Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI

Abstract

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules is challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200 μm spacing and 5 μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4 Tesla MRI scan of the intact, whole hippocampal formation acquired with 160 μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1 cm-thick tissue sub-blocks acquired with 200 μm isotropic resolution. These 1 cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

Regional Hippocampal Volumes and Development Predict Learning and Memory

“…Nevertheless, future reliability and validation studies on children and adolescents and across standard and submillimeter image resolution are surely awaited. Additionally, results obtained with the segmentation procedure used in the current study [29] should be compared with other available protocols [72,73,74], as a great deal of variability exists in both nomenclature and boundary rules. Third, as previous studies disagree with respect to whether adolescent memory development is associated with hippocampal or prefrontal cortical maturation [24,25], future studies should also analyze prefrontal cortical regions.…”

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

“…Our ex vivo imaging protocol yields images with extremely high resolution and signal-to-noise ratio, dramatically higher than is possible in vivo , which allows us to accurately identify more subregions with a delineation protocol specifically designed for this study. To the best of our knowledge, there is only one ex vivo atlas of the hippocampus, presented in (Yushkevich, et al, 2009); Adler et al (2014) have presented promising work towards an atlas based on both ex vivo MRI and histology, but they only labeled one case. Compared with Yushkevich’s atlas (henceforth “UPenn atlas”), our atlas (FreeSurfer v6.0) has the following advantages: 1. it is built at a higher resolution (0.13 mm isotropic, on average, vs. 0.2 mm); 2. it models a larger number of structures (15 vs. 5); 3. it is built upon a larger number of cases (15 vs. 5); and 4. in addition to the hippocampal subregions, it also models the surrounding structures, which enables its use in a generative modeling framework to directly segment in vivo MRI data of varying contrast properties.…”

High-resolution <em>In Vivo</em> Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

“…The most accurate analysis of neuroanatomy is through histological sectioning and staining, but this type of analysis suffers from a number of issues: limited access to fixed specimens (which results in very small sample sizes); the expertise required to prepare samples; distortions of the brain after fixation; and difficulties in applying a fixed atlas to digital, in vivo data 1,2,8 . In ex vivo imaging, long acquisition times of a fixed brain in an MR scanner also provides a detailed picture of neuroanatomy, but as with histology, sample number is limited and there are morphometric differences between the fixed and in vivo brain 37 .…”