Figures and data
Two architectural gradients scaffold human visual cortex.
(a) Principal component analysis on the concatenated cortical thickness and myelin/neurite density maps from all participants in HCP-YA to extract architectural gradients of human visual cortex produced a collection of orthogonal principal components, consisting of spatial maps (i.e., score) and individual weights (i.e., loading) in pairs. (b) The explained variance ratio of the top 5 principal components (PCs). The first two PCs (i.e., gradients 1 and 2) dominate the explainable variance. (c) Contributions of the two architectural measures (thickness and density) to the two gradients. (d) Topographic patterns of the two gradients on a flattened cortical surface. Gradient 1 (PC1) displays roughly monotonic change from negative to positive scores across visual cortex, emanating from primary visual cortex V1, while gradient 2 (PC2) showed repeated representation in four localities, mirroring the four visual streams (early, dorsal, lateral, ventral). Black dotted lines: borders where the different visual streams meet, defined using HCP-MMP label boundaries. A.U. is arbitrary units. (e) The convergence and divergence between the group average map of thickness and tissue density. (f) Histogram depicting gradient scores in the four visual stream regions. Gradient 1 is a global gradient increasing from early to ventral, for example. Gradient 2 is a local gradient sampled evenly within an individual visual stream. (g) The dependence of gradient value differences on geodesic distance are different for the two gradients. Gradient 1 shows larger changes across vertices separated by a long distance, whereas gradient 2 shows larger changes for short distances. (h) Geometric models of the two architectural gradients, which were constructed using the geodesic distance of each vertex of visual cortex to specific anatomical landmarks as anchors. The calcarine sulcus and eight local minima of gradient 2 were used as anchors to model gradient 1 and 2, respectively.
The functional and microstructural properties of the two architectural gradients.
(a) Gradient 1 was highly correlated with the pRF size (r=0.66), while the gradient 2 was not (r=0.03). (b) Gradient 1 was perfectly correlated with the hierarchical rank of the 10 visual areas within the ventral visual stream (Spearman rank ρ=1.00), while gradient 2 was not. (c) The functional significance of the architectural gradients was evaluated by measuring to what extent each gradient is related to areal differentiation of the visual cortex (left) and the global functional organization measured by fractional amplitude of low-frequency fluctuation (fALFF) from resting-state fMRI (right). The combination of gradients 1 and 2 greatly improved the predictive power of classifying visual areas compared to using either gradient alone. However, gradient 1 contributes more than gradient 2 in predicting the global-scale functional organization (i.e., fALFF map). (d) Cell body density from the BigBrain dataset is quantified for each cortical layer at each vertex and correlated with each gradient map. Gradient 1 was mainly correlated with cell body density in Layers III and IV, while gradient 2 was mainly correlated with cell body density in Layer I. (e) The architectural gradient 2 predicts the presence of novel visual field maps in the anterior temporal lobe. Left: example participant with the pRF eccentricity map displayed on the inflated cortical surface. The highlighted region (white dotted line) is the subject of the zoomed insets on the right. The black outline delineates the anatomical region from which pRF data was extracted. The putative visual field map cluster is outlined on the insets, showing a radial eccentricity representation, and a perpendicular representation of polar angle travelling roughly medio-anterior to latero-posterior as indicated by the white arrow. (f) Illustration on an inflated cortical surface illustrating that the AT-cluster of retinotopic maps is located near the anterior intersection of the occipitotemporal (OTS) and collateral sulci (CoS). The AT-cluster field maps demonstrate perpendicular representations of pRF eccentricity and polar angle. (g) In all 12 participants, pRF size and eccentricity from all above-threshold vertices within the anatomically defined region (black solid line from panel e) are extracted, binned by eccentricity, averaged across participants, and then lines-of-best fit are modeled across the averaged data. Shaded regions represent bootstrapped 68% confidence intervals.
The relevance of the architectural gradients to visual behavior, development, and degeneration across the lifespan.
(a) Canonical correlation analysis (CCA) was used to associate multiple vision-related tasks with the two weight-vectors of each architectural gradient in the HCP-YA. CCA finds the linear combination of variables that best associate measures from the two data domains across participants. (b) Both architectural gradients can significantly predict individual visual ability. However, gradient 2 showed stronger correlation with visual behaviors than gradient 1. (c) The normalized magnitude of behavioral factor weights from CCA indicate that gradient 1 was correlated more with low-level visual abilities, while gradient 2 was correlated more with higher-level visual abilities. (d) Sliding window spatial PCA was performed across the lifespan (PCA on a given age-bin results in “lifespan component”, LC) to compare how the patterns of gradient 1 and 2 change with the age. The top two LCs (i.e., spatial maps) extracted from each age window correlate strongly with their respective component from the HCP-YA, confirming that the architectural gradients can be observed across the lifespan. (e) Correlations between LCs and PCs reveal that gradient 2 shows more development and degeneration across childhood and aging, compared to gradient 1.
