Identifying super-feminine, super-masculine and sex-defining connections in the human braingraph

Abstract

For more than a decade now, we can discover and study thousands of cerebral connections with the application of diffusion magnetic resonance imaging (dMRI) techniques and the accompanying algorithmic workflow. While numerous connectomical results were published enlightening the relation between the braingraph and certain biological, medical, and psychological properties, it is still a great challenge to identify a small number of brain connections closely related to those conditions. In the present contribution, by applying the 1200 Subjects Release of the Human Connectome Project (HCP) and Support Vector Machines, we identify just 102 connections out of the total number of 1950 connections in the 83-vertex graphs of 1064 subjects, which—by a simple linear test—precisely, without any error determine the sex of the subject. Next, we re-scaled the weights of the edges—corresponding to the discovered fibers—to be between 0 and 1, and, very surprisingly, we were able to identify two graph edges out of these 102, such that, if their weights are both 1, then the connectome always belongs to a female subject, independently of the other edges. Similarly, we have identified 3 edges from these 102, whose weights, if two of them are 1 and one is 0, imply that the graph belongs to a male subject—again, independently of the other edges. We call the former 2 edges superfeminine and the first two of the 3 edges supermasculine edges of the human connectome. Even more interestingly, the edge, connecting the right Pars Triangularis and the right Superior Parietal areas, is one of the 2 superfeminine edges, and it is also the third edge, accompanying the two supermasculine connections if its weight is 0; therefore, it is also a “switching” edge. Identifying such edge-sets of distinction is the unprecedented result of this work.

Robust circuitry-based scores of structural importance of human brain areas

“…Using the exact methods and deep algorithms and approaches of graph theory (e.g., integer programming algorithms for computing exact solutions of NP-hard graph problems in connectomes, like minimum vertex cover or minimum bı ´section width), we have discovered numerous connectomical properties, related to the human sex differences [21][22][23][24][25], early brain development [16,[26][27][28], different lobal structures and organizations [29][30][31], and frequent edge sets in the whole brain or only those which are adjacent to the hippocampus [32][33][34][35].…”

Succinct Amyloid and Non-Amyloid Patterns in Hexapeptides

“…In the case of linear Support Vector Machines [15], the decision mechanism is much more transparent, and one can exploit a highly correct SVM for gaining unprecedented scientific information in certain cases [19]. In the present contribution, we show a novel and original method for gaining sitespecific amyloid-forming properties of amino acids in hexapeptides and prepar-ing amyloid-forming and non-amyloid forming patterns for the succinct representation of the SVM prediction results for hundreds (cf.…”

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

“…In the case of linear Support Vector Machines, 15 the decision mechanism is much more transparent, and one can exploit a highly correct SVM for gaining unprecedented scientific information in certain cases. 19 In this Article, we show a novel and original method for gaining site-specific amyloid-forming properties of amino acids in hexapeptides and preparing amyloid-forming and nonamyloid forming patterns for the succinct representation of the SVM prediction results for hundreds (cf., Table S1 ) or even tens of thousands (cf., Table 2 ) of hexapeptides at the same time.…”