Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the functional graph or network involves "thresholding" of the connectivity matrix, selecting the set of edges that together form the graph on which network organization is evaluated. To avoid systematic differences in absolute number of edges, studies have argued against the use of an "absolute threshold" in case-control studies and have proposed the use of "proportional thresholding" instead, in which a pre-defined number of strongest connections are selected as network edges, ensuring equal network density across datasets. Here, we systematically studied the effect of proportional thresholding on the construction of functional matrices and subsequent graph analysis in patient-control functional connectome studies. In a few simple experiments we show that differences in overall strength of functional connectivity (FC) - as often observed between patients and controls - can have predictable consequences for between-group differences in network organization. In individual networks with lower overall FC the proportional thresholding algorithm has to select more edges based on lower correlations, which have (on average) a higher probability of being spurious, and thus introduces a higher degree of randomness in the resulting network. We show across both empirical and artificial patient-control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences in network organization. Based on the presented case-control findings we inform about the caveats of proportional thresholding in patient-control studies in which groups show a between-group difference in overall FC. We make recommendations on how to examine, report and to take into account overall FC effects in future patient-control functional connectome studies.

Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution.

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The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

Macroscale white matter pathways are the infrastructure for large-scale communication in the human brain and a prerequisite for healthy brain function. Disruptions in the brain's connectivity architecture play an important role in many psychiatric and neurological brain disorders. Here we show that connections important for global communication and network integration are particularly vulnerable to brain alterations across multiple brain disorders. We report on a crossdisorder connectome study comprising in total 1,033 patients and 1,154 matched controls across eight psychiatric and four neurological disorders. We extracted disorder connectome fingerprints for each of these twelve disorders and combined them into a 'cross-disorder disconnectivity involvement map' describing the level of cross-disorder involvement of each white matter pathway of the human brain network. Network analysis revealed connections central to global network communication and integration to display high disturbance across disorders, suggesting a general cross-disorder involvement and importance of these pathways in normal function. Main The macroscale connectome is the brain's anatomical network for global communication and multimodal integration of information between brain areas 1. Topologically central connections have been argued to provide benefits for global neural integration 2 and healthy brain function 3. Due to 'motor' (r(217) = 0.21, p = 0.025, 95% CI = 0.09-0.34), as well as high-level functions such as 'cognitive control' (r(217) = 0.28, p < 0.001, 95% CI = 0.15-0.40), 'cued attention' (r(217) = 0.28, p < 0.001, 95% CI = 0.16-0.40) and 'visual attention' (r(217) = 0.25, p = 0.004, 95% CI = 0.12-0.37) (Bonferroni corrected for multiple testing across 24 functions, see Supplementary Figure 3 and for a complete list of functions Supplementary Table 1). Edge-wise centrality measures We further investigated the vulnerability of connections and their contribution to local and global communication in the brain network. The topological role of connections was assessed using four edge-wise centrality measures computed on a reference connectome that was based on highresolution data from the Human Connectome Project (HCP) 10. We used HCP data to ensure that the computation of network measures was performed independently from any patient-control effects and any of the included disorder datasets. The contribution of a connection in global communication across the network was measured by means of 'edge betweenness centrality', which assesses the number of shortest topological paths through each connection. Connections with high betweenness centrality (top 25%, n = 290) were found to be significantly more involved across disorders as contrasted to subject-label permuted cross-disorder involvement maps (d = 0.41, one-sided permutation testing, 10,000 permutations, p < 0.001, Figure 2, see Methods). In contrast, no significant effect was observed in connections with a low betweenness centrality (lowest 25%, n = 290, p = 1.000). We further exam...

60Macroscale white matter pathways form the infrastructure for large-scale communication in 61 the human brain, a prerequisite for healthy brain function. Conversely, disruptions in the 62 brain's connectivity architecture are thought to play an important role in a wide range of 63 psychiatric and neurological brain disorders. Here we show that especially connections 64 important for global communication and network integration are involved in a wide range of 65 brain disorders. We report on a meta-analytic connectome study comprising in total 895 66 patients and 1,016 controls across twelve neurological and psychiatric disorders. We 67 extracted disorder connectome fingerprints for each of these twelve disorders, which were 68 then combined into a cross-disorder disconnectivity involvement map, representing the 69 involvement of each brain pathway across brain disorders. Our findings show connections 70 central to the brain's infrastructure are disproportionally involved across a wide range of 71 disorders. Connections critical for global network communication and integration display 72 high disturbance across disorders, suggesting a general cross-disorder involvement and 73 importance of these pathways in normal function. Taken together, our cross-disorder study 74

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