Biology subjects

Cottaar, M.

Publications and source records attributed to Cottaar, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Longitudinal White Matter Microstructure and Transition to Psychosis in Individuals at Clinical High Risk

Altered white matter microstructure, commonly indexed by fractional anisotropy (FA), has been reported in individuals at clinical high-risk of psychosis (CHR-P) across multiple white matter tracts. However, the relationship between these alterations and later transition to psychosis remains unclear. To address this gap, we investigated whether baseline and longitudinally updated FA measurements were associated with risk of transition to psychosis in a large cohort of individuals at CHR-P. We analysed diffusion tensor imaging data from N = 205 participants at CHR-P, 23 of whom developed psychosis within 3 years of follow-up. Data were collected at baseline, 6 months, and 12 months. Associations between global and tract-specific FA and transition risk were examined using Cox proportional hazards models of baseline data and longitudinally updated data. Point estimates were directionally consistent, with hazard ratios <1 for FA across all white matter tracts in both baseline and longitudinal models. Associations reached nominal significance in the hippocampal cingulum, anterior thalamic radiation, and forceps minor, but none of these remained significant after correcting for multiple comparisons (pcorr > .05). The directionally consistent estimates are compatible with small, distributed effects across multiple white matter pathways being associated with transition to psychosis, although this requires replication. Longitudinally updated FA measurements did not yield stronger associations than were available from baseline imaging data alone. Studies at an even larger scale are needed to establish whether these associations are robust and clinically meaningful.

neuroscience↗

bioRxiv2026

Longitudinal White Matter Trajectories in Clinical High Risk and First-Episode Psychosis: Findings from the Multi-Centre PSYSCAN Study

Psychosis has been linked to changes in diffusion-derived fractional anisotropy (FA) across multiple white matter tracts, yet most evidence is cross-sectional. Longitudinal findings are inconsistent, and the clinical relevance of white matter changes over time remains unclear. To address this gap, we investigated longitudinal white matter changes and their clinical correlates in early psychosis in a large, multi-centre diffusion-tensor imaging study. Across 18 sites, 407 participants (healthy controls: n = 97, 59.8% men, mean age 23.9{+/-}4.6 years; clinical high risk of psychosis: n = 158, 53.5% men, mean age 23.0{+/-}4.9 years; first-episode psychosis: n = 152, 71.1% men, mean age 25.1{+/-}5.4 years) were scanned at up to three time points over 12 months. FA was assessed in the cingulum bundle, the superior longitudinal fasciculus, the inferior fronto-occipital fasciculus, and at the whole brain level. FA trajectories were analysed using linear mixed-effects models to test effects of group, social and occupational functioning, and (attenuated) psychotic symptoms, while controlling for demographic and socioeconomic covariates. No significant group differences in FA were observed, either globally or within tracts (p > .05). Longitudinal FA trajectories were not associated with changes in (attenuated) psychotic symptoms or functioning (p > .05). In contrast, higher baseline antipsychotic medication dose was significantly associated with lower FA (pcorr < .05). Overall, these findings indicate that white matter microstructure is relatively stable during the clinical-high-risk and first-episode phases of psychosis, and that it is not clearly associated with variation in symptom severity or functional outcomes over time.

neuroscience↗

bioRxiv2025

Investigating the sensitivity of the diffusion MRI signal to magnetization transfer and permeability via Monte-Carlo simulations

PurposeMagnetization transfer (MT) and water exchange via permeability operate on a similar spatiotemporal scale to water diffusion. In this study, we use a simulation-based approach to characterise how MT and permeability impact (1) diffusion-weighted MRI (dMRI) measurements from cylindrical substrates and (2) parameter estimation using a two-compartment model of white matter. MethodsWe used Monte-Carlo simulations to model the dMRI signal inside and outside axons by simulating signals from parallel cylinders with different diameters and volume densities. We subsequently introduced membrane permeability and MT at the cylinder walls to investigate their impact on the dMRI signal. We fitted a two-compartment model to the simulated signal to produce estimates of the cylinder diameter and density. We evaluated the impact of MT and permeability by comparing the fitted diameter and density to the simulated ground truth. ResultsPermeability leads to underestimation (up to 100%) of cylinder diameter and density. Specifically, by enabling isochromats to escape from restrictions and diffuse more freely, permeability makes the overall displacement profile closer to the extra-axonal displacement profile. MT had limited effects on diameter estimation but caused substantial bias (20-50%) in volume density estimates depending on the ratio of the intra-axonal and extra-axonal volume fraction. This is due to the intra-axonal and extra-axonal space having different surface-to-volume ratios and therefore different surface relaxation rates. ConclusionPermeability and MT can considerably influence the dMRI signal. They increase the relative contribution from larger cylinders to the dMRI signal and bias microstructural parameter estimates derived from dMRI data.

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

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

Universal Dynamic Fitting of Magnetic Resonance Spectroscopy

Dynamic (2D) magnetic resonance spectroscopy is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modelling temporal changes in the parameters. Here we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type. A simple interface allows information to be shared and precisely modelled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting. Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.

neuroscience↗

bioRxiv2023

Hierarchical Modelling of Crossing Fibres in the White Matter

While diffusion MRI is typically used to estimate microstructural properties of tissue in volumetric elements (voxels), more specificity can be obtained by separately modelling the properties of individual fibre populations within a voxel. In the context of cross-subjects modelling, these so-called fixel-based analyses require identifying equivalent fibre populations. This is usually done post-hoc, after estimating fibre orientations for individual subjects independently and subsequently matching the fixels between subjects. This approach can fail due to individual differences in fibre orientation distributions. Here, we introduce a hierarchical framework for fitting crossing fibre models to diffusion MRI data in a population of subjects. This hierarchical setup guarantees that the crossing fibres are consistent by construction and, therefore, comparable across subjects. We propose an expectation-maximisation approach to fit the model, which can scale to large numbers of subjects. This approach produces a crossing-fibre white matter fibre template, which can be used to estimate fibre-specific parameters that are consistent across subjects and, hence, can be used in fixel-based statistical analyses.

neuroscience↗

bioRxiv2022

The BigMac dataset: an open resource combining multi-contrast MRI and microscopy in the macaque brain

Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.

neuroscience↗

bioRxiv2022

Hebbian activity-dependent plasticity in white matter

Synaptic plasticity is required for learning and follows Hebbs Rule, the com-putational principle underpinning associative learning. In recent years, a complementary type of brain plasticity has been identified in myelinated axons, which make up the majority of brains white matter. Like synaptic plasticity, myelin plasticity is required for learning, but it is unclear whether it is Hebbian or whether it follows different rules. Here, we provide evidence that white matter plasticity operates following Hebbs Rule in humans. Across two experiments, we find that co-stimulating cortical areas to induce Hebbian plasticity leads to relative increases in cortical excitability and associated increases in a myelin marker within the stimulated fiber bundle. We conclude that Hebbian plasticity extends beyond synaptic changes, and can be observed in human white matter fibers.

neuroscience↗

bioRxiv2021

Identifying microstructural changes in diffusion MRI; How to break parameter degeneracy

Biophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH). We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change. BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions. Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.

neuroscience↗

bioRxiv2020

Heterogeneous relationships between white matter and behaviour

Several studies have established specific relationships between White Matter (WM) and behaviour. However, these studies have typically focussed on fractional anisotropy (FA), a neuroimaging metric that is sensitive to multiple tissue properties, making it difficult to identify what biological aspects of WM may drive such relationships. Here, we carry out a pre-registered assessment of WM-behaviour relationships in 50 healthy individuals across multiple behavioural and anatomical domains, and complementing FA with myelin-sensitive quantitative MR modalities (MT, R1, R2*). Surprisingly, we only find support for predicted relationships between FA and behaviour in one of three pre-registered tests. For one behavioural domain, where we failed to detect an FA-behaviour correlation, we instead find evidence for a correlation between behaviour and R1. This hints that multimodal approaches are able to identify a wider range of WM-behaviour relationships than focusing on FA alone. To test whether a common biological substrate such as myelin underlies WM-behaviour relationships, we then ran joint multimodal analyses, combining across all MRI parameters considered. No significant multimodal signatures were found and power analyses suggested that sample sizes of 40 to 200 may be required to detect such joint multimodal effects, depending on the task being considered. These results demonstrate that FA-behaviour relationships from the literature can be replicated, but may not be easily generalisable across domains. Instead, multimodal microstructural imaging may be best placed to detect a wider range of WM-behaviour relationships, as different MRI modalities provide distinct biological sensitivities. Our findings highlight a broad heterogeneity in WMs relationship with behaviour, suggesting that variable biological effects may be shaping their interaction. HighlightsO_LIPre-registered testing of microstructural imaging across modalities (FA, MT, R1, R2*) to test WM-behaviour relationships. C_LIO_LIPartial support for FA-behaviour relationships hypothesised based on previous literature. C_LIO_LIMultimodal approaches can help detect WM-behaviour relationships that are not detected with FA alone. C_LIO_LISample sizes of 40 to 200 may be needed to detect myelin-behaviour relationships in joint multimodal analyses. C_LIO_LIVariable biological effects may be shaping WM-behaviour relationships. C_LI

neuroscience↗

bioRxiv2020

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

PurposeMyelin has long been the target of neuroimaging research due to its importance in brain development, plasticity, and disease. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fibre pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibres would provide a more specific marker of myelination. Theory & MethodsOne MRI signal property sensitive to myelin is the phase accumulation, which to date has also been limited to voxel-averaged myelin estimates. We use this sensitivity by measuring the phase accumulation of the signal remaining after diffusion weighting, which we call DIffusion-Prepared Phase Imaging (DIPPI). Including diffusion weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) it increases the relative contribution of intra-axonal water, whose phase is related linearly to the amount of myelin surrounding the axon (in particular the log g-ratio) and (2) it gives directional information, which can be used to distinguish between crossing fibres. ResultsUsing simulations and phantom data we argue that other sources of phase accumulation (i.e., movement-induced phase shift during the diffusion gradients, eddy currents, and other sources of susceptibility) can be either corrected for or are sufficiently small to still allow the g-ratio to be reliably estimated. ConclusionsThis new sequence is capable of providing a g-ratio estimate per fibre population crossing within a voxel.

neuroscience↗

bioRxiv2020

Estimating intra-axonal axial diffusivity in the presence of fibre orientation dispersion

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d[boxV] = 1.7m2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ~ 2 - 2.5m2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

neuroscience↗

bioRxiv2020

Multimodal imaging brain markers in early adolescence are linked with a physically active lifestyle

The World Health Organization (WHO) promotes physical exercise and a healthy lifestyle as means to improve youth development. However, relationships between physical lifestyle and brain development are not fully understood. Here, we asked whether a brain - physical latent mode of covariation underpins the relationship between physical activity, fitness, and physical health measures with multimodal neuroimaging markers. In 50 12-year old school pupils (26 females), we acquired multimodal whole-brain MRI, characterizing brain structure, microstructure, function, myelin content, and blood perfusion. We also acquired physical variables measuring objective fitness levels, 7-days physical activity, body-mass index, heart rate, and blood pressure. Using canonical correlation analysis we unravel a latent mode of brain - physical covariation, independent of demographics, school, or socioeconomic status. We show that MRI metrics with greater involvement in this mode also showed spatially extended patterns across the brain. Specifically, global patterns of greater grey matter perfusion, volume, cortical surface area, greater white matter extra-neurite density, and resting state networks activity, covaried positively with measures reflecting a physically active phenotype (high fit, low sedentary individuals). Showing that a physically active lifestyle is linked with systems-level brain MRI metrics, these results suggest widespread associations relating to several biological processes. These results support the notion of close brain-body relationships and underline the importance of investigating modifiable lifestyle factors not only for physical health but also for brain health early in adolescence. Significance statementAn active lifestyle is key for healthy development. In this work, we answer the following question: How do brain neuroimaging markers relate with young adolescents level of physical activity, fitness, and physical health? Combining advanced whole-brain multimodal MRI metrics with computational approaches, we show a robust relationship between physically active lifestyles and spatially extended, multimodal brain imaging derived phenotypes. Suggesting a wider effect on brain neuroimaging metrics than previously thought, this work underlies the importance of studying physical lifestyle, as well as other brain - body relationships in an effort to foster brain health at this crucial stage in development.

neuroscience↗

bioRxiv2020

Modelling white matter in gyral blades as a continuous vector field

1Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called "gyral biases" limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemispheric connections when compared to tracers.

neuroscience↗

bioRxiv2019

Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP

Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. DW-SSFP datasets were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise across the brain. We compared one and two flip-angle DW-SSFP data using a diffusion tensor model that incorporates the full DW-SSFP Buxton signal model. The two-flip angle data were subsequently fitted using a modified DW-SSFP signal model that incorporates a Gamma distribution of diffusivities. This allowed us to generate tensor maps at a single, SNR-optimal effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments. HighlightsO_LIB1 inhomogeneity at 7T leads to spatially varying SNR & ADC estimates in DW-SSFP C_LIO_LI2-flip angle DW-SSFP data can address B1 effects in a cohort of post-mortem brains C_LIO_LIOur approach reduces degradations in PDD estimates & improves whole brain coverage C_LIO_LIOur approach provides a means to define ADCs at an SNR-optimal effective b-value C_LI

neuroscience↗

bioRxiv2019

Joint modelling of diffusion MRI and microscopy

The combination of diffusion MRI with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate diffusion MRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine diffusion MRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a brain-wide fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel.

neuroscience↗