship

Axonal conduction velocity, which ensures efficient function of the brain network, is related to axon diameter. Non-invasive, in vivo axon diameter estimates can be made with diffusion magnetic resonance imaging, but the technique requires 3D validation. Here, high resolution, 3D synchrotron X-ray Nano-Holotomography images of white matter samples from the corpus callosum of a monkey brain reveal that blood vessels, cells and vacuoles affect axonal diameter and trajectory. Within single axons, we find that the variance in diameter and conduction velocity correlates with the mean diameter, contesting the value of precise diameter determination in larger axons. These complex 3D axon morphologies drive previously reported 2D trends in axon diameter and g-ratio. Furthermore, we find that these morphologies bias the estimates of axon diameter with diffusion magnetic resonance imaging and, ultimately, impact the investigation and formulation of the axon structure-function relationship.

neuroscience↗

bioRxiv2018

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractographys inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

neuroscience↗