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Hussain, U.

Publications and source records attributed to Hussain, U..

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Investigating the in vivo Spatial Distribution of Hippocampal Microstructure and Macrostructure

The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current MR based neuroimaging techniques. In this work, we used diffusion MRI and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the Neurite Orientation Dispersion and Density Imaging (NODDI) model optimized for gray matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the Cornu Ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibers, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitecture differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures provided good separability across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fiber pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo diffusion MRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.

neuroscience↗

DIFFUSION MRI OF THE UNFOLDED HIPPOCAMPUS

The hippocampus is implicated in numerous neurological disorders and the ability to detect subtle or focal hippocampal abnormalities earlier in disease progression could significantly improve the treatment of patients. Ex vivo studies with ultra-high field have revealed that diffusion MRI (dMRI) can reveal microstructural variations within the hippocampal subfields and lamina, and may also be sensitive to intra-hippocampal pathways. However, translation to lower resolution in vivo dMRI studies of the hippocampus is challenging due to its complicated geometry. One novel way to overcome some of these obstacles is by transforming the usual Cartesian coordinates in an MRI image to coordinates that are crafted to curve themselves according to the complicated geometry of the hippocampus. This procedure allows us to virtually unfold the hippocampus into a thin sheet. In this work, we introduce an algorithm to map diffusion MRI data to this sheet, allowing us to overcome the difficulties associated with the hippocampus complicated geometry. We demonstrate how our method can be readily integrated into existing implementations of traditional tractography methods and how it leads to enhancements in the resulting tracts. Further, our results on high quality in vivo dMRI acquisitions show that unfolding the hippocampus leads to a more anatomically plausible modelling of the connectivity of the hippocampus as probed by probabilistic tractography, revealing key elements of the polysynaptic pathway and anterior-posterior connectivity gradients.

neuroscience↗