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

Abstract

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. It was derived from a 3D histological atlas of the human brain at 20-micrometer 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. 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, motor-frontal cortices showed the opposite pattern, with decreases in total and pyramidal layer thickness from motor to frontal association cortices. 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.

Fig 1. Cortical layers in 3D.

Six cortical layers segmented on the 3D volume on 3 orthogonal planes: A = coronal, B = axial, C = sagittal. Panel D shows the location of the sections on the reconstructed pial surface of the 3D BigBrain. (A) The coronal plane is the original plane of sectioning. Within this plane, the axes are centered on an area of the cortex where layers would be impossible to segment in 2D because the section only shows part of the gyrus, and most layers are not visible because of the oblique sectioning of the cortex with respect to the gyrus. Underlying data available from ftp://bigbrain.loris.ca.

Fig 2. Cortical layers (colored lines) intersected on a 2D coronal section of the right occipital cortex with manually segmented layers (superimposed grayscale masks).

(A) The boundaries follow the same contours as delineated by the manually segmented training areas and appear to accurately follow the layer bounds outside of each training area. (B) At the V1–V2 boundary (marked with arrows), the thickness of layer IV changes dramatically in both manual and automated segmentations (between green and blue lines), with additional peaks in V1 intensity due to the sublayers of layer IV. As each profile is individually segmented by the network, without reference to the neighboring profiles, the network is able to apply area-specific rules according to the shape of the profile, suggesting it might be internally identifying the area from which the profile is extracted as being either V1 or V2. Underlying data available from ftp://bigbrain.loris.ca.

Fig 3. Comparison of cortical thickness from the BigBrain with von Economo and Koskinas histological measurement and MRI cortical thickness data from the Human Connectome Project [33].

Thickness values range from 1.8 mm in the calcarine sulcus to 4.5 mm in the precentral gyrus. (A) Per-vertex cortical thickness values from the BigBrain (displayed on smoothed surfaces, values were smoothed 3 mm FWHM). Thicker regions of the cortex included the precentral gyrus containing the primary motor cortex. The occipital cortex around the calcarine sulcus was particularly thin. Also visible are smaller-scale variations in thickness than can only be observed through such high-density measurement. Von Economo reported thickness measurements from around 50 cortical areas, whereas the thickness of around 1 million vertices has been measured on BigBrain. (B) Regional BigBrain thickness values were highly correlated with measurements from von Economo and Koskinas. The size of each point is proportional to the area of the cortical region, and overall correlations were weighted according to these areas. The precentral gyrus, area FA, was the area of greatest discrepancy where BigBrain provided a lower estimate than von Economo. This might, in part, have been due to averaging of many vertices across the precentral gyrus in BigBrain, in comparison to a single measurement made by von Economo. (C) Regional BigBrain thickness values were also highly correlated with MRI cortical thickness values. MRI thickness appears to be overestimated in the insula, where it is thin in both histological data sets. This may be as the insula is highly convoluted and thus challenging to accurately delineate at lower resolutions. Underlying data available from S1 Data, S2 Data, and ftp://bigbrain.loris.ca. Area FA, frontal area A; FWHM, full width at half maximum.

Fig 4. Comparison of von Economo’s laminar thickness maps (coregistered with and visualized on the BigBrain) with laminar thicknesses of the BigBrain for left and right hemispheres.

BigBrain thickness values were smoothed across the surface with a 3-mm FWHM Gaussian kernel. Layer thickness values strongly correlated between BigBrain and von Economo for all layers except layer II (see Results). Similarities include the clear changes in thickness in pre- and postcentral thicknesses of layers III, V, and VI. For layer IV, the most striking feature is the abrupt change in layer IV thickness at the V1–V2 border. This abrupt change and the unique features of layer IV in V1 lead us to conclude that the neural network may have internally learned to recognize V1 and apply the appropriate laminar segmentation rules. Underlying data available from S1 Data and ftp://bigbrain.loris.ca. FWHM, full width at half maximum.

Fig 5. Cortical thickness with increasing geodesic distance from the primary area.

To aid visualization, locally weighted scatterplot smoothing lines are fit for each hemisphere. For primary visual, auditory, and somatosensory cortices (A-C), consistent with MRI studies of cortical thickness, thickness increased with geodesic distance from the primary sensory areas. These trends were also present in the von Economo data set, where statistical power was limited by the small number of samples. For the motor cortex (D), a negative relationship was present with thickness decreasing from the primary motor cortex into the frontal cortex in the BigBrain data set and von Economo. This structural gradient is the inverse of the pattern of myelination and of previously reported MRI frontal thickness gradients but consistent with patterns of structural type and neuronal density. These findings suggest the presence of distinct but overlapping structural hierarchies. Underlying data available from S1 Data and ftp://bigbrain.loris.ca.

Fig 6. Gradients of cortical and laminar thickness against geodesic distance from primary areas.

(A) Motor-frontal gradients show an inverse relationship from sensory gradients on both cortical and laminar thicknesses. Increasing sensory cortical thickness gradients were generally driven by thickness increases in layers III, V, and VI. By contrast, motor-frontal cortical thickness gradients exhibited decreases in thickness of the same layers. (B) The same trends were evident in the von Economo data set; however, because of the small number of recorded samples, the confidence intervals were larger and generally included zero. (C) Typical neuronal types and morphologies of individual cortical layers. Cortical thickness gradients in either direction are primarily driven by changes in pyramidal cell layers (in layers III, V, and VI). (D) Layer thicknesses averaged across vertices in a sliding window of geodesic distance values from the primary area for the visual, somatosensory, auditory, and motor systems. The motor cortex exhibits the inverse pattern of change to those observed in sensory gradients. (E) Single-cell morphological studies of pyramidal neurons in macaque sensory processing pathways reveal increasing dendritic arborization [34] consistent with the hypothesis that laminar volume changes and ultimately thickness changes represent increases in intracortical connectivity. Underlying data available from S1 Data and ftp://bigbrain.loris.ca.