BigBrain 3D atlas of cortical layers: cortical and laminar thickness gradients diverge in sensory and motor cortices

Abstract

<jats:title>Abstract</jats:title><jats:p>Histological atlases of the cerebral cortex, such as those made famous by Brodmann and von Economo, are invaluable for understanding human brain microstructure and its relationship with functional organization in the brain. However, these existing atlases are limited to small numbers of manually annotated samples from a single cerebral hemisphere, measured from 2D histological sections. We present the first whole-brain quantitative 3D laminar atlas of the human cerebral cortex. This atlas was derived from a 3D histological model of the human brain at 20 micron isotropic resolution (BigBrain), using a convolutional neural network to segment, automatically, the cortical layers in both hemispheres. Our approach overcomes many of the historical challenges with measurement of histological thickness in 2D and the resultant laminar atlas provides an unprecedented level of precision and detail.</jats:p><jats:p>We utilized this BigBrain cortical atlas to test whether previously reported thickness gradients, as measured by MRI in sensory and motor processing cortices, were present in a histological atlas of cortical thickness, and which cortical layers were contributing to these gradients. Cortical thickness increased across sensory processing hierarchies, primarily driven by layers III, V and VI. In contrast, fronto-motor cortices showed the opposite pattern, with decreases in total and pyramidal layer thickness. These findings illustrate how this laminar atlas will provide a link between single-neuron morphology, mesoscale cortical layering, macroscopic cortical thickness and, ultimately, functional neuroanatomy.</jats:p>

Shaping Brain Structure: Genetic and Phylogenetic Axes of Macro Scale Organization of Cortical Thickness

“…It is possible that the dorsal-ventral patterning of myelin in the upper layers reflects a dissociation in information processing, with sensory agranular regions providing feedforward information and project locally, whereas ventral, more granular paralimbic, regions are involved in feedback processing and project from infragranular layers 74, 75 . Additionally, we found comparable topologies in microstructural profile covariance and macro scale organization of thickness, in line with previous evidence that thickness topology relates to microstructural differentiation 14, 76 . Notably, both posterior-anterior macro scale organization patterns, as well as the combination of both the posterior-anterior and inferior-superior gradient showed a positive relation primary organizational axis of functional connectivity at rest.…”

Developmental Changes in EEG Phase Amplitude Coupling and Phase Preference over the First Three Years After Birth

“…It has been previously described that cortical layer 4 (which predominantly accepts thalamic input) and layer 6 (which predominantly provides thalamic input) (Guillery and Sherman, 2002) exhibit opposing phase preferences in respect to alpha oscillations (Bollimunta et al, 2011). This functional specialization is also reflected structurally: layer IV tends to be thick posteriorly but thin anteriorly in the cortex, whereas layer VI tends to be thick in frontal cortex but thin in occipital and other predominantly sensory cortices (Wagstyl et al, 2019). Scalp recordings cannot classically differentiate among the cortical layers that may be driving findings in particular electrodes.…”

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics: a framework for tracking neurodegenerative disease

“…This was done by mapping the von Economo MRI atlas onto the Big Brain data using a surface-based registration. Big Brain cortical layers were defined using a machine learning approach, as previously described (Wagstyl et al, 2019). Von Economo total cell count showed a significant positive correlation with average staining intensity (rho = 0.44, p = 0.003) at the ROI level (Fig 4.…”

“…The Big Brain atlas (Amunts et al, 2013) was created more recently. The brain of a 65 year-old neurotypical male was sectioned into 20μm slices, stained for cell bodies and reconstructed in 3-D. Machine learning approaches (Wagstyl et al, 2018; Wagstyl et al, 2019) have been employed to define cortical layers in the Big Brain data providing a comparable cortical layer histology atlas to the von Economo atlas. Using the von Economo and Big Brain atlases we could therefore test the hypothesis that UHF qMRI parameters near the pial surface are correlated with cell measures in superficial layers 1-3, UHF qMRI parameters at mid-cortical depth are correlated with layer 4 and UHF qMRI parameters near the grey matter/white matter (GM/WM) boundary are correlated with deep cortical layers 5-6.…”