We have shown how multimodal MRI measurements of the human and non-human primate cortex can be used to estimate the morphometric similarity between cortical areas and the topological properties of the anatomical connectome of a single brain. This robust and replicable new method of brain structural network analysis allowed us to test (and affirm) three key biological hypotheses about the organization of individual mammalian cortical networks. As theoretically predicted, we found evidence that cortical areas connected by an edge in MSNs were cytoarchitectonically similar and axonally connected to each other and had high levels of co-expression of genes specialized for neuronal functions. These results substantiated the biological validity of MSNs compared with other MRI- or DWI-based estimates of the human connectome and motivated us to test (and affirm) a fourth hypothesis: that individual differences in IQ are related to individual differences in the hubness or nodal degree of cortical nodes in human brain anatomical networks.

Like most spatially embedded real-life networks, including other brain networks, MSNs had a complex topology (). MSNs were binary graphs with a small-world combination of high clustering and short path length, some high-degree hub nodes with many connections to the rest of the network, and a community structure comprising modules and a rich club. This suite of topological properties was robust to variation in species (human and macaque), the number of morphometric features measured by MRI at each regional node (5 or 10 human or 8 macaque MRI-derived parameters per region), and the contrast between two independent human volunteer samples (NSPN and NIH cohorts) differing in magnetic field strength for MRI (1.5 T and 3 T) and pre-processing steps like cortical parcellation.

This robust and replicable network phenotype or connectome, derived from morphometric similarity mapping, is qualitatively similar to connectomes previously described using comparable graph theoretical metrics in many other neuroimaging and neuroscience datasets. A well-rehearsed interpretation of the complex topology of connectome organization is in terms of its supposed advantages for sensory, motor, or cognitive function. Some topological features, such as clusters and modules, will favor segregated processing of specific channels of information, whereas other features, such as hubs and a rich club, will favor integrated processing of all information (). This influential hypothesis, linking the topology of the human connectome to the psychological capacities of the brain, has some experimental support. The evidence is strongest for the link between modular or clustered topologies and specialized psychological or information processing functions (). At all scales of connectomics, from micro-scale analysis of C. elegans and Drosophila to macro-scale analysis of human MRI data (), there is evidence for topological modules of spatially co-localized (neuronal or areal) nodes with specialized functions. The evidence is not yet so strong for the link between integrative topological features, such as hubs and a rich club, and global or generalized cognitive functions (we return to this point later).

Morphometric Similarity and Anatomical Similarity

von Economo and Koskinas (1925) von Economo C.F.

Koskinas G.N. Die cytoarchitektonik der hirnrinde des erwachsenen menschen. Goulas et al., 2016 Goulas A.

Werner R.

Beul S.F.

Saering D.

van den Heuvel M.

Triarhou L.C.

Hilgetag C.C. Cytoarchitectonic similarity is a wiring principle of the human connectome. Goulas et al., 2017 Goulas A.

Uylings H.B.

Hilgetag C.C. Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse. By aligning individual MSNs with the classical cytoarchitectonic atlas of, we demonstrated a close correspondence between MSN topology and this histological classification of cortical areas. Morphometric similarity, measured by MRI, was greater between regional nodes that were histologically similar in the sense of belonging to the same cytoarchitectonic class. This meant that sparse MSNs, representing only a small percentage of the highest morphometric similarity statistics, were dominated by intra-class edges between regions. Correspondence between morphometric similarity and cytoarchitectonic similarity is supportive of the biological validity of the constituent MRI measurements. There is also growing evidence that cytoarchitectonic similarity predicts axonal connectivity between cortical areas, with greater probability of axonal connectivity between histologically similar areas (). Thus, we reasoned that alignment of network edges with cytoarchitectonic classes could provide a triangulation point to compare MSNs with other MRI-based methods of human connectome mapping. Because histologically similar nodes are more likely to be axonally connected, then any map of anatomical connectivity derived from MRI should be dominated by intra-class edges.

We compared MSNs with two other MRI-based anatomical networks estimated from the same sample—a single structural covariance network and a set of individual diffusion tractography networks. All three networks had qualitatively and quantitatively similar complex topology, but they were not identical. In relation to the benchmark of cytoarchitectonic classification, all networks were dominated by a high percentage of intra-class edges when graphs were thresholded sparsely to include only the strongest connections between regions. However, across all connection densities considered, the percentage of intra-class edges was greater for MSNs than for the SCN or DWI networks. This indicates that edges in MSNs are more representative of histologically similar pairs of regions, which are more likely to be axonally connected to each other, than edges in the SCN or DWI networks. One reason for the relatively poor performance of DWI networks in aligning to cytoarchitectonic classes seems likely to be the known difficulties in reconstructing interhemispheric connections by tractography analysis of DWI data.

Bernard et al., 2012 Bernard A.

Lubbers L.S.

Tanis K.Q.

Luo R.

Podtelezhnikov A.A.

Finney E.M.

McWhorter M.M.

Serikawa K.

Lemon T.

Morgan R.

et al. Transcriptional architecture of the primate neocortex. Hawrylycz et al., 2012 Hawrylycz M.J.

Lein E.S.

Guillozet-Bongaarts A.L.

Shen E.H.

Ng L.

Miller J.A.

van de Lagemaat L.N.

Smith K.A.

Ebbert A.

Riley Z.L.

et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Fulcher and Fornito, 2016 Fulcher B.D.

Fornito A. A transcriptional signature of hub connectivity in the mouse connectome. Zeng et al., 2012 Zeng H.

Shen E.H.

Hohmann J.G.

Oh S.W.

Bernard A.

Royall J.J.

Glattfelder K.J.

Sunkin S.M.

Morris J.A.

Guillozet-Bongaarts A.L.

et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Hawrylycz et al., 2012 Hawrylycz M.J.

Lein E.S.

Guillozet-Bongaarts A.L.

Shen E.H.

Ng L.

Miller J.A.

van de Lagemaat L.N.

Smith K.A.

Ebbert A.

Riley Z.L.

et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Krienen et al., 2016 Krienen F.M.

Yeo B.T.

Ge T.

Buckner R.L.

Sherwood C.C. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain. Although cytoarchitectonic classification is a well-established and traditional way of assessing histological similarity between cortical areas, more generally we can assess inter-areal similarity in terms of any locally (spatially) expressed cellular or genomic phenotype. Spatial patterns of gene expression in the mammalian cortex are intimately tied to regional differences in cortical layering and cell composition (). Transcriptomic similarity or gene co-expression was greater between regions of the mouse brain that were known to be axonally connected by analysis of anterograde tract-tracing data (). A functionally specialized set of so-called HSE genes, which are overexpressed specifically in supragranular layers of the human association cortex () and known to be important for the formation of long-distance inter-areal axonal connectivity (), were more strongly co-expressed by functionally connected brain regions ().

In this context, we predicted that morphometrically similar regions should have high levels of gene co-expression in general as well as high levels of HSE gene co-expression in particular. Whole-genome analysis confirmed that co-expression was positively correlated with morphometric similarity, and the genes that contributed most strongly to the overall association between transcriptional and morphometric similarity were specialized for neuronal functions. HSE genes were most strongly expressed in cytoarchitectonic classes 1–3, and HSE gene co-expression was positively correlated with morphometric similarity and degree or hubness of MSN nodes.

Fulcher and Fornito, 2016 Fulcher B.D.

Fornito A. A transcriptional signature of hub connectivity in the mouse connectome. Goulas et al., 2016 Goulas A.

Werner R.

Beul S.F.

Saering D.

van den Heuvel M.

Triarhou L.C.

Hilgetag C.C. Cytoarchitectonic similarity is a wiring principle of the human connectome. Goulas et al., 2017 Goulas A.

Uylings H.B.

Hilgetag C.C. Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse. We have thus demonstrated that morphometric similarity is strongly associated with cytoarchitectonic and genomic measures of histological similarity between cortical areas. To the extent that histological (cytoarchitectonic or transcriptional) similarity is coupled to axonal connectivity between cortical areas (), we can therefore expect morphometric similarity measured by MRI to be at least an approximate marker of axonal connectivity. However, to verify this important interpretation more directly, we generalized the MSN approach to analysis of whole-brain connectomes in the macaque monkey.

Donahue et al., 2016 Donahue C.J.

Sotiropoulos S.N.

Jbabdi S.

Hernandez-Fernandez M.

Behrens T.E.

Dyrby T.B.

Coalson T.

Kennedy H.

Knoblauch K.

Van Essen D.C.

Glasser M.F. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. van den Heuvel et al., 2015 Van den Heuvel M.P.

de Reus M.A.

Feldman Barrett L.

Scholtens L.H.

Coopmans F.M.

Schmidt R.

Preuss T.M.

Rilling J.K.

Li L. Comparison of diffusion tractography and tract-tracing measures of connectivity strength in rhesus macaque connectome. We observed a strong positive relationship between the edge weights of the macaque MSN and the edge weights of the tract-tracing network, especially for the most consistently and strongly weighted edges in the individual macaque MSNs. The strength of association between tract-tracing and MSNs was comparable in magnitude with previous reports of correspondence between tract-tracing and DWI-based networks in the macaque (). We note that the macaque MRI data were collected at 3 T and provided only 8 morphometric variables per cortical region. It is predictable from the human MRI datasets we analyzed that MSN metrics (and their alignment with tract-tracing data) could be more precisely measured in future macaque MRI experiments at higher field strength or using multi-parameter MRI sequences to sample the cortical micro-structure more comprehensively. Moreover, given the strong inter-hemispheric relationships captured by the MSNs ( Figure 3 C), their alignment with tract-tracing connectivity is likely under-estimated because of the lack of available bilateral (inter-hemispheric) datasets.