Brain size bias compensated graph-theoretical parameters are also better in women’s structural connectomes
@article{Szalkai2017BrainSB, title={Brain size bias compensated graph-theoretical parameters are also better in women’s structural connectomes}, author={Bal{\'a}zs Szalkai and B{\'a}lint Varga and Vince Grolmusz}, journal={Brain Imaging and Behavior}, year={2017}, volume={12}, pages={663-673} }
In our previous study it was shown that the female connectomes have significantly better, deep graphtheoretical parameters, related to superior “connectivity”, than the connectome of the males. Since the average female brain is smaller than the average male brain, one cannot rule out that the significant advantages are due to the sizeand not to the sex-differences in the data. To filter out the possible brain-volume related artifacts, we have chosen 36 small male and 36 large female brains such…
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Comparing numerous graph parameters in the three largest lobes—frontal, parietal, temporal—and in both hemispheres of the human brain is compared, and some findings are presented, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the leftParietal lobe.
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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.
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Based on the data of the NIH-funded Human Connectome Project, structural connectomes of 426 human subjects are computed in five different resolutions of 83, 129, 234, 463 and 1015 nodes and several edge weights in anatomically annotated GraphML format that facilitates better further processing and visualization.
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- 2020
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- 2018
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The frequent subgraphs of the connectome of the human brain
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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.
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