Biology subjects

Jbabdi, S.

Publications and source records attributed to Jbabdi, S..

At least 19 recordsLinked to original sources

bioRxiv2026

High-throughput light-sheet 3D microscopy of whole rodent brain microstructure for integration with MRI

Whole-brain cellular mapping is essential for understanding how differences in cellular architecture shape brain organisation and function. Magnetic Resonance Imaging (MRI) provides whole-brain coverage in humans and animal models but is only indirectly sensitive to cellular features. Integrating MRI with whole-brain microscopy is therefore critical for interpreting existing microstructural MRI metrics and developing new, more specific ones. Here, we present a scalable cross-resolution framework that integrates 3D light-sheet microscopy (LSM) of intact rodent brains with diffusion MRI (dMRI) to bridge cellular and macroscopic brain imaging. By combining active electrotransport-based tissue clearing and immunolabelling with accelerated LSM acquisitions, we achieve whole-brain multiplexed mapping of neuronal and glial populations in intact rodent brains on timescales comparable to conventional histology and 2D microscopy. This throughput enables LSM and MRI integration at scale, linking macroscopic MRI contrasts to microscopic cellular features within the same tissue. We apply this framework across eight genetically diverse mouse strains to develop predictive models of 3D microscopy-derived cellular features from multi-shell dMRI-derived contrasts. We demonstrate that data-driven combinations of state-of-the-art dMRI contrasts predict neuronal and astrocytic features better than individual contrasts, across all animals. Our novel approach enables 3D microscopy to guide the development and interpretation of microstructural MRI measures that are ultimately translatable to the living human brain.

neuroscience↗

bioRxiv2026

Brain Connectivity Modelling Through Joint Estimation of Parcels and Gradients

This paper presents a framework for modelling the topography of whole-brain connectivity in resting-state functional MRI. The aim is to disentangle functional segregation, which manifests as abrupt changes in connectivity, from so-called gradients, i.e., smooth variations in connectivity across the brain. Our core assumption is that functional segregation leads to low-rank structure in the dense (point-to-point) connectome, whereas connectivity gradients imply a sparse and non-low-rank structure in the dense connectome. Our method thus decomposes the connectome into low-rank and sparse components, enabling the integration of local-nonlinear and global-linear embedding strategies. We show that this hybrid model approximates the empirical dense connectome more effectively than purely low-rank or purely gradient approaches. We also find that connectivity gradients derived from this model exhibit strong correspondence with task-based topographic maps. We hope that this approach can provide insight into the organisational principles of brain regions where gradients remain poorly characterised.

neuroscience↗

bioRxiv2026

Microscopy-informed structural connectivity mapping in the in vivohuman brain via domain adaptation

Characterising human brain connectivity remains a major challenge in neuroscience. Multimodal datasets combining diffusion MRI with high-resolution microscopy in the same brain offer a unique link between macroscopic imaging and microstructural detail, but we lack tools to leverage these data to improve connectivity estimates for in vivo human imaging. We present a deep learning model that predicts high-resolution microscopy-informed fibre orientations from diffusion MRI. The model uses microscopy-derived three-dimensional fibre orientation maps as biologically grounded training targets. It is trained on a bespoke macaque dataset integrating in vivo MRI, postmortem MRI, and whole-brain microscopy, and then translated to in vivo human imaging. We use domain adaptation to predict fibre orientations from diverse MRI datasets: first to bridge differences in tissue state in the macaque (postmortem to in vivo), and then to generalise across species (macaque to human). Our method derives microscale-informed fibre architecture from diffusion MRI without requiring microscopy at inference. It leverages data that can easily be acquired only in animal models whilst generalising to in vivo human diffusion MRI with minimal acquisition requirements. The microscopy-informed fibre orientation distributions support biologically meaningful tractography, enhancing superficial white matter and cortical-subcortical pathway delineation for in vivo human data. More broadly, this work establishes a general framework for transferring microstructural information from microscopy to non-invasive imaging, enabling biologically informed mapping of brain connectivity.

neuroscience↗

bioRxiv2026

Scaling up polarization-sensitive optical coherence tomography to image the whole macaque brain

Polarization-sensitive optical coherence tomography (PS-OCT) is a label-free imaging technique that exploits birefringence to visualize myelinated axons at micrometer resolution. However, serial PS-OCT imaging has been limited to small volumes, including tissue blocks from larger species, owing to constraints in acquisition speed, system stability, and data processing. These limitations have prevented its application to whole-brain mapping in large mammals. Here we present a scalable PS-OCT acquisition system and computational pipeline for whole-brain imaging in the rhesus macaque. The framework integrates high-throughput serial imaging with automated reconstruction and processing, enabling volumetric imaging at micrometer-scale resolution across decimeter-scale brain volumes. Using this approach, we acquired two complete macaque brains at a voxel size of 5.5 x 5.5 x 3.4 m and an effective resolution of approximately 10 x 10 x 5.5 m, generating multi-terabyte datasets consisting of multiple contrasts including fiber orientation information. The datasets and associated processing tools are made publicly available. This platform establishes a method for large-scale, high-resolution mapping of white matter architecture in primate brains. The resulting datasets provide a reference for validating MRI models and support the development of neurotechnological applications, including deep brain stimulation, where accurate characterization of axonal organization is required.

neuroscience↗

bioRxiv2026

Hierarchical Signatures of Language in the Human Brain

Language relies on a hierarchy of sensory and cognitive processes, yet how different levels of this hierarchy are supported by distinct neural architectures remains unclear. Here we show that semantic processing, compared with phonological processing, is associated with higher-level brain networks, as characterized by resting-state fMRI connectivity, in vivo measures of cortical myelin, and cortical types derived from a cytoarchitecture-defined reference atlas. These relationships were established using individualized ultra-high field fMRI in both English and French speakers leveraging a multi-session, multi-modal 7T MRI protocol including a language localizer. For comparison, we developed an artificial neural network, in which a representational hierarchy spontaneously emerged where phonological information was captured in earlier layers and semantic information in later layers. By integrating individualized functional mapping, neuroanatomical characterization and artificial intelligence, this study advances understanding of the neural basis of language and provides a framework for linking biological and artificial systems of communication. Significance StatementLanguage is widely described as hierarchical, yet how this functional organization is implemented in the brains biological architecture remains unclear. By combining ultra-high field, individualized neuroimaging with in vivo measures of cortical microstructure and large-scale connectivity, this study establishes a framework for linking distinct levels of linguistic computation to the brains structural and functional organization. Integrating these findings with artificial neural network modeling further reveals shared principles between biological and machine systems. Together, this work advances a biologically grounded account of language, bridges cognitive neuroscience and artificial intelligence, and provides a roadmap for understanding how complex cognition emerges from structured brain architecture.

neuroscience↗

bioRxiv2026

Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Alzheimers disease is characterised by the pathological interaction of two proteins, amyloid-beta (A{beta}) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of A{beta}, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of A{beta}, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of A{beta} and tau, network-based tau propagation, A{beta}-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional A{beta}, tau, and neurodegeneration data. Using the dATN model, we show that A{beta}-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of A{beta} and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic-pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting A{beta}, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence SummaryColocalisation of A{beta} and tau predicts regional tau progression and optimal A{beta}-targeted intervention windows, while tau predicts neurodegeneration.

neuroscience↗

bioRxiv2025

10.5 Tesla High-Resolution Macaque Brain MRI for Connectivity Studies

Mapping brain connectivity in primates remains a major challenge due to difficulties in resolving microscopic white matter architecture, while maintaining whole-brain coverage. Increasing imaging spatial resolution is key for disambiguating fibre configurations within smaller anatomical volumes. Here, we present novel developments that allow high-resolution diffusion MRI of the macaque brain using one of the world's highest-field human MRI scanners operating at 10.5 Tesla, allowing both in vivo and ex vivo macaque brain imaging. Our approach achieves very high spatial resolution across both tissue states, (up to 580 m)3 in vivo and (300 m)3 ex vivo, with diffusion weighting up to b = 6000 s/mm2. We detail methodological advances in data acquisition, image reconstruction, processing and whole-brain tractography that overcome critical challenges associated with ultra-high-field imaging. This work establishes a new framework for high-resolution in vivo and ex vivo neuroimaging of the NHP brain at 10.5 T using a human bore scanner, paving the way for subsequent analyses of brain connectivity across species and tissue states at unprecedented detail. The dataset, along with all processing pipelines, containerised workflows, and reusable web services, is openly shared to support reproducibility and future integration with microscopy for studying white matter microstructure and connections at the mesoscale.

neuroscience↗

bioRxiv2025

Diffusion-weighted steady-state free precession imaging in the ex vivo macaque brain on a 10.5T human MRI scanner

Diffusion MRI provides a non-invasive probe of local fibre bundles and long-range anatomical connections to characterise the structural connectome. One way to achieve very high spatial resolution diffusion MRI data for connectivity investigations is to scan ex-vivo brains over many hours or days, ideally at ultra-high field strength to boost signal levels. However, conventional diffusion MRI acquisition techniques do not generally deliver good data quality for the challenging conditions of ex-vivo tissue, characterised by reduced diffusivities and relaxation times when compared to in vivo. In this work, we investigate the potential of the diffusion-weighted steady-state free precession (DW-SSFP) sequence for ex vivo diffusion imaging of the macaque brain using a 10.5 T human MRI scanner with a conventional (Gmax = 70 mT/m) gradient set. SNR-efficiency optimisations incorporating experimental relaxation times demonstrate that the DW-SSFP sequence is predicted to achieve improved or similar SNR efficiency compared to a diffusion-weighted spin- and stimulated-echo sequence. Importantly, DW-SSFP can achieve this with the additional benefit of negligible geometric distortions, unlike conventional diffusion MRI using an echo-planar imaging readout. Using optimised DW-SSFP sequence parameters, we propose a protocol at 0.4 mm isotropic resolution using a two-shell multi-orientation protocol (effective b-values of 3200 s/mm2 and 5600 s/mm2). We fit the data using Tensor, Ball and 3-Sticks and Constrained Spherical Deconvolution signal representations. The results demonstrate high-quality diffusivity estimates across the entire brain with the ability to resolve multiple fibre populations in challenging crossing-fibre regions. The data will be made fully open source and multimodal as part of the Center for Mesoscale Connectomics, providing a resource for future connectivity investigations.

neuroscience↗

bioRxiv2025

An anthropoid/strepsirrhine divergence in ventral visual stream connectivity

The ventral visual stream has undergone extensive reorganisation within the primate lineage. While some work has examined restructuring of the ventral prefrontal cortical grey matter across primates, comparative studies of white matter connectivity are lacking primarily due to difficulties in data acquisition and processing. Here, we present a data-driven approach to the study of white matter connectivity using post-mortem diffusion MRI data. With this approach, we reconstruct anterior temporal-frontal and occipitotemporal-frontal connections across two anthropoids and one strepsirrhine: the rhesus macaque, the black-capped squirrel monkey, and the ring-tailed lemur. We find that the anthropoids exhibit more dorsal prefrontal innervation of these ventral visual connections. This study supports the hypothesis that anthropoid primates underwent extensive reorganisation of both grey and white matter during their emergence as visual foragers in a complex ecological niche. The data-driven techniques presented here enable further research on white matter connectivity in previously understudied species.

neuroscience↗

bioRxiv2025

Cross-species Standardised Cortico-Subcortical Tractography

Despite their importance for brain function, cortico-subcortical white matter tracts are underrepresented in diffusion MRI tractography studies. Their non-invasive mapping is more challenging and less explored compared to other major cortico-cortical bundles. We introduce a set of standardised tractography protocols for delineating tracts between the cortex and various deep subcortical structures, including the caudate, putamen, amygdala, thalamus and hippocampus. To enable comparative studies, our protocols are designed for both human and macaque brains. We demonstrate how tractography reconstructions follow topographical principles obtained from tracers in the macaque and how these translate to humans. We show that the proposed protocols are robust against data quality and preserve aspects of individual variability stemming from family structure in humans. Lastly, we demonstrate the value of these species-matched protocols in mapping homologous grey matter regions in humans and macaques, both in cortex and subcortex.

neuroscience↗

bioRxiv2025

Multi-modal Monte Carlo MRI simulator of tissue microstructure

Monte Carlo simulation of MRI signals is a powerful tool for modelling and quantifying tissue microstructure. While these methods have been used in MRI for decades, they typically focus on one aspect of microstructure and one type of MRI contrast at a time. In this paper, we present a new Monte Carlo MRI (MCMR) simulator for investigating the impact of tissue microstructure on arbitrary MRI sequences. A key feature of the proposed simulator is that substrates can incorporate multiple microstructural features (diffusion, T1/T2 relaxation, membrane permeability, local off-resonance fields, surface relaxation and magnetisation transfer) simultaneously. This provides a single forward model from tissue microstructure to MRI signals that captures a broad range of contrasts typically considered to be of different MRI "modalities". We validate the results of the simulator by reproducing previous findings in well-established sequences, namely diffusion-weighted MRI, magnetisation transfer imaging, and gradient and spin echo sequences. The simulator is fully open source and packaged along with detailed documentation and tutorials.

neuroscience↗

bioRxiv2024

Linking microscopy to diffusion MRI with degenerate biophysical models: an application of the Bayesian EstimatioN of CHange (BENCH) framework

Biophysical modelling of diffusion MRI (dMRI) is used to non-invasively estimate microstructural features of tissue, particularly in the brain. However, meaningful description of tissue requires many unknown parameters, resulting in a model that is often ill-posed. The Bayesian EstimatioN of CHange (BENCH) framework was specifically designed to circumvent parameter fitting for ill-conditioned models when one is simply interested in interpreting signal changes related to some variable of interest. To understand the biological underpinning of some observed change in MR signal between different conditions, BENCH predicts which model parameter, or combination of parameters, best explains the observed change, without having to invert the model. BENCH has been previously used to identify which biophysical parameters could explain group-wise dMRI signal differences (e.g. patients vs. controls); here, we adapt BENCH to interpret dMRI signal changes related to continuous variables. We investigate how parameters from the dMRI standard model of white matter, with an additional sphere compartment to represent glial cell bodies, relate to tissue microstructure quantified from histology. We validate BENCH using synthetic dMRI data from numerical simulations. We then apply it to ex-vivo macaque brain data with dMRI and microscopy metrics of glial density, axonal density, and axonal dispersion in the same brain. We found that (i) increases in myelin density are primarily associated with an increased intra-axonal volume fraction and (ii) changes in the orientation dispersion derived from myelin microscopy are linked to variations in the orientation dispersion index. Finally, we found that the dMRI signal is sensitive to changes in glial cell soma in the WM, but that no parameter in the extended standard model was able to explain this observed signal change, suggesting model inadequacy.

neuroscience↗

bioRxiv2024

Connectivity profile and function of uniquely human cortical areas

Quantitative comparison of the white matter organization of the human neocortex with that of the chimpanzee and macaque shows a wide distribution of areas with a uniquely human connectivity profile, including the frontal-parietal fiber systems and the temporal visual pathway. Functional decoding of these areas shows their involvement in language, abstract reasoning, and social information processing. Overall, these results counter models that assign primacy to prefrontal cortex for human uniqueness.

neuroscience↗

bioRxiv2024

An Image Quality Transfer Technique for Localising Deep Brain Stimulation Targets

The ventral intermediate nucleus of the thalamus (Vim) is a well-established surgical target in functional neurosurgery for the treatment of tremor. As the structure lacks intrinsic contrast on conventional MRI sequences, targeting the Vim has predominantly relied on standardised Vim atlases which can fail to account for individual anatomical variability. To overcome this limitation, recent studies define the Vim using its structural connectivity profile generated via tractography. Although successful in accounting for individual variability, these connectivity-based methods are sensitive to variations in image acquisition and processing, and require high-quality diffusion imaging protocols which are usually not available in clinical settings. Here we propose a novel transfer learning approach to accurately target the Vim particularly on clinical-quality data. The approach transfers anatomical information from publicly-available high-quality datasets to a wide range of white matter connectivity features in low-quality data to augment inference on the Vim. We demonstrate that the approach can robustly and reliably identify Vim even with compromised data quality and is generalisable to datasets acquired with different protocols, outperforming previous surgical targeting methods. The approach is not limited to targeting Vim and can be adapted to other deep brain structures.

neuroscience↗

bioRxiv2024

Imaging the structural connectome with hybrid diffusion MRI-microscopy tractography

Neuroanatomical tract tracing methods are fundamental in providing "gold standard" estimates of brain connectivity. However, tracer methods cannot be performed in humans and even in animals, we can only study projections from typically one or two injection sites per animal sacrificed. Orientation-sensitive microscopy techniques such as PLI provide an alternative where they can visualise detailed fibre orientations at the micron-scale across the whole brain. However, these methods are often most informative on orientations within the 2D imaging plane, with less reliable or missing through-plane information, restricting 3D tract reconstruction. Conversely, dMRI can estimate fibre orientations in 3D but at low resolution, which leads to many false positive and negative estimates of fibre trajectories. To facilitate reconstruction of the microscopy-informed connectome, we develop a data-fusion method that complements 2D microscopy with through-plane information from diffusion MRI to construct 3D hybrid orientations that are both maximally informed by the high-resolution microscopy, have whole-brain coverage and can be input into existing tractography pipelines. Diffusion MRI can be readily acquired prior to microscopy meaning the same method is translatable across species, including in humans. Here we apply our method to an existing open-access macaque dataset and demonstrate (1) whole-brain microscopy-informed tractography (2) the advantages of hybrid tractography in two known tractography challenges, the gyral bias and bottleneck problem (3) how hybrid tractography appears to outperform diffusion-only tractography when compared to tracer data and (4) the generalisability of our hybrid method to different microscopy contrasts, facilitating wider translation.

bioengineering↗

bioRxiv2023

Diffusion MRS tracks distinct trajectories of neuronal development in the cerebellum and thalamus of rat neonates.

It is currently impossible to non-invasively assess cerebellar cell structure during early development. Here we propose a novel approach to non-invasively and longitudinally track cell-specific development using diffusion-weighted magnetic resonance spectroscopy in combination with microstructural modelling. Tracking metabolite diffusion allows us to probe cell-specific developmental trajectories in the cerebellum and thalamus of healthy rat neonates from post-natal day (P) 5 to P30. Additionally, by comparing different analytical and biophysical microstructural models we can follow the differential contribution of cell bodies and neurites during development. The thalamus serves as a control region to assess the sensitivity of our method to microstructural differences between the regions. We found significant differences between cerebellar and thalamic metabolites diffusion properties. For most metabolites, the signal attenuation is stronger in the thalamus, suggesting less restricted diffusion compared to the cerebellum. There is also a trend for lower signal attenuation and lower ADCs with increasing age, suggesting increasing restriction of metabolite diffusion. This is particularly striking for taurine in the thalamus. We use biophysical modelling to interpret these differences. We report a decreased sphere fraction (or an increased neurite fraction) with age for taurine and total creatine in the cerebellum, marking dendritic growth. Surprisingly, we also report a U-shape trend for segment length (the distance between two embranchments in a dendritic tree) in the cerebellum agreeing with age-matching morphometry of openly available 3D-Purkinje reconstructions. Results demonstrate that diffusion-weighted MRS probes early cerebellar neuronal development non-invasively.

neuroscience↗

bioRxiv2023

Personalised Regional Modelling Predicts Tau Progression in the Human Brain

Aggregation of the hyperphosphorylated tau protein is a central driver of Alzheimers disease, and its accumulation exhibits a rich spatio-temporal pattern that unfolds during the course of the disease, sequentially progressing through the brain across axonal connections. It is unclear how this spatio-temporal process is orchestrated - namely, to what extent the spread of pathologic tau is governed by transport between brain regions, local production or both. To address this, we develop a mechanistic model from tau PET data to describe tau dynamics along the Alzheimers disease timeline. Our analysis reveals longitudinal changes in production and transport dynamics on two independent cohorts, with subjects in early stage of the disease exhibiting transport-dominated spread, consistent with an initial spread of pathologic tau seeds, and subjects in late stage disease (Braak stage 3/4 onwards) characterised primarily by local production of tau. Furthermore, we demonstrate that the model can accurately predict subject-specific longitudinal tau accumulation at a regional level, potentially providing a new clinical tool to monitor and classify patient disease progression. TeaserA mechanistic model reveals tau protein dynamics in Alzheimers, showing stage-specific shifts in transport and local production.

neuroscience↗

bioRxiv2023

Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain

The hippocampus is involved in numerous cognitive functions, some of which have uniquely human aspects, such as autobiographical memory. Hippocampal anatomy, however, is typically considered conserved across primates and its evolutionary diversification is rarely studied. Comparing hippocampal structure and function is, therefore, critical for understanding human brain architecture. Here, we developed a novel comparative framework to study the hippocampus across species characterising its geometry, microstructure, and functional network embedding. In humans and macaques, we generated a new comparative space that represents the hippocampus as an unfolded surface, which respects its sheet-like anatomy. We mapped histological and MRI-derived markers of microstructure to the hippocampal surface and integrated it with low-dimensional embedding of resting-state MRI connectivity data. Our results demonstrate that the micro- and macro-structural organisation of the hippocampus are overall conserved in both species, showing consistent anterior-posterior and subfield-to-subfield differentiation. Furthermore, while hippocampal functional organisation also follows anterior-posterior trends in both species, hippocampal functional connectivity markedly reflected evolutionary reconfiguration of transmodal networks, in particular the default-mode network. Specifically, the inferior parietal lobe in the macaque mirrors an incomplete integration of the default mode network in non-human primates. By combining fine-grained anatomical investigation with large-scale functional imaging, we showed that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration, due to their embedding in higher-order association networks. SummaryWhile the hippocampus is key for uniquely human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, showing anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species also followed an anterior-posterior axis, the latter showed a marked evolutionary reconfiguration, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Our findings suggest that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding in heteromodal association networks.

neuroscience↗