Biology subjects

Topgaard, D.

Publications and source records attributed to Topgaard, D..

10 recordsLinked to original sources

bioRxiv2026

Multidimensional diffusion MRI reveals heterogeneous microstructural remodeling associated with amyloid pathology

Alzheimer's disease (AD) pathology involves amyloid deposition, reactive gliosis, and localized tissue alterations that coexist within the same brain regions, creating heterogeneous microstructural environments within individual imaging voxels. Conventional diffusion MRI averages these environments into aggregate measures, potentially obscuring their distinct contributions. Frequency-dependent multidimensional MRI ({omega}MD-MRI) resolves distributions of water components with different diffusion length scales, anisotropies, and relaxation properties, providing sensitivity to microstructural restriction, heterogeneity, and shape-size correlations within a voxel. Whether these measurements reveal microstructural complexity associated with AD pathology remains unclear. Here, we performed {omega}MD-MRI on ex vivo brain specimens from approximately 8-month-old 5xFAD and wild-type mice and interpreted the imaging findings alongside complementary histology. {omega}MD-MRI revealed widespread but spatially nonuniform differences between 5xFAD and wild-type brains. Measurements sensitive to microstructural restriction, heterogeneity, and shape-size correlations consistently indicated greater microstructural heterogeneity in 5xFAD brains, with the most prominent differences in the hippocampal formation and major cerebral white matter tracts. Complementary qualitative histology demonstrated extensive amyloid deposition and glial activation in affected regions, while overall cytoarchitecture and myelin organization remained largely preserved. Thus, the {omega}MD-MRI abnormalities occurred in tissue characterized by multiple coexisting pathological and relatively preserved microstructural environments rather than widespread structural degeneration. These findings demonstrate that {omega}MD-MRI can reveal the spatial and microstructural heterogeneity associated with amyloid pathology and provide a more comprehensive characterization of AD-related tissue alterations.

neuroscience↗

bioRxiv2025

Frequency-dependent diffusion tensor distribution imaging in the evaluation of ischemic stroke

Non-invasive MRI is widely used to assess and monitor ischemic stroke, yet conventional approaches often lack sensitivity to subtle microstructural changes and struggle to evaluate tissue viability across lesion, penumbra, and distal regions. In this study, frequency dependent diffusion tensor distribution imaging ({omega}DTD) was combined with clustering of diffusion tensor distributions D({omega}) and multivariate regression modeling to characterize ischemic tissue alterations in a whole brain section. Ex vivo {omega}DTD and histology were performed in rats subjected to middle cerebral artery occlusion (MCAO) or sham surgery (P = 17) 24 hours after reperfusion. Lesions showed cell loss and an increased presence of smaller, likely glial, cells. A random forest (RF) model was used to explain and predict histological parameters from diffusion tensor imaging (DTI), manually bin resolved {omega}DTD features, and cluster resolved {omega}DTD parameters. Model performance was evaluated using leave one animal out cross validation (LOO CV). {omega}DTD features better represented cell number than DTI metrics ({omega}DTD R2 = 0.73 vs. DTI R2 = 0.49), with similar advantages for nuclear area and circularity ({omega}DTD R2 = 0.64 and 0.61 vs. DTI R2 = 0.40 and 0.35). The RF model further proved beneficial in capturing complex, nonlinear relationships between MRI parameters and tissue characteristics. Overall, these results indicate that {omega}DTD provides richer microstructural information than standard DTI, and that combining {omega}DTD with advanced machine learning methods enhances interpretation of ischemic tissue damage.

neuroscience↗

bioRxiv2025

Data-driven classification of tissue water populations by massively multidimensional diffusion-relaxation correlation MRI

Massively multidimensional diffusion-relaxation correlation MRI provides detailed information on tissue microstructure by analyzing water populations at a sub-voxel level. This method correlates frequency-dependent tensor-valued diffusion MRI with longitudinal and transverse relaxation rates, generating nonparametric D({omega})-R1-R2 distributions. Traditionally, D({omega})-R1-R2 distributions are separated using manual binning of the diffusivity and anisotropy space to differentiate white matter (WM), gray matter (GM), and free water (FW) in brain tissue. However, while effective, this approach oversimplifies complex tissue fractions and does not fully utilize all available diffusion-relaxation parameters. In this study, we implemented an unsupervised clustering approach to automatically classify WM, GM, and FW and explore additional water populations using all components in the D({omega})-R1-R2 distributions on ex vivo and in vivo rat brain, and in vivo human brain. Results showed that a basic separation of WM, GM, and FW is possible using unsupervised clustering even under different multidimensional diffusion-relaxation protocols of rat brain and human brain. Additionally, when there is high frequency-dependent diffusion range, it is possible to obtain a cluster characterized by restriction localized in specific high cell density regions such as the dentate gyrus and cerebellum of rat brain. These findings were compared with rat histological sections of myelin and Nissl stainings. We demonstrated that unsupervised clustering of diffusion-relaxation MRI data can reveal tissue complexity beyond traditional WM, GM, and FW segmentation in rat and human brain without parameter assumptions. The unsupervised cluster approach could be used in other body parts (e.g., prostate and breast cancer) without requiring pre-defined bin limits. Furthermore, the characterization of the clusters by diffusivities, anisotropy, and relaxation rates can provide a better understanding of the subtle changes in different cellular fractions in tissue-specific pathologies.

neuroscience↗

bioRxiv2025

Ex vivo massively multidimensional diffusion-relaxation correlation MRI: scan-rescan reproducibility and caveats

Massively multidimensional diffusion-relaxation correlation MRI (MMD-MRI) provides information beyond the traditional voxel-averaged metric that may better characterize the microstructural characteristics of biological tissues. MMD-MRI reproducibility has been established in clinical settings, but has yet to be thoroughly evaluated under preclinical conditions, where superior hardware and modulated gradient waveforms enhance its performance. In this study, we investigate the reproducibility of MMD-MRI on a micro-imaging system using ex vivo mouse brains. Notably, the estimated signal fractions of intra-voxel spectral components in the MD-MRI distribution, corresponding to white and gray matter, along with the frequency-dependent parameters, demonstrated high reproducibility. We identified bias between scan and rescan in some of the metrics, which we attribute to the time gap between repeated scans pointing to a long-time progressive fixation effect. We compare our results with in vivo results from clinical scanners and show the reproducibility of diffusion frequency-dependent metrics to benefit from the improved gradient hardware on our preclinical setup. Our results inform future micro-imaging ex vivo MMD-MRI studies of the reproducibility of MMD-MRI metrics and their dependence on fixation time.

biophysics↗

bioRxiv2024

In vivo rat-brain mapping of multiple gray matter water populations using nonparametric D(omega)-R1-R2 distributions MRI

Massively Multidimensional Diffusion MRI combines tensor-valued encoding, oscillating gradients, and diffusion-relaxation correlation to provide multicomponent sub-voxel parameters depicting the tissue microstructure. This method was successfully implemented ex vivo in micro-imaging systems and in clinical conditions but with a reduced diffusion frequency ({omega}) range due to the use of classical tensor-valued encoding. We demonstrate here its preclinical in vivo implementation with a protocol of 389 contrast images probing a wide diffusion frequency range of 18 to 92 Hz at b-values up to 2.1 ms/{micro}m2 enabled by the use of modulated gradient waveforms and combined with multislice high-resolution and low-distortion EPI acquisition with segmented and full reversed phase-encode acquisition. This framework allows the identification of diffusion{omega} -dependence in the rat cerebellum and olfactory bulb gray matter (GM) and the parameter distributions are shown to resolve two water pools in GM with different diffusion coefficients, shapes,{omega} -dependence, relaxation rates, and spatial repartition whose attribution to specific microstructure could modify the current understanding of the origin of restriction in GM.

biophysics↗

bioRxiv2024

The variability of multidimensional diffusion-relaxation MRI estimates in the human brain

Diffusion-relaxation correlation multidimensional MRI (MD-MRI) replaces voxel-averaged diffusion tensor quantities and R1 and R2 relaxation rates with their multidimensional distributions, enabling the selective extraction and mapping of specific diffusion-relaxation spectral ranges that correspond to different cellular features. This approach has the potential of achieving high sensitivity and specificity in detecting subtle changes that would otherwise be averaged out. Here, the whole brain characterization of MD-MRI distributions and derived parameters is presented and the intrascanner test-retest reliability, repeatability, and reproducibility are evaluated to promote the further development of these quantities as neuroimaging biomarkers. We compared white matter tracts and cortical and subcortical gray matter regions, revealing notable variations in their diffusion-relaxation profiles, indicative of unique microscopic morphological characteristics. We found that the reliability and repeatability of MD-MRI-derived diffusion and relaxation mean parameters were comparable to values expected in conventional diffusion tensor imaging and relaxometry studies. Importantly, the estimated signal fractions of intra-voxel spectral components in the MD-MRI distribution, corresponding to white matter, gray matter, and cerebrospinal fluid, were found to be reproducible. This underscores the viability of employing a spectral analysis approach to MD-MRI data. Our results show that a clinically feasible MD-MRI protocol can reliably deliver information of the rich structural and chemical variety that exists within each imaging voxel, creating potential for new MRI biomarkers with enhanced sensitivity and specificity.

neuroscience↗

bioRxiv2024

Frequency-dependence in multidimensional diffusion-relaxation correlation MRI of the brain: overfitting or meaningful parameter?

Time- or frequency-dependent ("restricted") diffusion potentially provides useful information about cellular-scale structures in the brain but is challenging to interpret because of the intravoxel tissue heterogeneity. Frequency-dependence was recently incorporated in a multidimensional diffusion-relaxation correlation MRI framework relying on tensor-valued diffusion encoding at multiple frequencies, as well as variable echo and repetition times, to give model-free characterization of intravoxel heterogeneity by Monte Carlo data inversion into nonparametric distributions of frequency-dependent diffusion tensors and nuclear relaxation rates. While microimaging equipment with high-amplitude magnetic field gradients allows exploration of frequencies from tens to hundreds of Hz, clinical scanners with more moderate gradient capabilities are limited to a narrow frequency range, which may be insufficient to observe effects of restricted diffusion for brain tissues. We here investigate the effects of including or omitting frequency-dependence in the data inversion from isotropic and anisotropic liquids, excised tumor tissue, ex vivo mouse brain, and in vivo human brain. For microimaging measurements covering a wide frequency range, from 35 to 320 Hz at b-values over 4{middle dot}109 sm-2, the inclusion of frequency-dependence drastically reduces the fit residuals and avoids bias in the diffusion metrics for tumor and brain voxels with micrometer-scale structures. Conversely, for the case of in vivo human brain investigated in the narrow frequency range from 5 to 11 Hz at b = 3{middle dot}109 sm-2, analyses with and without inclusion of frequency-dependence yield similar fit residuals and diffusion metrics for all voxels. These results indicate that frequency-dependent inversion may be generally applied to diffusion-relaxation correlation MRI data with and without observable effects of restricted diffusion.

biophysics↗

bioRxiv2023

In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,{omega} , in addition to the diffusion tensor, D, and relaxation, R1, R2, correlations. A D({omega})-R1-R2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D({omega})-R1-R2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

neuroscience↗

bioRxiv2020

Visualizing orientation-specific relaxation-diffusion features mapped onto orientation distribution functions estimated via nonparametric Monte Carlo MRI signal inversion

Diffusion MRI techniques are widely used to study in vivo changes in the human brain connectome. However, to resolve and characterise white matter fibres in heterogeneous diffusion MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and restrictive constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times in order to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins in order to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work may be useful for investigating the microstructural properties along individual white matter pathways.

neuroscience↗

bioRxiv2019

Intermolecular interactions play a role in the distribution and transport of charged contrast agents in a cartilage model

The transport and distribution of charged molecules in polyelectrolyte solutions are of both fundamental and practical importance. A practical example, which is the specific subject addressed in the present paper, is the transport and distribution of charged species into cartilage. The charged species could be a contrast agent or a drug molecule involved in diagnosis or treatment of the widespread degenerative disease osteoarthritis, which leads to degradation of articular cartilage. Associated scientific issues include the rate of transport and the equilibrium concentrations of the charged species in the cartilage and the synovial fluid. To address these questions, we present results from magnetic resonance micro-imaging experiments on a model system of articular cartilage. The experiments yield temporally and spatially resolved data on the transport of a negatively charged contrast agent (charge = -2), used in medical examinations of cartilage, into a polyelectrolyte solution, which is designed to capture the electrostatic interactions in cartilage. Also presented is a theoretical analysis of the transport where the relevant differential equations are solved using finite element techniques as well as treated with approximate analytical expressions. In the analysis, non-ideal effects are included in the treatment of the mobile species in the system. This is made possible by using results from previous Monte Carlo simulations. The results demonstrate the importance of taking non-idealities into account when data from measurements of transport of charged solutes in a system with fixed charges from biological polyelectrolytes are analyzed.

biophysics↗