A blueprint of mammalian cortical connectomes

Abstract

The cerebral cortex of mammals exhibits intricate interareal wiring. Moreover, mammalian cortices differ vastly in size, cytological composition, and phylogenetic distance. Given such complexity and pronounced species differences, it is a considerable challenge to decipher organizational principles of mammalian connectomes. Here, we demonstrate species-specific and species-general unifying principles linking the physical, cytological, and connectional dimensions of architecture in the mouse, cat, marmoset, and macaque monkey. The existence of connections is related to the cytology of cortical areas, in addition to the role of physical distance, but this relation is attenuated in mice and marmoset monkeys. The cytoarchitectonic cortical gradients, and not the rostrocaudal axis of the cortex, are closely linked to the laminar origin of connections, a principle that allows the extrapolation of this connectional feature to humans. Lastly, a network core, with a central role under different modes of network communication, characterizes all cortical connectomes. We observe a displacement of the network core in mammals, with a shift of the core of cats and macaque monkeys toward the less neuronally dense areas of the cerebral cortex. This displacement has functional ramifications but also entails a potential increased degree of vulnerability to pathology. In sum, our results sketch out a blueprint of mammalian connectomes consisting of species-specific and species-general links between the connectional, physical, and cytological dimensions of the cerebral cortex, possibly reflecting variations and persistence of evolutionarily conserved mechanisms and cellular phenomena. Our framework elucidates organizational principles that encompass but also extend beyond the wiring economy principle imposed by the physical embedding of the cerebral cortex.

Fig 1. Mammalian cerebral cortices.

(A) Mouse, (B) cat, (C) macaque monkey, and (D) marmoset monkey cortex. Cortical areas are shown with their respective cytoarchitectonic status dictated by cortical types (mouse and cat) or neuronal density per mm³ (marmoset and macaque monkey). Cortical types define an ordinal scale from cytoarchitectonically less differentiated areas, which correspond to overall less neuronally dense areas (lower cortical types), to cytoarchitectonically more differentiated areas, which correspond to overall more neuronally dense areas (higher cortical types). Note that there is no one-to-one correspondence of the cortical types for the mouse and cat cortex. Each scale denotes degrees of cytoarchitectonic differentiation within each species. (E) Illustration of cortex size differences and phylogenetic relations of the examined species. See S1 Table for full names of the cortical areas. max, maximum; min, minimum.

Fig 2. Existence of connections in relation to physical distance and cytoarchitectonic similarity of cortical areas.

The existence of connections is reflected in the cytoarchitectonic similarity of cortical areas and the physical distance between them, as is evident in the density plots of each axis. Present connections span short distances and link cytoarchitectonically similar areas, whereas the opposite holds for absent connections. However, for the marmoset monkey, conjoint multivariate examination of cytoarchitectonic similarity and physical distance shows a lack of statistical significance between cytoarchitectonic similarity and existence of connections (S2 Fig), thus pointing out a species-species manifestation of the relation between cytoarchitecture and existence of connections. Depicted cytoarchitectonic similarity and physical distance values are the result of a linear rescaling to the 0–1 interval. Note that for the cat cortex, both physical distance and cytoarchitectonic similarity are ordinal scales, and thus the frequency of presence or absence of connections for each pair of the ordinal values is depicted. Note as well that for the cat cortex, data points correspond only to connections with known status (present or absent) [30]; hence, no data points for certain physical distance and cytoarchitectonic similarity combinations are depicted. max, maximum; min, minimum.

Fig 3. Cytoarchitectonic similarity relates to the existence of connections in a species-specific manner.

(A) Increasing cytoarchitectonic dissimilarity of cortical areas entails a decrease in the probability of the existence of a connection. This decrease is more pronounced for the cat when compared to the mouse, as indicated by the larger probability decrease (shaded areas) for the same increase of cytoarchitectonic dissimilarity. (B) Same relation as in (A), but for the comparison of mouse versus macaque monkey. The decrease of the probability of the existence of a connection is more pronounced for the macaque monkey when compared to the mouse. (C) Same relation as in (A), but for the comparison of cat versus macaque monkey. In this comparison, no species-specific differences of the effect of cytoarchitectonic similarity on the probability of connections was observed.

Fig 4. Predictions of laminar origin of connections in the macaque monkey.

(A) Quantitative data of laminar origin of connections (NSG%) across areas of the macaque monkey cortex [45]. (B) Predictions of NSG% based on cytoarchitecture, distance of areas along the rostrocaudal axis, and the combination of these predictors. All predictions were statistically significant. Cytoarchitecture-based predictions led to higher correlation between actual and predicted NSG% values compared to rostrocaudal distance–based predictions (p < 0.001, permutation test). A combination of cytoarchitecture and rostrocaudal distance did not lead to a higher correlation between actual and predicted NSG% values compared to the use of cytoarchitecture alone (p > 0.1, permutation test). Thus, rostrocaudal distance did not carry additional information on NSG% values. Boxplot edges, gray lines, and whiskers and crosses depict, the 25th and 75th percentiles, median, and extreme nonoutlier and outlier values, respectively. (C) Scatterplots of actual and predicted NSG% values based on cytoarchitecture, rostrocaudal distance of areas, and the combination of these predictors. For visualization purposes, predicted NSG% values are averaged across 100 predictions. See S1 Table for full names of the cortical areas. NSG%, percentage of supragranular labeled neurons.

Fig 5. Predictions of laminar origin of connections in the cat.

(A) Quantitative data of laminar origin of connections (NSG%) after retrograde injections in areas of the visual system [46]. (B) Same as in Fig 4B. (C) Same as in Fig 4C. See S1 Table for full names of the cortical areas. NSG%, percentage of supragranular labeled neurons.

Fig 6. Prediction of the laminar origin of connections for the human cortex.

(A) Cortical regions of the human cortex based on the Desikan-Killiany atlas [48]. Cortical regions are assigned to cortical areas S2 Table for which quantitative cell density measurements are available from the classic cytoarchitectonic map of von Economo and Koskinas [37]. The cell density of each region is the average cell density of the assigned cortical areas. (B) A cytoarchitecture-based model that was built with macaque monkey data was used to predict quantitative laminar origin values (NSG%) of putative connections in the human cortex. Such information cannot be obtained with current in vivo techniques but is essential for addressing structure–function relations in the human cortex, such as relating laminar origin of connections to interareal functional communication in different frequency channels [47]. Area-to-area NSG% predictions are not symmetric and are depicted for all pairs of areas, irrespective of the evidence for the existence of a connection in between them. C, caudal; D, dorsal; max, maximum; min, minimum; NSG%, percentage of supragranular labeled neurons; R, rostral; V, ventral.

Fig 7. Core–periphery network topology and cytoarchitecture.

The structural network core of the (A) mouse, (B) marmoset monkey, (C) cat, and (D) macaque monkey. Areas of the structural network core of the mouse and marmoset monkey do not exhibit statistically significant cytoarchitectonic differences with the areas of the periphery. In contrast, in the cat and macaque monkey, areas of the core differ significantly from areas of the periphery, with core areas exhibiting lower cortical types and neuronal density, compared to periphery areas. Boxplot edges, gray lines, and whiskers and crosses depict the 25th and 75th percentiles, median, and extreme nonoutlier and outlier values, respectively. Differences of the distributions of the core and periphery values were assessed with the Kolmogorov-Smirnov or the statistical energy test, and statistical significance was assessed with permutation tests. Note that areas colored in gray were not part of the core–periphery analyses because of a lack of data. For visualization purposes, the cytoarchitectonic status of cortical areas (cortical type or neuronal density) was linearly rescaled to the 0–1 interval. For the mouse and macaque monkey core, see also [20, 21]. See S1 Table for full names of the cortical areas. C, caudal; D, dorsal; D/M, dorsal/medial; R, rostral; V, ventral; V/L, ventral/lateral.

Fig 8. Core–periphery topology and network efficiency.

Core areas of the (A) mouse, (B) marmoset monkey, (C) cat, and (D) macaque monkey connectome exhibit higher incoming efficiency than the periphery areas. Higher incoming efficiency is observed for the core under two different modes of network communication—that is, when a shortest path (efficiency) or random walk (diffusion efficiency) mode of communication is assumed. Thus, under both modes of network communication, core areas can be reached faster than the periphery areas. The core areas also exhibit higher outgoing efficiency for the shortest path, but not for the random walk, mode of communication. Thus, for fast communication with other cortical areas, the structural core must adhere to a mode of communication that is geared toward shortest paths. Boxplot edges, lines, and whiskers and crosses depict the 25th and 75th percentiles, median, and extreme nonoutlier and outlier values, respectively. Differences of the distribution of efficiency values for the core and periphery areas were assessed with the Kolmogorov-Smirnov test, and statistical significance was assessed with permutation tests. Note that for visualization purposes, efficiency values were linearly rescaled to the 0–1 interval.

Fig 9. Putative neurodevelopmental mechanisms underlying the observed preferential connectivity between cytoarchitectonically similar cortical areas.

(A) The cytoarchitecture of areas in the adult cerebral cortex might reflect their distinct time courses in neurogenesis. Heterochronous and spatially ordered neurogenetic gradients indicate distinct time windows in neurogenesis in the mouse, with arrows denoting the direction of propagation of neuron release and accumulation [53]. Hence, similar cytoarchitecture might entail a similar time course of neurogenesis, thus biasing the cortical connections to form primarily between areas with similar overlapping time windows, since they host more neurons functioning as probable “connection partners.” (B) The duration of neurogenesis is shorter in mice compared to macaque monkeys [54]. Overall, a shorter neurogenetic period and less distinct time windows of neurogenesis may result in the observed species-specific relation of the existence of connections and the cytoarchitecture of the cerebral cortex.

Fig 10. Unifying principles of mammalian connectomes and their common and diverse manifestation across species.

(A) Cytoarchitectonic gradients of the cerebral cortex and their relation to fundamental interareal connectome features (existence and laminar origin of connections) and global network topology (core–periphery). (B) The relation of cytoarchitecture and connectome features is manifested in a species-specific manner, distinguishing the mouse and the marmoset monkey from the cat and macaque monkey. The direction of the arrow denotes pronounced correspondence of cytoarchitectonic similarity and existence of connections, pronounced shifts of the laminar origin of connections across the cortical sheet, and neuronal sparsification of the structural core, resulting in the segregation of the cytology of core and periphery areas. IG, infragranular; SG, supragranular.