Biology subjects

Palombo, M.

Publications and source records attributed to Palombo, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Human brain cortical oxygen consumption is positively related to soma surface area density.

Evidence from in-silico, ex vivo and invasive animal studies suggested the function of neural tissue to be inextricably linked to its structural and microstructural organization. However, this link has never been assessed non-invasively in humans. Here, we established a simple quantitative framework linking cerebral oxygen metabolism to gray matter microstructural features in healthy humans using advanced non-invasive MRI methods. The cerebral metabolic rate of oxygen consumption (CMRO2) was quantified with calibrated fMRI, whereas microstructural properties were estimated from diffusion MRI data fitted to the Soma and Neurite Density Imaging (SANDI) model, yielding soma radius (R soma) and soma volume fraction (fsoma) metrics, from which we derived maps of soma surface area density (SAD) and soma numerical density (ND) under the assumption of a spherical soma. CMRO2 was positively associated with SAD (r = 0.29, p < 0.01), fsoma (r = 0.28, p < 0.05) and ND (r = 0.25, p < 0.05), and negatively associated with Rsoma (r = -0.23, p < 0.05) across cortical brain regions. These relationships are consistent with predictions from experimental and theoretical studies, providing the first quantitative evidence that cerebral metabolic demand is coupled to microstructure in humans. The positive association of energy consumption with SAD is consistent with the notion that both the number of synapses (energy-demanding structures), and the energy required to maintain a membrane potential, scale with soma surface area. Our findings establish a non-invasive MRI framework for investigating the microstructural determinants of brain energy metabolism in health and potentially in disease.

neuroscience↗

bioRxiv2026

Brain Structure Shapes Function through higher-order Functional Interactions

Brain function is deeply embedded within multiscale structural architecture. Conventional studies predominantly utilize pairwise connectivity networks to investigate structure-function relationships. However, this low-dimensional perspective overlooks multi-region collaborations for complex cognition. Consequently, whether and how anatomy constrains such higher-order functional networks remains unresolved. To address this pivotal question, we utilize an information-theoretic O-information approach to characterize higher-order functional interactions (HOIs). By reconstructing individual-level HOIs from multimodal structural networks, we directly validate the structural constraint on HOIs. The resulting reconstruction coefficients are defined as structural-functional constraint strength (SFCS), serving as a quantitative vehicle to decipher how anatomy shapes these higher-order networks. SFCS uncovers a highly heterogeneous structural constraint landscape across data modalities, spatial regions, and informational interaction modes. Crucially, individualized SFCS robustly predicts multi-domain cognitive phenotypes, showing higher sensitivity for informant-reported than patient-reported assessments. Finally, we show that this landscape undergoes pathological, mode-specific reorganization in Alzheimers disease. Cross-scale alignment with spatial transcriptomics further demonstrates that this macroscale network remodeling is coupled with microscale metabolic and regulatory gene pathways. Collectively, our findings not only validate the structural constraint on higher-order functional networks but also decipher its precise underlying mechanisms. This constraint paradigm plays a pivotal role in shaping diverse cognitive capabilities, while its pathological disruption in Alzheimers disease highlights the potential of SFCS as a biomarker for tracking neurodegenerative network impairments.

neuroscience↗

bioRxiv2026

From Brain Microstructure to Dynamics: Linking Grey and White Matter Architecture to Propagation Delays

Understanding the relationship between neural dynamics and underlying brain structure, and in particular the impact of the latter on conduction delays, remains a core question in neuroscience. In humans, we primarily access these phenomena at the macroscale through neurophysiological measures of propagation. At this scale, signal propagation is reflected in measurable delays arising from both white matter (WM) axonal conduction and grey matter (GM) synaptic and integrative processes. While specific WM features have been explicitly linked to single-axon conduction delays, no clear models link GM microstructural properties to large-scale propagation. To this aim, we combined advanced MRI microstructural modelling with resting-state MEG to link brain structure to signal propagation in a sample of 94 healthy controls. WM metrics included axonal diameter, myelination (g-ratio, myelin water fraction), and tract length, while GM was characterised using the SANDI model and cortical thickness. Propagation delays were quantified using the neuronal avalanches framework. Our findings indicated significant associations between all of WM and GM metrics and the propagation delays. When attempting to use our W/GM metrics we were able to predict up to ~26% of the observed delay using linear regression modelling. We also found associations between frequency-specific propagation dynamics and tract length. Finally, using a canonical correlation analysis we demonstrated multivariate coupling between our W/GM metrics and the MEG frequency-specific propagation delays. Our findings provide evidence for the link between tissue structure and large-scale neural dynamics, supporting the development of biologically grounded models of signal propagation.

neuroscience↗

bioRxiv2025

Cross-modal quality transfer: enhancing MEG spatial resolution using BOLD-fMRI and Explainable machine learning.

Purpose:1Blood-oxygen-level-dependent functional MRI (BOLD-fMRI) and magnetoencephalog-raphy (MEG) offer complementary insights into brain function, with BOLD-fMRI providing high spatial resolution and MEG offering high temporal sensitivity. This study aimed to enhance MEGs spatial resolution through learning inter-modal relationships via data-driven fusion with BOLD-fMRI using explainable machine learning (xML). MethodsMEG and BOLD-fMRI data were collected for sixteen participants watching the same naturalistic visual stimulus. MEG signals were filtered into standard frequency bands and processed to match the haemodynamic response. Data were then decomposed using Tenso-rial Independent Component Analysis (TICA), yielding 250 MEG (25 per frequency band) and 30 BOLD-fMRI components. A set of Extreme Gradient Boosted tree (XGBoost) models were trained to predict MEG component activity from downsampled BOLD-fMRI components. The models with best-performing configuration of hyperparameters at this resolution were then used to generate voxelwise upsampled MEG maps using the natively higher-resolution BOLD-fMRI data. Performance was compared to trilinear interpolation using R{superscript 2}, mean squared error (MSE), and structural similarity index (SSI). Model inter-pretability was enhanced by generating Shapley values (SHAP), describing relationships between input data and model output. ResultsModels trained on 6mm3 data achieved high predictive performance on previously unseen data (R{superscript 2} = 0.80, MSE = .07). Using higher-resolution 2mm3 BOLD-fMRI data, MEG activ-ity was upsampled to 2mm3, resulting in more detailed image, while maintaining reasonable congruence to naive interpolation (R{superscript 2} = .81, MSE = .06). ConclusionsThis work demonstrates that explainable machine learning enables spatial super-resolution of MEG maps via BOLD-fMRI-informed quality transfer, enabling enhancement of spatial resolution, and offering improved localization, as well as means of generating data-driven insights into neurovascular coupling.

neuroscience↗

bioRxiv2025

Sulcal landmarks reveal lineage-specific trajectories and multi-scale specialization of the mammalian cortex

An important topic of evolutionary neuroscience is to understand how brain function and structure across evolutionary time. In the absence of functional data for ancestral species, studies often rely on structural features such as cortical volume or surface area. However, atlas-based metrics derived from human or a few model organisms lack biological interpretability across species with divergent cortical architectures. Here, we investigate sulcal pits--the locally deepest points of cortical folds--as conserved, atlas-independent landmarks that provide a robust basis for cross-species comparison. Using cortical surface reconstructions from 90 mammalian species, we show that sulcal pit distribution patterns partly recapitulate phylogenetic relationships, independent of overall brain size (volume and surface area), and vary systematically with ecology and lifestyle. By tracing pit-based evolutionary trajectories from 80 million years ago to the present, we found pronounced differences across ecological and behavioral categories, indicating that cortical folding has diversified in close association with species habitats, lifestyles, and social structures. Using sulcal pits, we identified Homo sapiens-specific regions and species-shared regions. The Homo-specific areas were functionally associated with higher cognitive and emotional processes, distinguished by unique histological features, enriched for gene sets related to neural regulation, and exhibited markedly different cell-type abundance profiles compared to shared regions. Together, these results establish sulcal pits as a robust, evolutionarily informative feature for cross-species cortical alignment, offering new insights into the structural innovations underlying brain evolution study.

evolutionary biology↗

bioRxiv2025

Resolving Cellular Morphology in the Human Brain with Multiparametric Diffusion MR Spectroscopy

Diffusion-weighted magnetic resonance spectroscopy (dMRS) noninvasively probes the diffusion of mostly intracellular metabolites and can therefore report on brain microstructure with a cell-type specificity that water-based diffusion MRI cannot achieve. However, the morphological information accessible to conventional dMRS is limited: estimating both cell-body (soma) and neurite dimensions from a single diffusion-encoding scheme is an ill-posed problem, and neither ultra-high b-value nor diffusion-time-dependent measurements alone distinguish soma size from neurite radius. Here we introduce multiparametric dMRS in the human brain, combining diffusion-time- dependent encoding (apparent diffusion coefficient, ADC, and diffusion kurtosis, K, sampled over diffusion times of 6 ms to 250 ms and b-values up to 8 ms {micro}m-2) with double-diffusion-encoded spectroscopy (DDES). Using Monte-Carlo analysis, we show that fitting a two-compartment (soma + neurite) tissue model to diffusion-time data alone is degenerate, admitting two near-indistinguishable solutions. Adding the orthogonal angular information from DDES breaks this degeneracy: jointly fitting both experiments converges to a single, biophysically plausible solution irrespective of initialization, yielding cell-type- specific estimates of intrinsic diffusivity, soma radius, neurite radius, and neurite signal fraction for neuronal (NAA, glutamate) and glial (choline, myo-inositol) metabolites. Metabolite microscopic anisotropy approaches unity, consistent with predominantly intra-neurite diffusion, while simultaneously acquired water data reveal short-range structural disorder and intercompartmental exchange ({approx}15 ms). Multiparametric dMRS thus extends the standard model of metabolite diffusion by a soma compartment and offers a route toward in vivo, cell-type- specific morphometry of neurons and glia in humans--a foundation for biomarkers in conditions where soma and neurite morphology are altered.

neuroscience↗

bioRxiv2025

Distinct cellular processes drive motor skill learning in the human brain

Despite decades of research, the biological mechanisms by which motor skills consolidate in the human brain remain poorly understood. Diffusion MRI provides a unique opportunity to probe biological processes non-invasively, as water displacements occur on the micrometer scale. Using diffusion tensor imaging (DTI), our team showed that motor sequence learning (MSL) induces microstructural changes in the hippocampus and key motor regions, suggesting that declarative and procedural systems may operate as part of the same network. Yet DTI cannot identify the cellular source of these changes, leaving open whether they reflect structural plasticity --remodeling of dendritic and astrocytic processes described in rodents-- or transient homeostatic responses that accompany learning --neuronal and astrocytic swelling. Here, we combined ultra-high-gradient diffusion MRI with the compartment-based Soma and Neurite Density Imaging (SANDI) model to disentangle the cellular basis of motor skill memory consolidation. DTI showed that MSL induced rapid microstructural changes in the hippocampus, precuneus, and motor regions, but only those in the precuneus and posterior parietal cortex (PPC) persisted overnight. SANDI revealed that DTI changes were driven by two distinct cellular processes: a transient enlargement of the cell soma across all regions consistent with a short-lived homeostatic response, and a sustained rise in cell-process density restricted to the precuneus and PPC, compatible with structural plasticity. By decomposing diffusion signals into their cellular sources, our work disambiguates transient and enduring processes, providing the first non-invasive evidence for the cellular basis of human motor memory consolidation and a framework for studying neuroplasticity in vivo.

neuroscience↗

bioRxiv2025

VASAL: A Vascular Substrate Algorithm to Generate Microvascular Network Phantoms

To date, few methods have been proposed for synthesising realistic microvascular networks, particularly with compatibility for diffusion MRI (dMRI) simulations. This work presents a generative algorithm for building computational brain microvascular network phantoms, together with a Monte Carlo framework for simulating spin flow dynamics and corresponding dMRI signals. Phantom morphology can be user-tuned according to the: number of generations (branches); total number of segments; segment diameter and length; angle between connecting segments, and; maximum extent in space. The geometric fidelity of resulting VASAL phantoms was validated against an exemplar rat hippocampal network. Comparable distributions of segment lengths, diameters and orientations, and branching characteristics were obtained, demonstrating VASAL replicated realistic microvascular structures. Implementation of spin flow dynamics was validated by simulating dMRI signals in the ballistic regime, which has a well-defined analytical solution; agreement was excellent between simulated signals and analytical solutions (average root mean square error of 0.005). Following this validation of network geometry and spin flow dynamics, VASAL phantoms with different morphologies were generated to evaluate key IVIM model assumptions. Phantom geometry substantially affected dMRI signals and caused deviations from the IVIM model: for example, complex branching phantoms displayed non-Gaussian signal attenuation and diffusion time dependence. While the IVIM model was exemplified here, the presented VASAL algorithm holds potential for advancing other dMRI acquisitions and models: by enabling simulation of complex blood flow within physiologically accurate vascular networks, a route to disentangling how vascular structure and function influence dMRI signals is initiated.

biophysics↗

bioRxiv2025

Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI

The central nervous system (CNS), comprised of both the brain and spinal cord, and is a complex network of white and gray matter responsible for sensory, motor, and cognitive functions. Advanced diffusion MRI (dMRI) techniques offer a promising mechanism to non-invasively characterize CNS architecture, however, most studies focus on the brain or spinal cord in isolation. Here, we implemented a clinically feasible dMRI protocol on a 3T scanner to simultaneously characterize neurite and soma microstructure of both the brain and spinal cord. The protocol enabled the use of Diffusion Tensor Imaging (DTI), Standard Model Imaging (SMI), and Soma and Neurite Density Imaging (SANDI), representing the first time SMI and SANDI have been evaluated in the cord, and in the cord and brain simultaneously. Our results demonstrate high image quality even at high diffusion weightings, reproducibility of SMI and SANDI derived metrics similar to those of DTI with few exceptions, and biologically feasible contrasts between and within white and gray matter regions. Reproducibility and contrasts were decreased in the cord compared to that of the brain, revealing challenges due to partial volume effects and image preprocessing. This study establishes a harmonized approach for brain and cord microstructural imaging, and the opportunity to study CNS pathologies and biomarkers of structural integrity across the neuroaxis.

neuroscience↗

bioRxiv2025

LONGITUDINAL CHANGES IN WHITE MATTERMICROSTRUCTURAL STATUS FOLLOWING QUANTIFIEDHEAD-BALL IMPACTS IN SOCCER: A PRELIMINARY,PROSPECTIVE STUDY.

Repetitive, sub-concussive head impacts have been associated with increased chronic traumatic encephalopathy (CTE) incidence. CTE diagnosis traditionally relies on post-mortem examination, which limits precise correlation between cause- and-effect. This prospective study embraced innovative diffusion magnetic resonance imaging, which enables in vivo quantification of acute, sub-acute and chronic changes in brain tissue microstructure. This approach was used to evaluate changes in white matter microstructural status at intervals up to 180 days following a specified soccer heading protocol. This study was approved by the university ethics panel. Twelve males (21 - 23 years) were recruited to the study and gave signed, informed consent. Six Intervention participants were university-level soccer players, with 6 Control participants drawn from university-level non-contact sports. Multi-shell diffusion-weighted MRI data were acquired on a 3T Siemens Connectom (300mT/m) scanner using the HARDI protocols. Baseline measures of fractional anisotropy, mean diffusivity and mean kurtosis were acquired at day 0. The Intervention cohort then performed 10 soccer headers in a laboratory, with acceleration-time data captured using an instrumented mouthguard and post-processed to report common metrics. The Intervention group was then re-scanned at day 1 (n = 6), day 90 (n = 5) and day 180 (n = 4). The Control group was re-scanned at day 1 (n = 6) and day 180 (n = 3). Many brain tracts were identified as having significant (p < 0.05) changes in white matter microstructural changes at day 90, which correlated strongly with the magnitude of head impact. A smaller number of tracts had changes at day 1 and day 180. These results indicate that, within this pilot population, the magnitude of repeated soccer headers appears to correlate with the magnitude of white matter microstructural change. Additional investigation is required to determine whether the effect of such an intervention influences long-term brain health risk.

bioengineering↗

bioRxiv2024

The developing hippocampus: Microstructural evolution through childhood and adolescence

The hippocampus is a structure in the medial temporal lobe which serves multiple cognitive functions. While important, the development of the hippocampus in the formative period of childhood and adolescence has not been extensively investigated, with most contemporary research focusing on macrostructural measures of volume. Thus, there has been little research on the development of the micron-scale structures (i.e., microstructure) of the hippocampus, which engender its cognitive functions. The current study examined age-related changes of hippocampal microstructure using diffusion MRI data acquired with an ultra-strong gradient (300 mT/m) MRI scanner in a sample of children and adolescents (N=88; 8-19 years). Surface-based hippocampal modelling was combined with established microstructural approaches, such as Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion Density Imaging (NODDI), and a more advanced gray matter diffusion model Soma And Neurite Density Imaging (SANDI). No significant changes in macrostructural measures (volume, gyrification, and thickness) were found between 8-19 years, while significant changes in microstructure measures related to neurites (from NODDI and SANDI), soma (from SANDI), and mean diffusivity (from DTI) were found. In particular, there was a significant increase across age in neurite MR signal fraction and a significant decrease in extracellular MR signal fraction and mean diffusivity across the hippocampal subfields and long-axis. A significant negative correlation between age and MR apparent soma radius was found in the subiculum and CA1 throughout the anterior and body of the hippocampus. Further surface-based analyses uncovered variability in age-related microstructural changes between the subfields and long-axis, which may reflect ostensible developmental differences along these two axes. Finally, correlation of hippocampal surfaces representing age-related changes of microstructure with maps derived from histology allowed for postulation of the potential underlying microstructure that diffusion changes across age may be capturing. Overall, distinct neurite and soma developmental profiles in the human hippocampus during late childhood and adolescence are reported for the first time.

neuroscience↗

bioRxiv2024

MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression

Neuroanatomical changes to the cortex during adolescence have been well documented using MRI, revealing ongoing cortical thinning and volume loss with age. However, the underlying cellular mechanisms remain elusive with conventional neuroimaging. Recent advances in MRI hardware and new biophysical models of tissue informed by diffusion MRI data hold promise for identifying the cellular changes driving these morphological observations. This study used ultra-strong gradient MRI to obtain high-resolution, in vivo estimates of cortical neurite and soma microstructure in sample of typically developing children and adolescents. Cortical neurite signal fraction, attributed to neuronal and glial processes, increased with age (mean R2fneurite=.53, p<3.3e-11, 11.91% increase over age), while apparent soma radius decreased (mean R2Rsoma=.48, p<4.4e-10, 1% decrease over age) across domain-specific networks. To complement these findings, developmental patterns of cortical gene expression in two independent post-mortem databases were analysed. This revealed increased expression of genes expressed in oligodendrocytes, and excitatory neurons, alongside a relative decrease in expression of genes expressed in astrocyte, microglia and endothelial cell-types. Age-related genes were significantly enriched in cortical oligodendrocytes, oligodendrocyte progenitors and Layer 5-6 neurons (pFDR<.001) and prominently expressed in adolescence and young adulthood. The spatial and temporal alignment of oligodendrocyte cell-type gene expression with neurite and soma microstructural changes suggest that ongoing cortical myelination processes contribute to adolescent cortical development. These findings highlight the role of intra-cortical myelination in cortical maturation during adolescence and into adulthood.

neuroscience↗

bioRxiv2024

Age-trajectories of higher-order diffusion properties of major brain metabolites in cerebral and cerebellar grey matter using in vivo diffusion-weighted MR spectroscopy at 3T

Healthy brain aging involves changes in both brain structure and function, including alterations in cellular composition and microstructure across brain regions. Unlike diffusion-weighted MRI (dMRI), diffusion-weighted MR spectroscopy (dMRS) can assess cell-type specific microstructural changes, providing indirect information on both cell composition and microstructure through the quantification and interpretation of metabolites diffusion properties. This work investigates age-related changes in the higher-order diffusion properties of three major intracellular metabolites (N-Acetyl-aspartate, Creatine and Choline) beyond the classical apparent diffusion coefficient in cerebral and cerebellar grey matter of healthy human brain. Twenty-five subjects were recruited and scanned using a diffusion-weighted semi-LASER sequence in two brain regions-of-interest (ROI) at 3T: posterior-cingulate (PCC) and cerebellar cortices. Metabolites diffusion was characterized by quantifying metrics from both Gaussian and non-Gaussian signal representations and biophysical models. All studied metabolites exhibited lower apparent diffusivities and higher apparent kurtosis values in the cerebellum compared to the PCC, likely stemming from the higher microstructural complexity of cellular composition in the cerebellum. Multivariate regression analysis (accounting for ROI tissue composition as a covariate) showed slight decrease (or no change) of all metabolites diffusivities and slight increase of all metabolites kurtosis with age, none of which statistically significant (p>0.05). The proposed age-trajectories provide benchmarks for identifying anomalies in the diffusion properties of major brain metabolites which could be related to pathological mechanisms altering both the brain microstructure and cellular composition.

biophysics↗

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↗

bioRxiv2022

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (DellAcqua, 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for mapping pore sizes, in particular large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion times and gradient settings. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time optimises the TDR diffusion sequences in simulation for a range of different tissues and scanner constraints. We extend the original work (which considers substrates containing cylinders) by additionally considering the TDR signal obtained from spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration and short diffusion time with an acquisition with long gradient duration and long diffusion time improves the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for mapping pore sizes and restricted diffusion in general. HighlightsO_LITemporal Diffusion Ratio (TDR) 2-seq approach maps areas with restricted diffusion C_LIO_LIOptimised gradient waveform pair is: long {delta} + low G and short {delta} + high G C_LIO_LIIf data is noisy calculating TDR using HARDI acquisition subsets increases accuracy C_LIO_LIWe demonstrate TDR for the first time pre-clinically in rat spinal cord C_LIO_LIPre-clinical TDR values are strongly correlated with axon diameter C_LI

bioengineering↗

bioRxiv2022

Dissecting brain grey and white matter microstructure: a novel clinical diffusion MRI protocol

Soma and Neurite Density Image (SANDI) is an advanced diffusion magnetic resonance imaging (dMRI) signal model devised to probe in vivo microstructural information from both grey and white matter. However, this model requires multi-shell acquisitions that include b-values that are at least 6 times higher than those used in clinical practice. Here we present a 10-minute acquisition protocol that enables to acquire the necessary images for SANDI modelling on a clinical 3T scanner. We demonstrate the feasibility and assess the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI and other state-of-the-art models on five healthy subjects and we present its potential clinical impact on five subjects affected by multiple sclerosis with relapsing-remitting course. Our results suggest that SANDI is a repeatable, reproducible, feasible, and practical method to characterize both white and grey matter tissues in both the healthy brain and in neurological diseases.

neuroscience↗

bioRxiv2021

Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain

Diffusion MRI (dMRI) provides unique insights into the neural tissue milieu by probing interaction of diffusing molecules and tissue microstructure. Most dMRI techniques focus on white matter tissues (WM) due to the relatively simpler modelling of diffusion in the more organized tracts; however, interest is growing in gray matter characterisations. The Soma and Neurite Density MRI (SANDI) methodology harnesses a model incorporating water diffusion in spherical objects (assumed to be associated with cell bodies) and in impermeable "sticks" (representing neurites), which potentially enables the characterisation of cellular and neurite densities. Recognising the importance of rodents in animal models of development, aging, plasticity, and disease, we here sought to develop SANDI for preclinical imaging and provide a validation of the methodology by comparing its metrics with the Allen mouse brain atlas. SANDI was implemented on a 9.4T scanner equipped with a cryogenic coil, and experiments were carried out on N=6 mice. Pixelwise, ROI-based, and atlas comparisons were performed, and results were also compared to more standard Diffusion Kurtosis MRI (DKI) metrics. We further investigated effects of different pre-processing pipelines, specifically the comparison of magnitude and real-valued data, as well as different acceleration factors. Our findings reveal excellent reproducibility of the SANDI parameters, including the sphere and stick fraction as well as sphere size. More strikingly, we find a very good rank correlation between SANDI-driven soma fraction and Allen brain atlas contrast (which represents the cellular density in the mouse brain). Although some DKI parameters (FA, MD) correlated with some SANDI parameters in some ROIs, they did not correlate nearly as well as SANDI parameters with the Allen atlas, suggesting a much more specific nature of the SANDI parameters. We conclude that SANDI is a viable preclinical MRI technique that can greatly contribute to research on brain tissue microstructure.

neuroscience↗

bioRxiv2021

Identification of the key role of white matter alteration in the pathogenesis of Huntington's Disease

Pathogenesis of the inherited neurodegenerative disorder Huntingtons Disease (HD) is complex and progressive, with a long presymptomatic phase in which subtle changes occur in the brain of gene carriers up to 15 years before the onset of symptoms. Thus, there is a need of early, functional biomarker to better understand disease progression and to evaluate treatment efficacy far from onset. In particular, recent studies have shown that white matter may be affected early in HD. In this study, we scanned longitudinally Ki140CAG mice with structural MRI, Diffusion Tensor Imaging (DTI), Chemical Exchange Saturation Transfer of glutamate (gluCEST) and Magnetization Transfer (MT) imaging, in order to assess white matter integrity over the life of this very progressive mouse model. Our results show early defects of diffusion properties in the anterior part of the corpus callosum, preceding gluCEST defects in the same region (-10.8% at 8 months, -19% at 12 months) that spread to adjacent regions. At 12 months, frontal (-7.3%) and piriform (-16.7%) cortices showed reduced gluCEST, as well as the pallidum (-21.0%). MT imaging showed reduced signal in the septum (-21.7%) at 12 months. Cortical and striatal atrophy then appear at 18 months. Vulnerability of the striatum and motor cortex, combined with alterations of anterior corpus callosum, seems to point out the pivotal role of white matter, in the pathogenesis of HD and the pertinence of gluCEST and DTI as biomarkers in HD. Highlights- A knock-in mouse model of Huntingtons disease is longitudinally characterized - A multimodal MRI protocol is performed to identify biomarkers of the disease - The white matter plays a pivotal role in the pathogenesis of the disease - The cortico-striatal pathway seems particularly vulnerable in Huntingtons disease

neuroscience↗