Biology subjects

Feiweier, T.

Publications and source records attributed to Feiweier, T..

4 recordsLinked to original sources

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.

neuroscience↗

bioRxiv2025

Spatial Image Gradient Estimation from the Diffusion MRI Profile

PurposeIn the course of diffusion, water molecules experience varying values for the relaxation-time properties of the underlying tissue. This factor, which has rarely been accounted for in diffusion MRI (dMRI), is modeled in this work, allowing for the estimation of the gradient of the relaxation-time properties from the dMRI signal. MethodsWith the aim of mining the dMRI data for information about the spatial variations in the tissue relaxation-time properties, a new mathematical relationship between the diffusion signal and the spatial gradient of the image is derived, enabling the estimation of the latter from the former. The hypothesis was validated on human brain dMRI images from three datasets: the public Human Connectome Project Young Adults database, 10 healthy volunteers and 1 ex vivo sample scanned in-house with stimulated-echo diffusion encoding and a long diffusion time of 1 second (which will be made publicly available), and 3 subjects from the public Multi-TE database. The effects of the confounding factor of "fiber continuity" were furthermore measured. ResultsThe image spatial gradient estimated from the diffusion signal was compared to the gold-standard spatial gradient approximated through the finite difference. The former gradient was significantly related to the latter in all datasets (i.e., with a difference significantly smaller than chance), with an effect distinct from fiber continuity. ConclusionThe results support the hypothesized relationship between within-voxel dMRI signal and image gradient, with an effect that was not explainable by the confounding factor of fiber continuity.

bioengineering↗

bioRxiv2024

Human gray matter microstructure mapped using Neurite Exchange Imaging (NEXI) on a clinical scanner

Biophysical models of diffusion in gray matter (GM) can provide unique information about microstructure of the human brain, in health and disease. Therefore, their compatibility with clinical settings is key. Neurite Exchange Imaging (NEXI) is a two-compartment model of GM microstructure that accounts for inter-compartment exchange, whose parameter estimation requires multi-shell multi-diffusion time data. In this work, we report the first estimates of NEXI in human cortex obtained on a clinical MRI scanner. To do that, we establish an acquisition protocol and fitting routine compatible with clinical scanners. The model signal equation can be expressed either in the narrow-pulse approximation, NEXINPA, or accounting for the actual width of the diffusion gradient pulses, NEXIWP. While NEXINPA enables a faster analytical fit and is a valid approximation for data acquired on high-performance gradient systems (preclinical and Connectom scanners), on which NEXI was first implemented, NEXIWP has significant relevance for data acquired on clinical scanners with longer gradient pulses. We establish that, in the context of broad pulses, NEXIWP estimates were more comparable to previous literature values. Furthermore, we evaluate the repeatability of NEXI estimates in the human cortex on a clinical MRI scanner and show intra-subject variability to be lower than inter-subject variability, which is promising for characterizing healthy and patient cohorts. Finally, we analyze the relationship of NEXI parameters on the cortical surface to the Myelin Water Fraction (MWF), estimated using an established multicomponent T2 relaxation technique. Indeed, although it is present in small quantities in the cortex, myelin can be expected to decrease permeability. We confirm a strong correlation between the exchange time (tex) estimates and the MWF, although the spatial correspondence between the two is brain-region specific and other drivers of tex than myelin density are likely at play.

neuroscience↗

bioRxiv2021

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusion Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mappings in neural tissue. However, the specificity of DKI is limited as different microstructural sources can contribute to the total diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to intravoxel diffusion anisotropy (Kaniso), and microscopic kurtosis (K) related to restricted diffusion and/or microstructural disorder. The latter in particular is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible. Recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation and revealed non negligible K in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the kurtosis sources in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of the relative importance of K in the healthy brain. Using this clinical CTI approach, we find that K significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first K maps of the human brain are presented. We find that the spatial distribution of K provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. We further show that ignoring K - as done by many contemporary methods based on multiple gaussian component approximation for kurtosis source estimation - biases the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue. HighlightsO_LICorrelation Tensor MRI (CTI) was recently proposed to resolve kurtosis sources C_LIO_LIWe implemented CTI on a 3T scanner to study kurtosis sources in the human brain C_LIO_LIIsotropic, anisotropic, and microscopic kurtosis sources were successfully resolved C_LIO_LIMicroscopic kurtosis (K) significantly contributes to overall kurtosis in human brain C_LIO_LIK provides a novel source of contrast in the human brain in vivo C_LI

neuroscience↗