Convergence of cortical types and functional motifs in the mesiotemporal lobe

Abstract

The parahippocampus-hippocampus complex in the mesiotemporal lobe (MTL) is implicated in many different cognitive processes, is compromised in numerous disorders, and exhibits a unique cytoarchitectural transition from six-layered isocortex to three-layered allocortex. Our study leveraged an ultra-high-resolution histological reconstruction of a human brain to (i) develop a continuous surface model of the MTL iso-to-allocortex transition and (ii) quantitatively characterise the region's cytoarchitecture. We projected the model into the native space of in vivo functional magnetic resonance imaging of healthy adults to (iii) construct a generative model of its intrinsic circuitry and (iv) determine its relationship with distributed functional dynamics of macroscale isocortical fluctuations. We provide evidence that the most prominent axis of cytoarchitectural differentiation of the MTL follows infolding from iso-to-allocortex and is defined by depth-specific variations in neuron density. Intrinsic effective connectivity exhibited a more complex relationship to MTL geometry, varying across both iso-to-allocortical and anterior-posterior axes. Variation along the long axis of the MTL was associated with differentiation between transmodal and unimodal systems, with anterior regions linked to transmodal cortex. In contrast, the iso-to-allocortical gradient was associated with the multiple demand system, with isocortex linked to regions activated when task demands prohibit the use of prior knowledge. Our findings establish a novel model of the MTL, in which its broad influence on neural function emerges through the combination micro-and macro-scale structural features.

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).…”

“…Here, we illustrate how we track resting-state functional connectivity changes along the latero-medial axis of the mesiotemporal lobe, from parahippocampal isocortex towards hippocampal allocortex. For further details and additional motivation, please see (Paquola et al, 2020a): (i) Our volumetric label represents the iso-to-allocortical axis of the mesiotemporal lobe. We constructed this axis by joining the isocortical (Lewis et al, 2014) andhippocampal (DeKraker et al, 2019) surface meshes in BigBrain histological space, calculated the distance of each vertex in the new surface model to the intersection of isocortical and hippocampal meshes (Figure 3A).…”

“…Since then, a number of additional surface reconstructions have been published from which we may attain a range of metrics (Table 1). (Paquola et al, 2020a) Note: Initialisation broadly refers to an input for feature generation, for example creation of staining intensity profiles or surface transformations.…”

A structure-function substrate of memory for spatial configurations in medial and lateral temporal cortices

“…It is worth noting that while our result pertaining to the overall volume of the hippocampus corroborates prior evidence, our analytical approach may not have been sensitive enough to identify subregional effects. Further analyses with larger cohorts and/or higher resolution imaging of the hippocampus are required to more robustly explore subregional substrates in the hippocampus; these approaches may benefit from approaches that tap into hippocampal longitudinal and medio-lateral axes (Vos de Wael, et al, 2018;Plachti, et al, 2019;Przeździk, et al, 2019;Paquola, et al, 2020).…”

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

“…Since then, a number of additional surface reconstructions have been published from which we may attain a range of metrics (Table 1). (Wagstyl et al, 2018a) Equivolumetric Staining intensity profiles (Waehnert et al, 2014) Deep learning laminar Laminar thickness (Wagstyl et al, 2020) Hippocampal Initialisation and visualisation (DeKraker et al, 2019) Mesiotemporal confluence Initialisation and visualisation (Paquola et al, 2020a) Note: Initialisation broadly refers to an input for feature generation, for example creation of staining intensity profiles or surface transformations.…”