Biology subjects

Magdoom, K. N.

Publications and source records attributed to Magdoom, K. N..

6 recordsLinked to original sources

bioRxiv2026

Towards Mesoscopic Human Brain Imaging Using Non-Parametric Diffusion Tensor Distribution (DTD) MRI

The majority of MR-based brain imaging methods provides macroscopic information averaged over the entire imaging voxel. Yet tissue composition and microstructure are heterogeneous within the cubic millimeter-sized MRI voxel that contains numerous distinct water pools at mesoscopic, microscopic, and nanoscopic length scales. Accurately measuring their individual characteristics in live human brain has the potential to reveal hidden salient meso/micro-structural features and uncover subtle changes that may occur in development, neurological disorders, trauma, etc. Nevertheless, because of many technical and scientific challenges, there is a dearth of robust, quantitative methods to probe tissue water dynamics at these subvoxel length scales. Here we present a novel empirical spectroscopic diffusion MRI method that estimates the probability density function (pdf) of diffusion tensors, i.e., the diffusion tensor distribution (DTD) in the human brain in-vivo. Our method entails performing a multi-dimensional Inverse Laplace Transform (ILT) which is generally an ill-posed and ill-conditioned problem. However, we overcome these obstacles using a hierarchy of lower-dimensional marginal distributions of the DTD estimated from diffusion weighted (DW) signals obtained from single, double, and triple pulsed-field gradient (PFG) experiments. Iteratively applying this hierarchy of marginal distribution progressively shrinks the space of admissible solutions. We extensively vet this framework with simulated DWI data obtained from realistic DTD motifs that mimic different cell and tissue properties seen in the brain. We then experimentally test our approach in vivo in brains of healthy normal human subjects. We segment the reconstructed DTD within a voxel to identify signatures of different tissue and cell types, and cluster these DTDs to identify various water pools. We use the high dimensional spectrum to robustly remove the free water compartment that often confounds tissue microstructure. We take ensemble averages of invariants of the micro-diffusion tensors, and measure and map their distributions to visualize salient intrinsic mesoscopic features. Since DTD MRI subsumes DTI, we also compute the family of DTI-derived quantitative imaging biomarkers from the moments of the distributions of the mean diffusivity and FA derived from the DTD. Our approach has great translational potential, revealing new microstructural features not observed previously observed in in vivo MRI.

bioengineering↗

bioRxiv2025

In vivo palpation of anisotropic human brain tissue using MRI

"Remote palpation" appears to be an oxymoron, but here we demonstrate a non-contacting MRI method to obtain mechanical stiffness parameters of the human brain solely by measuring deformations caused by the pumping action of the heart. Mechanical stiffness is an important tissue property that is highly sensitive to subtle changes in the tissue milieu; MR elastography (MRE) is among a handful of methods used to measure it, typically via an external driver/tamper that introduces mechanical waves into the tissue. Applying MRE in the brain is challenging due to the use of an external actuator/tamper and the mechanical anisotropy of brain tissue, which requires a 4th-order tensor to describe it. In this study, we use the intrinsic deformation of brain tissue caused by periodic cardiac pulsations to measure the 4th-order elasticity tensor throughout the brain while simultaneously estimating the 2nd-order diffusion tensor in each voxel throughout the cardiac cycle which we use as a priori information in the reconstruction of the elasticity tensor. While the DTI-derived mean diffusivity (MD) appears uniform throughout brain parenchyma, stiffness maps obtained at about 1 Hz (i.e., at the fundamental cardiac frequency) show that brain tissue is very soft within gray matter, and within white matter pathways, such as along the corpus callosum, corona radiata, etc. Generally, stiffness differences at internal tissue boundaries are expected to produce local stress concentration there, which may predispose tissues to damage, e.g., in traumatic brain injury (TBI). Therefore, our novel tamperless MRE method has the potential to not only identify such interfaces, but assess and follow changes in tissue stiffness there that might occur following injury.

neuroscience↗

bioRxiv2024

Water Diffusion in the Live Human Brain is Gaussian at the Mesoscale

Imaging the live human brain at the mesoscopic scale is a desideratum in basic and clinical neurosciences. Despite the promise of diffusion MRI, the lack of an accurate model relating the measured signal and the associated microstructure has hampered its success. The widely used diffusion tensor MRI (DTI) model assumes an anisotropic Gaussian diffusion process in each voxel, but lacks the ability to capture intravoxel heterogeneity. This study explores the extension of the DTI model to mesoscopic length scales by use of the diffusion tensor distribution (DTD) model, which assumes a Gaussian diffusion process in each subvoxel. DTD MRI has shown promise in addressing some limitations of DTI, particularly in distinguishing among different types of brain cancers and elucidating multiple fiber populations within a voxel. However, its validity in live brain tissue has never been established. Here, multiple diffusion-encoded (MDE) data were acquired in the living human brain using a 3 Tesla MRI scanner with large diffusion weighting factors. Two different diffusion times ({Delta} = 37, 74 ms) were employed, with other scanning parameters fixed to assess signal decay differences. In vivo diffusion-weighted signals in gray and white matter were nearly identical at the two diffusion times. Fitting the signals to the DTD model yielded indistinguishable results, except in the cerebrospinal fluid (CSF)-filled voxels likely due to pulsatile flow. Overall, the study supports the time invariance of water diffusion at the mesoscopic scale in live brain parenchyma, extending the validity of the anisotropic Gaussian diffusion model in clinical brain imaging.

biophysics↗

bioRxiv2023

Multimodal anatomical mapping of subcortical regions in Marmoset monkeys using high-resolution MRI and matched histology with multiple stains.

Subcortical nuclei and other deep brain structures play essential roles in regulating the central and peripheral nervous systems. However, many of these nuclei and their subregions are challenging to identify and delineate in conventional MRI due to their small size, hidden location, and often subtle contrasts compared to neighboring regions. To address these limitations, we scanned the whole brain of the marmoset monkeys in ex vivo using a clinically feasible diffusion MRI method, called the mean apparent propagator (MAP)-MRI, along with T2W and MTR (T1-like contrast) images acquired at 7 Tesla. Additionally, we registered these multimodal MRI volumes to the high-resolution images of matched whole-brain histology sections with seven different stains obtained from the same brain specimens. At high spatial resolution, the microstructural parameters and fiber orientation distribution functions derived with MAP-MRI can distinguish the subregions of many subcortical and deep brain structures, including fiber tracts of different sizes and orientations. The good correlation with multiple but distinct histological stains from the same brain serves as a thorough validation of the structures identified with MAP-MRI and other MRI parameters. Moreover, the anatomical details of deep brain structures found in the volumes of MAP-MRI parameters are not visible in conventional T1W or T2W images. The high-resolution mapping using novel MRI contrasts, combined and correlated with histology, can elucidate structures that were previously invisible radiologically. Thus, this multimodal approach offers a roadmap toward identifying salient brain areas in vivo in future neuroradiological studies. It also provides a useful anatomical standard reference for the region definition of subcortical targets and the generation of a 3D digital template atlas for the marmoset brain research (Saleem et al., 2023). Additionally, we conducted a cross-species comparison between marmoset and macaque monkeys using results from our previous studies (Saleem et al., 2021). We found that the two species had distinct patterns of iron distribution in subregions of the basal ganglia, red nucleus, and deep cerebellar nuclei, confirmed with T2W MRI and histology.

neuroscience↗

bioRxiv2022

A Novel Framework for In-vivo Diffusion Tensor Distribution MRI of the Human Brain

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe heterogeneity at the mesoscopic length scale, orders of magnitude smaller than the nominal MRI voxel size, by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring tensor encoded diffusion weighted images (DWIs) and estimating DTD from them for in-vivo human brain imaging. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined gradient pulse duration and mixing/diffusion times in our diffusion preparation, we show that iPFG retains salient features of traditional multiple-PFG (mPFG) sequence while overcoming some of its implementation issues thereby extending its applications beyond DTD MRI. We assume DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite (CNTVD) to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape and orientation distributions to best fit the measured DWIs. From these tensors we obtain the mean diffusivity (MD) spectrum, spectrum of diffusion tensor shapes, microscopic orientation distribution function ({micro}ODF), and microscopic fractional anisotropy ({micro}FA) which disentangle the underlying heterogeneity within a voxel. Using DTD derived {micro}ODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results obtained in the live human brain showed microscopic anisotropy in various gray and white matter regions and skewed MD distribution in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and identified the source of microscopic anisotropy which may help improve the diagnosis of various neurological diseases and disorders.

neuroscience↗

bioRxiv2020

A New Framework for MR Diffusion Tensor Distribution

The ability to characterize heterogeneous and anisotropic water diffusion processes within macro-scopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values unlike existing models. We use a Monte Carlo scheme to synthesize realistic numerical diffusion tensor distribution (DTD) phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.

neuroscience↗