Biology subjects

Jelescu, I. O.

Publications and source records attributed to Jelescu, I. O..

At least 19 recordsLinked to original sources

bioRxiv2026

OCTOPUS: A versatile open-source tool creating realistic numericalbrain cells

Brain cell morphology plays a crucial role in function and pathology. Biophysical models of diffusion MRI (dMRI) quantify cell morphology in vivo, enabling the design of novel biomarkers. These models represent cells by simplified geometries, such as spheres and randomly oriented cylinders, for which analytical signal expressions exist. However, dMRI signals are sensitive to morphological features, such as branching, tapering, undulation, beading, and protrusions, unaccounted for in most models, as they often render analytical solutions impossible. Simulations of the dMRI signal in synthetically generated cells offer a powerful tool to explore how microstructural morphology impacts the dMRI signal. Nevertheless, no tool to generate digital replicas of brain cells is available openly. To address this gap, we introduce the OCTOPUS toolbox, which generates cells featuring all geometrical features described above. OCTOPUS, provided via the Python interface OCTOpool, enables accessible, efficient creation of cells with complex geometries. We recreated histologically reconstructed neuronal and glial cells, including pyramidal, GABAergic and glutamatergic neurons, and astro- and microglia. To illustrate the plausibility of OCTOPUS-generated cells, we reproduced established properties of dMRI signals from brain tissue, such as the signatures of short-range disorder, branching and protrusions, and a high-b-value power law. By comparing the geometries and dMRI signals of generated and original cells, we found different growth strategies adequate for more isotropic and more anisotropic cells. We anticipate that realistic cell substrates created by OCTOPUS will help validate biophysical models, design dMRI sequences sensitive to fine-grained cell morphology beyond analytical models, and generate realistic numerical substrates of brain tissue.

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bioRxiv2026

A Comprehensive Analysis Comparing Isotropic ADC to BOLD-fMRI: Sensitivity to Resting State Networks and Grey to White Matter Functional Connectivity

While Blood Oxygenation Level-Dependent (BOLD) fMRI remains the gold standard for mapping functional brain networks with MRI, its vascular origins inherently conflate haemodynamic effects with neural activity, limiting its sensitivity in white matter (WM) or its interpretation in neurovascular diseases. Apparent Diffusion Coefficient (ADC) fMRI offers an alternative, diffusion-based contrast that is theoretically more sensitive to neuromorphological coupling and therefore more specific to neuronal activation, though investigated primarily during task-based conditions. This study aimed to comprehensively evaluate the efficacy of isotropic ADC-fMRI in detecting established resting-state networks (RSNs) and to extend this methodology to the investigation of grey-to-white matter (GM-WM) functional connectivity. Our analyses revealed a gradient of ADC detectability shaped by the degree of static functional cohesion and structural tethering of each network. The visual and somatomotor networks, being both highly segregated and strongly anchored to underlying structural pathways, yielded the most robust detection. The default mode network (DMN) and dorsal attention network (DAN) reached group-level significance but with lower effect sizes, and their detection proved fragile across analytical approaches. The frontoparietal network (FPN) and salience network (SAN), whose functional identity is defined by dynamic cross-network reconfiguration, did not reach significance. This gradient partially mirrors the established hierarchy of network segregation observed in BOLD, while further suggesting that ADC sensitivity depends on the structural grounding of each network. Furthermore, ADC demonstrated superior sensitivity to GM-WM functional coupling compared to BOLD. GM-WM functional connectivity profiles derived from ADC were significantly more aligned with underlying structural WM architecture across subjects. Taken together, these findings position isotropic ADC-fMRI as a viable complementary modality to BOLD, offering a more direct window into the neural and structural foundations of brain connectivity.

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bioRxiv2026

Characterising the diffusion functional signature of negative BOLD with interleaved TMS-fMRI in the human brain

The coupling between brain excitatory activity and positive blood oxygen level-dependent (BOLD) responses is well-established. Although often associated with inhibition, negative BOLD remains partially understood. Moving away from neurovascular coupling, apparent diffusion coefficient (ADC)-fMRI provides a more direct measure of excitatory activity, possibly mediated by transient cellular deformations. While decreases in ADC align with positive BOLD, the possible translation of negative BOLD into positive ADC has not been investigated in humans. Diffusion-weighted fMRI (dfMRI) combines vascular and microstructural contributions. Using interleaved subthreshold transcranial magnetic stimulation (TMS)-fMRI on the primary motor cortex (M1), we induced negative BOLD responses in contralateral M1 and primary somatosensory cortex (S1). This was accompanied by a negative dfMRI response, but no ADC-fMRI response, indicating minimal microstructural fluctuations. In ipsilateral M1/S1, no BOLD response was detected while dfMRI revealed a positive cluster, suggesting sensitivity to subtle neural activity. These findings provide new insights into vascular and neuronal responses underlying subthreshold TMS and negative BOLD.

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bioRxiv2026

Directed Brain Connectomics Revealed by Bicommunity Structure

The brain is a complex, interconnected system in which structural wiring underpins the flow of information between functional units. After two decades of advances in connectomics, the asymmetry of edge structures in large-scale brain networks remains largely unexplored. Here, we build a directed connectome combining structure and function, and dissect its organization into bicommunities where sending and associated receiving sets of nodes, through clustering of directed-edge information, are fully acknowledged. Our findings reveal a primary directional axis from sensory to association cortices, indicative of bottom-up information flow. Validation with invasive electrophysiological measures demonstrates that these bicommunities capture an intermediate organizational scale at which connection asymmetry aligns across modalities. Finally, sets of distinct known anatomical fiber bundles and their functional mapping are recovered as specific bicommunities. This work identifies asymmetric pathways onto which neural computation can be expressed, and establishes directed connectomics as a novel framework for understanding brain organization.

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bioRxiv2026

Validating Neurite EXchange Imaging (NEXI) using diffusion Monte Carlo simulations in realistic numerical gray matter substrates

NEXI is a gray matter (GM) microstructural model designed to probe brain tissue microstructure in vivo using diffusion MRI. NEXI describes GM as two exchanging Gaussian compartments - neurites, modeled as randomly oriented, infinitely long sticks, and the extracellular space - allowing the estimation of biophysically interpretable parameters related to neurite microstructure and intercompartmental exchange. While modeling cell processes as sticks and each compartment as Gaussian are common assumptions for brain biophysical models of diffusion, neurite structural irregularities and the presence of somas, particularly in GM, may violate them and bias NEXI parameter estimates. Furthermore, the barrier-limited exchange assumed in the Karger model that underlies NEXI may also be violated in realistic conditions. Therefore, in this work, we evaluate NEXIs accuracy in numerical substrates that incorporate realistic GM features and membrane permeability. To this end, we generated several GM-like substrates with neurite beading, undulation, orientation dispersion, and somas across a range of membrane permeabilities. Diffusion signals were generated with Monte Carlo simulations of water diffusion and subsequently fitted with NEXI. Overall, NEXI accurately recovered exchange times across permeability levels and successfully disentangled exchange effects from other microstructural features, showing only minor bias in estimates from the realistic geometries. These results support its potential for in vivo GM microstructure mapping and studies of brain disorders.

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bioRxiv2026

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

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

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bioRxiv2025

Microstructural correlates of white and gray matter in the healthy human brain: comparative analysis of diffusion biophysical models, inhomogeneous magnetization transfer, and macromolecular proton fraction

Abstract summaryDiffusion MRI (dMRI) and magnetization transfer (MT) rely on distinct biophysical principles and provide complementary insights into tissue microstructure. In this study, we investigated associations between microstructural metrics derived from the Standard Model (SMI) in white matter (WM) and the Standard Model with EXchange (SMEX/NEXI) in gray matter (GM) with two MT measures differing in specificity and sensitivity to myelination: the macromolecular proton fraction (MPF) and the inhomogeneous magnetization transfer ratio (ihMTR). Measurements were performed in WM and GM in ten healthy subjects scanned at 3T. In WM, the strongest significant association was observed between ihMTR and the axonal water fraction, consistent with higher myelination in regions of elevated axonal density and limited extra-axonal space. This correlation exceeded that of MPF, supporting the greater specificity of ihMTR to myelin. Interestingly, ihMTR displayed a gradient along the longitudinal axis of the corpus callosum, in agreement with previous histology measurements. Correlation between ihMTR and the extra-axonal perpendicular diffusivity (De{perp}), a putative myelination biomarker, was not significant, whereas MPF and De{perp} exhibited a moderate significant positive correlation. Since a negative correlation is expected if reflecting myelination, these results suggest that in healthy tissue De{perp} is mainly influenced by microstructural factors like fiber coherence and packing, the latter most likely affecting MPF but not ihMTR. In GM, ihMTR correlated significantly only with the exchange time (t), confirming tex as a proxy for cell membrane permeability modulated by myelin. MPF correlated exclusively with the cell-process fraction (f), suggesting the latter is modulated by total (neuronal and glial) cell membrane density. Overall, findings underscore the complementary and concurring microstructural information captured by these metrics, highlighting their potential to disentangle distinct tissue mechanisms in both healthy and pathological conditions. Future studies incorporating ground-truth histology should validate the precise sensitivity of each metric to various microstructural tissue features.

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bioRxiv2025

Ultra-High Field 31P functional Magnetic Resonance Spectroscopy Reveals NAD+ Dynamics in Brain Energy Metabolism during Visual Stimulation.

We investigated dynamic changes in nicotinamide adenine dinucleotide (NAD{square}) metabolism in the human occipital lobe using ultra-high field 31P functional magnetic resonance spectroscopy (fMRS) at 7 Tesla. Twenty-five healthy volunteers (mean age 24 {+/-} 4 years, 10 female) performed a visual task alternating between fixation and flashing checkerboard stimuli. 31P MRS spectra were acquired from a visual cortex voxel functionally localized by prior fMRI. Linear mixed-effects modelling revealed a significant reduction in NAD{square} concentrations during the first stimulation block, while no significant change was observed during the second block. No significant changes were observed for other high-energy phosphate metabolites (ATP, phosphocreatine, and inorganic phosphate), indicating specificity in the NAD{square} response. Exploratory analyses, dividing the blocks in two halves, suggested further reductions in NAD{square} and tNAD in the second halves of both stimulation blocks, though these trends were not statistically significant. Our findings demonstrate the feasibility of using fMRS at 7T to detect stimulus-induced dynamics in cerebral NAD{square} metabolism in vivo, providing insights into the interplay between glycolysis and oxidative phosphorylation during neural activation.

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bioRxiv2025

Non-invasive prediction of conduction velocities in the human brain from MRI-derived microstructure features at 7 Tesla

The conduction velocity of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can be measured in presurgical assessment to provide information about conduction delay between a subset of brain regions, it is currently not possible to efficiently and systematically estimate conduction velocity in vivo across the whole brain. Given the established link between conduction velocity and axon morphology (most notably axon diameter but also myelination), mapping a reliable and quantitative metric linked to axon properties could fill the gap of inferring conduction velocity across the entire human brain. By integrating multiple MRI-derived microstructural measures - including axon radius, axonal water fraction, extra-axonal perpendicular diffusivity, and longitudinal relaxation time - and conduction velocity estimates obtained from a large database of CCEPs, we developed a whole-brain prediction model of conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological conduction velocity, making it possible to partially predict whole-brain conduction velocity and delay matrices along connections for which no direct measurement is commonly available from epilepsy surgery investigations. This integrative MRI-based approach could provide a non-invasive framework for comprehensively characterising conduction delays in vivo across the human brain white matter.

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bioRxiv2025

Complementary BOLD- and ADC-fMRI explore the role of lateral superior colliculus in flicker fusion frequency

The transition from static to dynamic vision is encoded in the superior colliculus, as recently shown using blood-oxygen-level-dependent functional MRI (BOLD-fMRI) of the rat brain. Visual stimulation at higher frequency than the flicker fusion frequency threshold leads to continuity illusion and is associated with negative BOLD response in the visual cortex, triggered by the superior colliculus. In this paper, we explored this mechanism using fMRI of the rat brain with visual stimulation at low (1Hz) and high (25Hz) frequency. We compared responses between different brain regions (the dorsolateral geniculate nucleus of the thalamus, the medial and lateral parts of the superior colliculus, and the visual cortex), sexes, and field strengths (9.4T and 14T, with varying contributions from large vessels). Results confirmed distinct neural responses to low and high frequency stimulation and highlighted the role of the lateral part of the superior colliculus in the transition from static to dynamic vision. Finally, we evaluated the ability of apparent diffusion coefficient (ADC)-fMRI to detect response to visual stimulation without vascular contribution. We found significant ADC-fMRI response in the medial and lateral parts of the superior colliculus but also in the corpus callosum. Our results highlight the ADC-fMRI high spatial specificity and high sensitivity to white matter.

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bioRxiv2025

CATERPillar: A Flexible Framework for Generating White Matter Numerical Substrates with incorporated Glial Cells

Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is the use of numerical phantoms that accurately represent the target tissue, which is in this case, cerebral white matter (WM). This study introduces CATERPillar (Computational Axonal Threading Engine for Realistic Proliferation), a novel method that simulates the mechanic of axonal growth using overlapping spheres as elementary units. CATERPillar facilitates parallel axon development while preventing collisions, offering user control over key structural parameters such as cellular density, undulation, beading and myelination. Its uniqueness lies in its ability to generate not only realistic axonal structures but also realistic glial cells, enhancing the biological fidelity of simulations. We showed that our grown substrates feature distributions of key morphological parameters that agree with those from histological studies. The structural realism of the astrocytic components was quantitatively validated using Sholl analysis. Furthermore, the time-dependent diffusion in the extra- and intra-axonal compartments accurately reflected expected characteristics of short-range disorder, as predicted by theoretical models. CATERPillar is open source and can be used to (a) develop new acquisition schemes that sensitise the MRI signal to unique tissue microstructure features, (b) test the accuracy of a broad range of analytical models, and (c) build a set of substrates to train machine learning models on. HighlightsO_LICATERPillar generates realistic synthetic voxels of white matter containing packed axons and glial cells. C_LIO_LISynthetic axons had similar morphologies to those segmented from human electron microscopy in previous works. C_LIO_LIThe morphological features of synthetic astrocytes closely matched those observed in histology. C_LIO_LIThe functional form of diffusion and kurtosis time-dependence in the intraand extra-axonal spaces agreed with experimentally observed disorder power laws in voxels composed of synthetic axons. C_LI

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bioRxiv2025

Evaluating the dependence of ADC-fMRI on haemodynamics inbreath-hold and resting-state conditions

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of mapping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by BOLD effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromorphological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two interleaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm-2; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO2 and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and a pattern of delayed haemodynamic response in white matter. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between expired CO2 and ADC timeseries, as well as low interand intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to microstructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds.

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bioRxiv2024

Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI

Functional MRI (fMRI) using the blood-oxygen level dependent (BOLD) signal provides valuable insight into grey matter activity. However, uncertainty surrounds the white matter BOLD signal. Apparent diffusion coefficient (ADC) offers an alternative fMRI contrast sensitive to transient cellular deformations during neural activity, facilitating detection of both grey and white matter activity. Further, through minimising vascular contamination, ADC-fMRI has the potential to overcome the limited temporal specificity of the BOLD signal. However, the use of linear diffusion encoding introduces sensitivity to fibre directionality, while averaging over multiple directions comes at great cost to temporal resolution. In this study, we used spherical b-tensor encoding to impart diffusion sensitisation in all directions per shot, providing an ADC-fMRI contrast capable of detecting activity independently of fibre directionality. We provide evidence from two task-based experiments on a clinical scanner that isotropic ADC-fMRI is more temporally specific than BOLD-fMRI, and offers more balanced mapping of grey and white matter activity. We further demonstrate that isotropic ADC-fMRI detects white matter activity independently of fibre direction, while linear ADC-fMRI preferentially detects activity in voxels containing fibres perpendicular to the diffusion encoding direction. Thus, isotropic ADC-fMRI opens avenues for investigation into whole-brain grey and white matter functional connectivity.

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bioRxiv2024

Activation mapping in multi-center rat sensory-evoked functional MRI datasets using a unified pipeline.

Functional Magnetic Resonance Imaging (fMRI) in rodents is pivotal for understanding the mechanisms underlying Blood Oxygen Level-Dependent (BOLD) signals and phenotyping animal models of disorders, amongst other applications. Despite its growing use, comparing rodent fMRI results across different research sites remains challenging due to variations in experimental protocols. Here, we aggregated and analyzed 22 sensory-evoked rat fMRI datasets from 12 imaging centers, totaling scans from 220 rats, to assess the consistency of results across diverse protocols. We applied a standardized preprocessing pipeline and evaluated the impact of different hemodynamic response function models on group and individual level activity patterns. Our analysis revealed inter-dataset variability attributed to differences in experimental design, anesthesia protocols, and imaging parameters. We identified robust activation clusters in all (22/22) datasets. The comparison between stock human models implemented in software and rat-specific models showed significant variations in the resulting statistical maps. Our findings emphasize the necessity for standardized protocols and collaborative efforts to improve the reproducibility and reliability of rodent fMRI studies. We provide open access to all datasets and analysis code to foster transparency and further research in the field.

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bioRxiv2024

Rebalance the inhibitory system in the elderly brain: Influence of balance learning on GABAergic inhibition and functional connectivity

Aging involves complex processes that impact the structure, function, and metabolism of the human brain. Declines in both structural and functional integrity along with reduced local inhibitory tone in the motor areas, as indicated by reduced {gamma}-Aminobutyric acid (GABA) levels, are often associated with compromised motor performance in elderly adults. Using multi-modal neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion magnetic resonance imaging (MRI), functional MRI as well as transcranial magnetic stimulation to assess short interval intracortical inhibition (SICI), this study explores whether these age-related changes can be mitigated by motor learning. The investigation focused on the effects of long-term balance learning (3 months) on intracortical inhibition, metabolism, structural and functional connectivity in the cortical sensorimotor network among an elderly cohort. We found that after three months of balance learning, subjects significantly improved balance performance, upregulated sensorimotor cortical GABA levels and ventral sensorimotor network functional connectivity (VSN-FC) compared to a passive control group. Furthermore, correlation analysis suggested a positive association between baseline VSN-FC and balance performance, between baseline VSN-FC and SICI, and between improvements in balance performance and upregulation in SICI in the training group, though these correlations did not survive the false discovery rate correction. These findings demonstrate that balance learning has the potential to counteract aging-related decline in functional connectivity and cortical inhibition on the tonic (MRS) and functional (SICI) level and shed new light on the close interplay between the GABAergic system, functional connectivity, and behavior.

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bioRxiv2024

Apparent Diffusion Coefficient fMRI shines a new light on white matter resting-state connectivity, as compared to BOLD

Resting-state functional magnetic resonance imaging (fMRI) is used to derive functional connectivity (FC) between brain regions. Typically, blood oxygen level-dependent (BOLD) contrast is used. However, BOLDs reliance on neurovascular coupling poses challenges in reflecting brain activity accurately, leading to reduced sensitivity in white matter (WM). WM BOLD signals have long been considered physiological noise, although recent evidence shows that both stimulus-evoked and resting-state WM BOLD signals resemble those in gray matter (GM), albeit smaller in amplitude. We introduce apparent diffusion coefficient fMRI (ADC-fMRI) as a promising functional contrast for GM and WM FC, capturing activity-driven neuromorphological fluctuations. Our study compares BOLD-fMRI and ADC-fMRI FC in GM and WM, showing that ADC-fMRI mirrors BOLD-fMRI connectivity in GM, while capturing more robust FC in WM. ADC-fMRI displays higher average clustering and average node strength in WM, and higher inter-subject similarity, compared to BOLD. Taken together, this suggests that ADC-fMRI is a reliable tool for exploring FC that incorporates gray and white matter nodes in a novel way.

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bioRxiv2024

Unveiling microstructural dynamics: Somatosensory-evoked response induces extensive diffusivity and kurtosis changes associated with neural activity in rodents

Neural tissue microstructure is dynamic during brain activity, presenting changes in cellular morphology and membrane permeability. The sensitivity of diffusion MRI (dMRI) to restrictions and hindrances in the form of cell membranes or subcellular structures enables the exploration of brain activity under a new paradigm, offering a more direct functional contrast than its BOLD counterpart. The current work aims at probing Mean Diffusivity (MD) and Mean Kurtosis (MK) changes and their time-dependence signature across various regions in the rat brain during somatosensory processing and integration, upon unilateral forepaw stimulation. We report a decrease in MD in the contralateral primary somatosensory cortex, forelimb region (S1FL), previously ascribed to cellular swelling and increased tortuosity in the extracellular space, paralleled by a positive BOLD response. For the first time, we also report a paired decrease in MK during stimulation in S1FL, suggesting increased membrane permeability. This observation was further supported by the reduction in exchange time estimated from the kurtosis time-dependence analyses. Conversely, the secondary somatosensory cortex and subcortical areas, formerly reported as responsive to sensory stimulation in rodents (thalamus, striatum, hippocampal subfields), displayed a marked MD and MK increase, paralleled by a weak-to-absent BOLD response. Overall, MD and MK uncovered functional-induced changes with higher sensitivity than BOLD. Although the exact origin of the MD and MK increase is yet to be unraveled, the potential of dMRI to provide complementary functional insights, even below the BOLD detection threshold, has been showcased.

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bioRxiv2023

Non-exponential transverse relaxation decay in subcortical grey matter

According to theoretical studies, the MRI signal decay due to transverse relaxation, in brain tissue with magnetic inclusions (e.g. blood vessels, myelin, iron-rich cells), exhibits a transition from a Gaussian behaviour at short echo times to exponential at long echo times. Combined, the Gaussian and exponential decay parameters carry information about the inclusions (e.g., size, volume fraction) and provide a unique insight into brain tissue microstructure. However, gradient echo decays obtained experimentally typically only capture the long-time exponential behaviour. Here, we provide experimental evidence of non-exponential transverse relaxation signal decay at short times in human subcortical grey matter, from MRI data acquired in vivo at 3T. The detection of the non-exponential behaviour of the signal decay allows the subsequent characterization of the magnetic inclusions in the subcortex. The gradient-echo data was collected with short inter-echo spacings, a minimal echo time of 1.25 ms and novel acquisition strategies tailored to mitigate the effect of motion and cardiac pulsation. The data was fitted using both a standard exponential model and non-exponential theoretical models describing the impact of magnetic inclusions on the MRI signal. The non-exponential models provided superior fits to the data, indicative of a better representation of the observed signal. The strongest deviations from exponential behaviour were detected in the substantia nigra and globus pallidus. Numerical simulations of the signal decay, conducted from histological maps of iron concentration in the substantia nigra, closely replicated the experimental data - highlighting that non-heme iron can be at the source of the non-exponential signal decay. To investigate the potential of the non-exponential signal decay as a tool to characterize brain microstructure, we attempted to estimate the properties of the inclusions at the source of this decay behaviour using two available analytical models of transverse relaxation. Under the assumption of the static dephasing regime, the magnetic susceptibility and volume fractions of the inclusions was estimated to range from 1.8 to 4 ppm and from 0.02 to 0.04 respectively. Alternatively, under the assumption of the diffusion narrowing regime, the typical inclusion size was estimated to be [~]2.4 m. Both simulations and experimental data point towards an intermediate regime with a non-negligible effect of water diffusion to signal decay. Non-exponential transverse relaxation decay provides new means to characterize the spatial distribution of magnetic material within subcortical grey matter tissue with increased specificity, with potential applications to Parkinsons disease and other pathologies.

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