Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain

Abstract

The internal structure of the human hippocampus is challenging to map using histology or neuroimaging due to its complex archicortical folding. Here, we aimed to overcome this challenge using a unique combination of three methods. First, we leveraged a histological dataset with unprecedented 3D coverage, 3D BigBrain. Second, we imposed a computational unfolding framework that respects the topological continuity of hippocampal subfields, which are traditionally defined by laminar composition. Third, we adapted neocortical parcellation techniques to map the hippocampus with respect to not only laminar but also morphological features. Unsupervised clustering of these features revealed subdivisions that closely resemble ground-truth manual subfield segmentations. Critically, we also show that morphological features alone are sufficient to derive most hippocampal subfield boundaries. Moreover, some features showed differences within subfields along the hippocampal longitudinal axis. Our findings highlight new characteristics of internal hippocampal structure, and offer new avenues for its characterization with in-vivo neuroimaging.

The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

“…Note, the presented profile was subjected to smoothing as described in the following section. BigBrainWarp also supports integration of previous research on BigBrain including (D-E) cytoarchitectural and (F-G) morphological models (DeKraker et al, 2019;Paquola et al, 2020aPaquola et al, , 2019Wagstyl et al, 2020).…”

“…Tutorial 1: BigBrain  ICBM2009sym MNI152 space Motivation: Despite MRI acquisitions at high and ultra-high fields reaching submillimeter resolutions with ongoing technical advances, certain brain structures and subregions remain difficult to identify (Kulaga-Yoskovitz et al, 2015;Wisse et al, 2017;Yushkevich et al, 2015). For example, there are challenges in reliably defining the subthalamic nucleus (not yet released for BigBrain) or hippocampal Cornu Ammonis subfields [manual segmentation available on BigBrain, https://osf.io/bqus3/, (DeKraker et al, 2019)]. BigBrain-defined labels can be transformed to a standard imaging space for further investigation.…”

“…Manual segmentations of the putamen, caudate nucleus, globus pallidus pars externa, globus pallidus pars interna, nucleus accumbens, amygdala, thalamus, red nucleus, substantia nigra, subthalamic nucleus and the hippocampus are available on Open Science Framework (https://osf.io/xkqb3/). Extending upon wholestructure segmentation, a recent study (DeKraker et al, 2019) used anatomical landmarks to create an internal coordinate system of the hippocampus. The approach involved solving Laplace's equation under three sets of boundary conditions: anterior-posterior, proximal-distal (relative to the subiculum), and inner-outer (DeKraker et al, 2018).…”

“…It is specially tailored for neuroimagers, as it is made available in common MRI formats (minc and NifTI), accompanied by cortical surface reconstructions (Lewis et al, 2014), and nonlinearly registered to standard MRI templates (ICBM152 and MNI-ADNI) (Fonov et al, 2011a). Furthermore, recent studies have expanded the resource by offering improved registrations to standard spaces (Lewis et al, 2020;Xiao et al, 2019), nuanced intracortical surface models and laminar approximations (Wagstyl et al, 2018a(Wagstyl et al, , 2020 as well as regional segmentations (DeKraker et al, 2019;Xiao et al, 2019). Several studies have already capitalised on this unique resource for integrative histological-neuroimaging analyses, including comparison of cytoarchitectural and functional gradients (Paquola et al, 2019), cross-validation of in vivo defined microstructural gradients in the insula with histological measures (Royer et al, 2020), mapping variations in functional connectivity along the histological axis of the mesiotemporal lobe (Paquola et al, 2020b), fMRI responses of the histologically-defined auditory system (Sitek et al, 2019), comparison of cytoarchitectural similarity with MRI-derived estimates of structural connectivity (Wei et al, 2018), and analysis of the cytoarchitectural similarity of large-scale network hubs (Arnatkevičiūtė et al, 2020).…”

The structure of hippocampal circuitry relates to rapid category learning in humans

“…structural integrity of one's TSP-rather than other features like hippocampal volume-might be most predictive of learning, particularly when such learning primarily taxes TSP. However, to the best of our knowledge the integrity of connections between subfields along the TSP has been neither measured nor related to behavior among healthy young adults using diffusion-weighted imaging (DWI), leaving a large gap between theoretical frameworks highlighting the importance of intra-hippocampal pathways and empirical studies largely focussing on hippocampal volume (Canada et al, 2019;Chadwick et al, 2014;Daugherty et al, 2016;Travis et al, 2014) or morphology (DeKraker et al, 2020;Voineskos et al, 2015).…”