Biology subjects

Descoteaux, M.

Publications and source records attributed to Descoteaux, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Tractography from Serial Optical Coherence Tomography: How and Why?

To disentangle complex fiber configurations that remain challenging for diffusion MRI tractography, insights might be gained from microscopy tractography. Indeed, by precisely following small white matter (WM) fascicles, invisible at the resolution of diffusion MRI, microscopy tractography can help explain how fiber populations are organized at the finest scales. Due to its high resolution and its 3D nature, serial optical coherence tomography (S-OCT) offers promise for studying WM at the microscale. However, whether the reflectivity contrast from S-OCT supports tractography at the microscale remains unknown. Furthermore, there is a gap in the literature regarding how an ideal microscopy tractography algorithm should behave with respect to the choice of tractography algorithm, tracking maps definition and microscale orientation distribution functions (ODF) estimation. In this work, we describe a tailored approach to reconstruct WM fascicles at the microscale from S-OCT acquisitions. We validate our approach on a simulated microscopy-like FiberCup dataset, and show that multiscale Frangi filters outperforms structure tensor analysis for estimating ODF. We also show that anatomically-constrained particle filtering tractography enables targetted, region-to-region tractography, and outperforms standard deterministic or probabilistic tracking approaches. We further demonstrate our method on a whole mouse brain S-OCT reconstruction at 10 m by reconstructing thalamocortical WM projections. Overall, our results show that S-OCT tractography recovers fine white matter fascicles that are supported by viral tracing experiments from the Allen Mouse Brain Connectivity Atlas. Moreover, this work shows the first ODF estimation and fully-3D probabilistic particle filtering tractography of the mouse brain from S-OCT reconstructions at 10 m isotropic resolution.

bioinformatics↗

bioRxiv2026

Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc

Diffusion MRI enables noninvasive mapping of human white matter pathways, but automated segmentation methods have largely focused on large association, projection, and commissural bundles. Brainstem and subcortical pathways supporting basal ganglia, cerebellar, limbic/reward, sensory, and homeostatic functions remain underrepresented in large-scale connectomic analyses. To address this gap, we adapted BundleParc, a recently introduced bundle-parcellation architecture, into an automated pipeline for direct segmentation and along-tract parcellation of 97 subcortical and brainstem white matter pathways. The model was trained on a curated reference dataset derived from Human Connectome Project diffusion MRI using anatomy-guided tractography, explicit inclusion and exclusion criteria, automated outlier filtering, and manual quality assurance. Operating directly on native-space fiber orientation distributions, the algorithm successfully recovers these intricate anatomical trajectories and ordered parcellations. We show the model generalizes to diverse external datasets spanning development, aging, and neurodegenerative disease cohorts, maintaining robust performance across variations in spatial resolution and angular sampling. The released container, trained model, population atlas, reference streamlines, and quality assurance outputs provide a resource for studying deep brainstem and subcortical pathways in development, aging, disease, and neuromodulation-relevant anatomy.

neuroscience↗

bioRxiv2026

Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts

Large-scale population analyses of structural connectome organization remain challenging because of cross-subject alignment, pathway interpretability and computational burden. No widely adopted standard exists for systematic evaluation across processing methods. We developed connectome-based spatial statistics (CBSS), a scalable framework for anatomically aligned and functionally informed quantification of white-matter microstructure that yields atlas-defined pathways organized into 13 functional networks. Using data from 56,510 UK Biobank participants together with five independent lifespan cohorts, we evaluated the streamline-, voxel- and network-level measures in the aspects of reliability, heritability, structure-function coupling, cognitive and behavioral prediction, brain aging patterns and lifespan trajectories across cohorts. The systematic evaluation workflow compares population-level white-matter representations across methods, spatial scales, tasks and datasets. The results support CBSS as a common connectome reference for large-scale, cross-cohort diffusion MRI studies.

neuroscience↗

bioRxiv2026

sf-pediatric: A robust and age-adaptable end-to-end pipeline for pediatric diffusion MRI

Diffusion MRI (dMRI) provides a powerful, non-invasive window into white matter (WM) development. Yet, most existing processing pipelines are not well-suited to the rapidly evolving neurophysiology of the pediatric brain. Here, we introduce sf-pediatric, a scalable, end-to-end, age-adaptable dMRI pipeline that integrates normative models of brain diffusivities to enable optimal subject-specific analysis from birth through 18 years old. Leveraging normative trajectories derived from nearly 2,000 participants from six cohorts, sf-pediatric dynamically calibrates diffusion priors, template selection, segmentation, and WM atlases based on the subjects age. By incorporating automatic quality control into a portable, tested, containerized, open-access, and press-button framework across computing environments, sf-pediatric provides a robust pipeline for large-scale pediatric dMRI studies. We validated this approach by showing improved local modeling and cortical fanning while preserving reproducibility and the ability to derive brain-behavior relationships. Additionally, we demonstrated robust recovery of known developmental trajectories of WM microstructure and connectome-derived network organization.

neuroscience↗

bioRxiv2025

Regional brain age deviations reveal divergent developmental pathways in youth

BackgroundNormative modeling of brain development has gained traction for quantifying individual deviations in maturation. The brain age gap (BAG), the difference between predicted age from MRI features and chronological age, offers a potential individualized normative metric of neurodevelopment. However, consistent patterns across psychiatric disorders remain elusive, and no studies have examined whether BAG can predict developmental trajectories within an inclusive continuous model of youths cognition and behavior. MethodsUsing longitudinal data from the Adolescent Brain Cognitive Development Study (ages 9-15, n=9,074), we built 8 region-specific brain age models using volumes, thicknesses, and surface areas of parcels from the Brainnetome adolescent atlas. We derived psychiatric diagnoses from a parental questionnaire. Multivariate linear regression was used to assess case-control differences and cross-sectional continuous cognitive/behavioral profiles. We modeled cognitive/behavioral trajectories using a multivariate joint latent-class mixed model and assessed the relationship with BAG values using multinomial logistic regression. ResultsChildren with ADHD showed delayed maturation across multiple regions (Cohens d: - 0.12 to -0.08), while subcortical BAG emerged as a transdiagnostic indicator of delayed development (d: -0.07, pfdr = 0.024). Accelerated maturation characterized the high cognition and low symptom profile, while the inverse was found for the low cognition profile. Three developmental trajectories were identified: stable, towards externalizing behaviors, or internalizing behaviors. Widespread accelerated maturation predicted evolution towards internalizing behaviors but was protective against the externalizing trajectory. ConclusionsIntegrating BAG with continuous cognitive and behavioral profiles yielded a plausible framework for early identification of atypical trajectories, potentially contributing to personalized medicine in psychiatry.

neuroscience↗

bioRxiv2025

White matter geometry confounds Diffusion Tensor Imaging Along Perivascular Space (DTI-ALPS) measures

IntroductionThe perivascular space (PVS) is integral to glymphatic function, facilitating fluid exchange and waste clearance in the brain. Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS) has been proposed as a non-invasive marker of perivascular diffusion, yet its specificity remains unclear. ALPS measures assume that radial asymmetry in white matter diffusivity predominantly reflects PVS contributions. However, anatomical and microstructural confounds may influence these metrics. MethodsWe systematically evaluated potential biases in ALPS-derived measures using high-resolution, multi-shell diffusion MRI from the Human Connectome Project (HCP) and high-field imaging. Specifically, we examined (1) the prevalence of radial asymmetry across white matter, (2) the influence of crossing fibers on ALPS indices, (3) the impact of axonal undulations and dispersion, and (4) the spatial alignment of vasculature with white matter in ALPS-associated regions. ResultsRadial asymmetry is widespread across white matter and persists even at high b-values, suggesting a dominant contribution from axonal geometry rather than faster PVS-specific diffusion. Crossing fibers significantly inflate ALPS indices, with greater radial asymmetry observed in regions with a greater prevalence of crossing fibers. Furthermore, anisotropic axonal dispersion and undulations introduce systematic asymmetry independent of perivascular diffusion. Finally, high-resolution vascular imaging reveals substantial heterogeneity in medullary vein orientation, challenging the assumption that PVS consistently aligns with the left-right axis in ALPS regions. ConclusionALPS indices are significantly influenced by white matter microstructure, including fiber crossings, undulations, and dispersion. These findings suggest that ALPS-derived metrics may not provide a direct measure of glymphatic function but rather reflect underlying axonal geometry. Interpretations of ALPS-derived metrics as biomarkers of glymphatic function must consider these anatomical complexities, and future studies should integrate advanced modeling approaches to disentangle perivascular contributions from white matter structure.

neuroscience↗

bioRxiv2024

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subjects sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1{+/-}6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44[≤]r[≤]0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21[≥]r[≥]-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20[≥]r[≥]-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39[≤]r[≤]0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22[≥]r[≥]-0.25). Linear mixture of sex and age explained 26{+/-}10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33{+/-}11% when body height was added into the mixture model. Age itself explained only 2{+/-}2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

neuroscience↗

bioRxiv2023

Data-driven characterization and correction of the orientation dependence of magnetization transfer measures using diffusion MRI

PurposeTo characterize the orientation dependence of magnetization transfer (MT) measures in white matter (WM) and propose a first correction method for such measures. MethodsA characterization method was developed using the fiber orientation obtained from diffusion MRI (dMRI) with diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD). This allowed for characterization of the orientation dependence of measures in all of WM, regardless of the number of fiber orientation in a voxel. Furthermore, a first correction method was proposed from the results of characterization, aiming at removing said orientation dependence. Both methods were tested on a 20-subject dataset and effects on tractometry results were also evaluated. ResultsPrevious results for single-fiber voxels were reproduced and a novel characterization was produced in voxels of crossing fibers, which seems to follow trends consistent with single-fiber results. Unwanted effects of the orientation dependence on MT measures were highlighted, for which the correction method was able to produce improved results. ConclusionEncouraging results of corrected MT measures showed the importance of such correction, opening the door for future research on the topic.

neuroscience↗

bioRxiv2023

Mapping caudolenticular gray matter bridges in the human brain striatum through diffusion magnetic resonance imaging and tractography

In primates, the putamen and the caudate nucleus are connected by [~]1mm-thick caudolenticular gray matter bridges (CLGBs) interspersed between the white matter bundles of the internal capsule. Little is understood about the functional or microstructural properties of the CLGBs. In studies proposing high resolution diffusion magnetic resolution imaging (dMRI) techniques, CLGBs have been qualitatively identified as an example of superior imaging quality, however, the microstructural properties of these structures have yet to be examined. In this study, it is demonstrated for the first time that dMRI is sensitive to an organized anisotropic signal oriented in the direction parallel to the CLGBs, suggesting that dMRI could be a useful imaging method for probing the microstructure of the CLGBs. To demonstrate the anisotropic diffusion signal is coherently organized along the extent of the CLGBs and to enable a subsequent CLGB microstructural measurement, a novel tractography algorithm is proposed that utilizes the shape of the human striatum (putamen + caudate nucleus) to reconstruct the CLGBs in 3D. The method is applied to two publicly available and one locally acquired diffusion imaging datasets varying in resolution and imaging quality, thereafter demonstrating that the reconstructed CLGBs directly overlap expected gray matter regions in the human brain. In addition, the method is shown to accurately reconstruct CLGBs repeatedly across multiple test-retest cohorts. The novel tractography CLGB reconstructions are then used to extract a quantitative measurement of microstructure from a local model of the diffusion signal along the CLGBs themselves. This is the first work to comprehensively study the CLGBs in-vivo using dMRI and presents techniques suitable for future human neuroscience studies targeting these structures.

neuroscience↗

bioRxiv2022

Filtering Methods for Asymmetric ODFs: Where and How Asymmetry Occurs in the White Matter

Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. The objectives of this work are two-fold: to disentangle the various filtering methods proposed in the past for estimating asymmetric ODFs, and to study the occurrence of asymmetric patterns in dMRI brain acquisitions. Hence, we describe a new filtering equation for estimating asymmetric ODFs resulting from the unification of these previously proposed filtering methods. Our method is distributed as an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Following its validation on toy datasets, we apply our method to multi-shell multi-tissue fiber ODF reconstructions for 21 subjects from the Human Connectome Project in test-retest acquisitions. Our results show that our method estimates branching, fanning, bending, ending and other complex asymmetric fiber configurations in less than 2 minutes. Also, our novel number of fiber directions (NuFiD) index reveals that the filtering reduces the number of peak directions in the resulting asymmetric representation. Finally, our MNI-aligned template of asymmetries, describing the degree of asymmetry of each voxel, suggests that at least 60% of brain voxels in a dMRI acquisition contain asymmetric fiber configurations.

bioinformatics↗

bioRxiv2022

High-frequency longitudinal white matter diffusion- & myelin-based MRI database: reliability and variability

Assessing the consistency of quantitative MRI measurements is critical for inclusion in longitudinal studies and clinical trials. Intraclass coefficient correlation and coefficient of variation were used to evaluate the different consistency aspects of diffusion- and myelinbased MRI measures. Multi-shell diffusion and inhomogeneous magnetization transfer datasets were collected from twenty healthy adults at a high-frequency of five MRI sessions. The consistency was evaluated across whole bundles and the track-profile along the bundles. The impact of the fiber populations on the consistency was also evaluated using the number of fiber orientations map. For whole and profile bundles, moderate to high reliability of diffusion and myelin measures were observed. We report higher reliability of measures for multiple fiber populations than single. The overall portrait of the most consistent measurements and bundles drawn from a wide range of MRI techniques presented here will be particularly useful for identifying reliable biomarkers capable of detecting, monitoring and predicting white matter changes in clinical applications and has the potential to inform patient-specific treatment strategies. Key pointsO_LIReliability and variability are excellent to good for DWI measurements, and good to moderate for MT measures for whole bundles and along the bundles. C_LIO_LIThe number of fiber populations affects the reliability and variability of the MRI measurements. C_LIO_LIThe reliability and variability of MRI measurements are also bundle dependent. C_LI

neuroscience↗

bioRxiv2022

The length of the thalamo-cortical white matter fibers brings insight into sex differences in sleep spindle frequency

Sleep spindles (SS) are crucial to brain functions like memory and learning. SS characteristics result from the propagation of nerve impulses along white matter (WM) projections underlying an intricate loop between the thalamus and the cortex. SS amplitude and density have been associated with WM diffusion microarchitecture but physiological mechanisms underlying individual and sex-related variations in SS frequency are unknown. Here, we tested a model of traveling signals along the thalamo-cortico-thalamic projections to explain individual differences in spindle frequency. We predicted the presence of a relationship between the length of the thalamo-cortical WM bundles and a specific characteristic of this functional network, SS frequency. Thirty young participants underwent a polysomnographic recording and a 3T MRI including a diffusion sequence. The length of WM fiber bundles between the thalamus and the frontal cortex was derived from probabilistic tractography computed through constrained spherical deconvolution. Longer WM fiber bundles between the thalamus and specific regions of the frontal cortex (rostral middle frontal gyrus and anterior and middle part of the superior frontal gyrus) were associated with slower SS frequency. Moreover, the length of these WM fiber bundles statistically mediated the sex-related differences in SS frequency. By providing a neuroanatomical marker of individual and sex-related differences in SS frequency, this study is the first to highlight the association between the anatomy of a specific brain network and a specific functional characteristic of this network, the frequency of oscillations produced during sleep.

neuroscience↗

bioRxiv2021

TractoInferno: A large-scale, open-source, multi-site database for machine learning dMRI tractography

TractoInferno is the worlds largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 datasets acquired from 3T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.

neuroscience↗

bioRxiv2021

mrHARDIflow : A pipeline tailored for the preprocessing and analysis of Multi-Resolution High Angular diffusion MRI and its application to a variability study of the PRIME-DE database

The validation of advanced methods in diffusion MRI requires finer acquisition resolutions, which is hard to acquire with decent Signal-to-Noise Ratio (SNR) in humans. The use of Non-Human Primates (NHP) and anaesthesia is key to unlock valid microstructural maps, but tools must be adapted and configured finely for them to work well. Here, we propose a novel processing pipeline implemented in Nextflow, designed for robustness and scalability, in a modular fashion to allow for maintainability and a high level of customization and parametrization, tailored for the analysis of diffusion data acquired on multiple spatial resolutions. Modules of processes and workflows were implemented upon cutting edge and state-of-the-art MRI processing technologies and diffusion modelling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD) and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND), a multi-tensor distribution estimator. Using our pipeline, we provide an in-depth study of the variability of diffusion models and measurements computed on 32 subjects from 3 sites of the PRIME-DE, a database containing anatomical (T1, T2), functional (fMRI) and diffusion (DWI) imaging of Non-Human Primate (NHP). Together, they offer images acquired over a range of different spatial resolutions, using single-shell and multi-shell b-value gradient samplings, on multiple scanner vendors, that present artifacts at different level of importance. We also perform a reproducibility study of DTI, CSD and DIAMOND measurements outputed by the pipeline, using the Aix-Marseilles site, to ensure our implementation has minimal impact on their variability. We observe very high reproducibility from a majority of diffusion measurements, only gamma distribution parameters computed on the DIAMOND model display a less reproducible behaviour. This should be taken into consideration when future applications are performed. We also show that even if promising, the PRIME-DE diffusion data exhibits a great level of variability and its usage should be done with care to prevent instilling uncertainty in statistical analyses.

neuroscience↗

bioRxiv2021

An atlas of white matter anatomy, its variability, and reproducibility based on Constrained Spherical Deconvolution of diffusion MRI

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the first segment of the superior longitudinal fasciculus, fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a highly reproducible parcellation-based dissection protocol, as well as being an educational resource for applied neuroimaging and clinical professionals. O_FIG O_LINKSMALLFIG WIDTH=182 HEIGHT=200 SRC="FIGDIR/small/464139v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@18e7c79org.highwire.dtl.DTLVardef@18e2213org.highwire.dtl.DTLVardef@5f124borg.highwire.dtl.DTLVardef@1aa242c_HPS_FORMAT_FIGEXP M_FIG Graphical abstract(Top) shows the FWT pipeline for both CSTs, AF, and motor CC bundles. (Left to right) show the required input structural parcellation maps and a priori atlases for FWT and the resulting virtual dissection include/exclude VOIs. FWT provides two approaches to virtual dissection: (1) is a bundle-specific approach where streamlines are only seeded for the bundle of interest, (2) is a whole brain tractography followed by streamlines segmentation, (top right) shows output tractograms. (Middle) Group-averaged T1 and fODF images are generated from the HCP test-retest data, and FWT is applied to generate the HCP-atlas using the bundle-specific approach (1*). FWTs whole brain tracking and segmentation approach (2*) was applied to the HCP and MASSIVE dataset (right and left) and conducted model-based, and pair-wise similarity analyses and generated voxel-wise cumulative maps per bundle. FWT= Fun With Tracts, FS= FreeSurfer, MSBP= MultiScaleBrainParcellator, PD25= NIST Parkinsons histological, JHU= Johns Hopkins university, Juelich= Juelich university histological atlas, AC/PC= anterior commissure/posterior commissure) UKBB= UK Biobank, SUIT (spatially unbiased cerebellar atlas template), dMRI= diffusion magnetic resonance imaging, CSD= constrained spherical deconvolution, fODF= fiber orientation distribution function, CST= corticospinal tract, AF= arcuate fasciculus, CC= corpus callosum, HCP= human connectome project, MASSIVE= Multiple acquisitions for standardization of structural imaging validation and evaluation. C_FIG

neuroscience↗

bioRxiv2021

Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

neuroscience↗

bioRxiv2021

Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of high angular resolution diffusion imaging (HARDI) data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), opening the door to the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (FA). However, tensor-valued diffusion MRI data is not mathematically compatible with latest versions of CSD and the impacts of such atypical data on fODF reconstruction with CSD are yet to be studied. In this work, we lay down the mathematical and computational foundations of a tensor-valued CSD and use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs. We also compare the combinations with regards to their performance at producing accurate and precise FA with DIVIDE, and present an optimised protocol for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and FA on in vivo data.

neuroscience↗

bioRxiv2021

Structural connectome fingerprinting and age prediction in pediatric development: assessing voxel- and surface-based white matter connectivity

Mapping structural white matter connectivity is a challenge, with many barriers to accurate representation. Here, we assessed the replicability and reliability of two connectome-generating methods, voxel- or surface-based, using test-retest analyses, fingerprinting and age prediction. The two connectomic methods are initiated by the same state-of-the-art dMRI processing pipeline before diverging at the tractography and connectome-generating steps using either voxels or surfaces. While both methods performed very well across all analyses, voxel-based connectomes performed marginally better than surface-based connectomes. Notably, structural connectomes derived from either method demonstrate reliably accurate representations of both individuals and their chronological age, comparable to similar analyses employing multi-modal features. The difference in methodological performance could be attributed to a number of method-specific features but ultimately show that cutting-edge tractography with robust dMRI processing produces reliable white matter connectivity measures.

neuroscience↗