Graph Theoretical Analysis Reveals: Women’s Brains Are Better Connected than Men’s

  title={Graph Theoretical Analysis Reveals: Women’s Brains Are Better Connected than Men’s},
  author={Bal{\'a}zs Szalkai and B{\'a}lint Varga and Vince Grolmusz},
  journal={PLoS ONE},
Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google’s PageRank and the subsequent rise of the most popular search engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists… 

The Graph of Our Mind

The male and female braingraphs graph-theoretically are analyzed and statistically significant differences in numerous parameters between the sexes are shown: the female braedraphs are better expanders, have more edges, larger bipartition widths, and larger vertex cover than the bras of the male subjects.

Discovering sex and age implicator edges in the human connectome

The frequent complete subgraphs in the human connectome

The mapping of the frequent complete subgraphs of the human brain networks gives robust substructures in the graph: if a subgraph is present in the 80% of the graphs, then, most probably, it could not be an artifact of the measurement or the data processing workflow.

The frequent subgraphs of the connectome of the human brain

The present contribution describes the frequent connected subgraphs of at most six edges in the human brain, and analyzes these frequent graphs and examines sex differences in these graphs: numerous connected sub graphs that are more frequent in female or male connectomes.

How to Direct the Edges of the Connectomes: Dynamics of the Consensus Connectomes and the Development of the Connections in the Human Brain

A surprising and unforeseen property of the Budapest Reference Connectome Server is recognized, and it is hypothesized that this movement of the slider in the webserver may copy the development of the connections in the human brain in the following sense: the connections that are present in all subjects are the oldest ones, and those that arepresent only in a decreasing fraction of the subjects are gradually the newer connections inThe individual brain development.

Good Neighbors, Bad Neighbors: The Frequent Network Neighborhood Mapping of the Hippocampus Enlightens Several Structural Factors of the Human Intelligence

  • M. FellnerBálint VargaV. Grolmusz
  • Computer Science
    2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
  • 2019
The frequent neighbor sets of the most deeply investigated brain area, the hippocampus are identified by applying the Frequent Network Neighborhood mapping method, which may influence numerous psychological parameters, including intelligence-related ones.

The Frequent Network Neighborhood Mapping of the human hippocampus shows much more frequent neighbor sets in males than in females

The new method of the Frequent Network Neighborhood Mapping for the connectome, which serves as a robust identification of the neighborhoods of given vertices of special interest in the graph, is introduced and strong statistical asymmetries between the connectomes of the sexes are discovered.

Functional Geometry of Human Connectome and Robustness of Gender Differences

A systematic analysis of consensus networks of 100 female and 100 male subjects by varying the number of fibres launched reveals the functional geometry of the common F\&M-connectome, which is characterized by a complex architecture of simplexes to the 14th order, and highlights the functional gender-related differences.

Good neighbors, bad neighbors: the frequent network neighborhood mapping of the hippocampus enlightens several structural factors of the human intelligence on a 414-subject cohort

This work uses the braingraphs, computed from the imaging data of the Human Connectome Project’s 414 subjects, each with 463 anatomically identified nodes, to identify frequent neighbor-sets of the hippocampus, which may influence numerous psychological parameters, including intelligence-related ones.

The Database with more than 1000 Robust Human Structural Connectomes in Five Resolutions.

This dataset makes possible the access to these graphs for scientists unfamiliar with neuroimaging- and connectome-related tools: mathematicians, physicists, and engineers can use their expertize and ideas in the analysis of the connections of the human brain.



Expander Graphs and their Applications

A major consideration we had in writing this survey was to make it accessible to mathematicians as well as to computer scientists, since expander graphs, the protagonists of our story, come up in

Rich-club organization of the newborn human brain

Though rich-club organization remains intact following premature birth, significant disruptions in both in cortical–subcortical connectivity and short-distance corticocortical connections are revealed.

The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

The Connectome Mapper is presented, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses.

Some Simplified NP-Complete Graph Problems

MR connectomics: a conceptual framework for studying the developing brain

This body of work is reviewed to identify current limitations of such approaches, what can be improved, and how these points generalize to other topics in connectome research.

Sex differences in the structural connectome of the human brain

Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes.

On the mis-presentation and misinterpretation of gender-related data: The case of Ingalhalikar’s human connectome study

The selective choice of data that forms the basis for Ingalhalikar et al.