Biology subjects

Karat, B.

Publications and source records attributed to Karat, B..

4 recordsLinked to original sources

bioRxiv2025

Characterization of hippocampal subfields using histology-based annotated postmortem MRI: Lessons for in vivo segmentation II

High-resolution in vivo magnetic resonance imaging (MRI) of hippocampal subfields is a rapidly advancing field due to their implication in cognition, disorder, and disease. Hippocampal subfield segmentation on in vivo MRI is generally guided by postmortem reference material, which has been limited by small sample sizes that preclude comprehensive characterization of subfield border locations and their variability. Addressing this, we characterized hippocampal subfield border variability in two ultra-high-resolution postmortem MRI datasets with combined annotated histological sections, including cases with and without dementia. We examined: 1) the order of appearance and disappearance of subfields along the long axis of the hippocampus; 2) the order of appearance and disappearance of subicular subregions; 3) the medial-lateral position of subicular subregional boundaries along the hippocampal body; 4) the location of the CA3 relative to hippocampal head digitations; 5) the subfield borders in the hippocampal body relative to a volume proportion of the dark band; and 6) the association of hippocampal length and subiculum-CA1 border location with diagnosis, demographic factors, and factors related to postmortem imaging. Our findings reaffirmed that there is a consistent order of appearance and disappearance of subfields in the hippocampal head and tail, respectively. The subicular subregions exhibited a first in, last out order of appearance and disappearance, and pre/parasubiculum consistently occupied half of the subicular complex in coronal slices throughout the hippocampal body. Hippocampal head digitations were not a reliable landmark for CA3 appearance, but SRLM proportionality did offer a potentially consistent approach for estimating CA2 and CA3 subfield borders in relation to the hippocampal border. No clear relationship was found between the anatomical features and diagnosis, demographic factors, and factors related to postmortem imaging. These findings have implications for the development and harmonization of hippocampal subfield segmentation protocols and interpretation of high-resolution functional MRI studies of the human hippocampus.

neuroscience↗

bioRxiv2024

HippoMaps: Multiscale cartography of the human hippocampal formation

The hippocampus has a unique microarchitecture, is situated at the nexus of multiple macroscale functional networks, contributes to numerous cognitive as well as affective processes, and is highly susceptible to brain pathology across common disorders. These features make the hippocampus a model to understand how brain structure covaries with function, in both health and disease. Here, we introduce HippoMaps, an open access toolbox and online data warehouse for the mapping and contextualization of subregional hippocampal data in the human brain (http://hippomaps.readthedocs.io). HippoMaps capitalizes on a unified hippocampal unfolding approach as well as shape intrinsic registration capabilities to allow for cross-subject and cross-modal data aggregation. We initialize this repository with an unprecedented combination of hippocampal data spanning 3D ex-vivo histology, ex-vivo 9.4 Tesla MRI, as well as in-vivo structural MRI and resting-state functional MRI (rsfMRI) obtained at 3 and 7 Tesla, together with intracranial encephalography (iEEG) recordings in epilepsy patients. HippoMaps also contains validated tools for spatial map association analysis in the hippocampus that correct for autocorrelation. All code and data are compliant with community standards, and comprehensive online tutorials facilitate broad adoption. Applications of this work span methodologies and modalities, spatial scales, as well as clinical and basic research contexts, and we encourage community feedback and contributions in the spirit of open and iterative scientific resource development.

neuroscience↗

bioRxiv2022

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging templates and datasets. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (n = 132 subjects for a total of 15,232 fiducials) and more than 10 brain templates (4,288 fiducials), compiling over 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1-2 mm (using a total of 50,336 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure (BIDS) allowing for facile incorporation into modern neuroimaging analysis pipelines. Data is accessible on GitHub (https://github.com/afids/afids-data).

neuroscience↗

bioRxiv2021

HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subjects hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

neuroscience↗