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. 2020 Nov 4;9:e60673. doi: 10.7554/eLife.60673

Figure 1. Cytoarchitectural profiling of the mesiotemporal confluence.

(A) Isocortical and allocortical surfaces projected onto the 40 µm BigBrain volume. Notably, conventional isocortical surface construction skips over the allocortex. Purple = isocortex. Red = subiculum. Dark orange = CA1. Orange = CA2. Light orange = CA3. Yellow = CA4. (B) The iso-to-allocortical axis was estimated as the minimum geodesic distance to the intersection of the isocortical and hippocampal surface models. (C) Intensity sampling along 16 surfaces from the inner/pial to the outer/white surfaces produced microstructure profiles. Darker tones represent higher cellular density/soma size. (D) Above. The principle eigenvector/axis of cytoarchitectural differentiation, projected onto the confluent surface. Below. The association between the principle eigenvector and the iso-to-allocortical axis, with the optimal polynomial line of best fit. (E) Cytoarchitectural features generated for each microstructure profile. (F) Empirical vs predicted position on the iso-to-allocortical, based on supervised random forest regression with cross-validation. (G) Feature importance was approximated as how much the feature decreased variance in the random forest. (H) Cubic fit of each selected feature with the iso-to-allocortical axis. Feature values are z-standardised. (I) Line plots show the cubic fit of cytoarchitectural features to the iso-to-allocortical axis within 23 bins of the anterior-posterior axis (yellow-to-green). Neighbouring scatter plots depict the goodness of fit (adjusted R2) of each polynomial, showing high consistency of the pattern of skewness.

Figure 1.

Figure 1—figure supplement 1. Manifold learning of MTL cytoarchitecture and relation to subregions.

Figure 1—figure supplement 1.

(A) Surface projection and average microstructure profiles of MTL subregions, defined by the Desikan-Killany atlas for isocortex and manual segmentations for the hippocampus proper. (B) Microstructure profile covariance (MPC) across all vertices in the cortical confluence model was transformed into a normalised angle matrix and sorted according to the first eigenvector from diffusion map embedding. Scatterplot shows approximate variance explained by each eigenvector. (C) Second and third eigenvectors shown on the MTL confluence model. (D) Deviations from the polynomial association of the geometric iso-to-allocortical axis with the first eigenvector were estimated by the standardised residuals. Standardised residuals are shown on the surface projection and stratified by subregions.
Figure 1—figure supplement 2. Exemplar microstructure profiles of selected features, namely skewness, intensity at ~13% depth and ~53% depth.

Figure 1—figure supplement 2.

(A) Top: Standardised feature values of each vertex, sorted by value and coloured by position on the iso-toallocortical axis. Bottom: Average profiles of vertices within 20 bins, stratified by the respective feature value, which illustrate how features capture differences in the profile shape. Profiles are coloured by standardised feature value. Higher Merker-staining intensity values reflect higher cellular density/soma size. (B) Feature values of each vertex projected onto the MTL confluence model.