Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0

Szalkai, Balázs; Kerepesi, Csaba; Varga, Bálint; Grolmusz, Vince

doi:10.1007/s11571-016-9407-z

Cited by 56 publications

(67 citation statements)

References 7 publications

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“…In the articles [10,8] we have described the Budapest Reference Connectome Server https://connectome.pitgroup.org, which generates parameter-ized consensus connectomes from hundreds of braingraphs. The Budapest Reference Connectome Server led us to the discovery of the phenomenon of the Consensus Connectome Dynamics [14], and consequently, to a novel method for directing the edges of the human connectomes, gained from diffusion weighted MR imaging data [7,9,15].…”

Section: Previous Workmentioning

confidence: 99%

The frequent subgraphs of the connectome of the human brain

2019

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In mapping the human structural connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (i) discovery that women's connectomes have deeper and richer connectivity-related graph parameters like those of men, or (ii) the description of more and less conservatively connected lobes and cerebral regions, and (iii) the discovery of the phenomenon of the Consensus Connectome Dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and functions. The present contribution describes the frequent connected subgraphs (instead of sub-circuits) of at most 6 edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected sub-graphs that are more frequent in female or the male connectome. While our results describe subgraphs, instead of sub-circuits, we need to note that all macroscopic sub-circuits correspond to an underlying connected subgraph. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.

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Section: Previous Workmentioning

confidence: 99%

The frequent subgraphs of the connectome of the human brain

2019

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“…This approach will not consider rarely appearing errors, since if we deal with substructures, which appear with a minimum frequency of 80% or 90%, then the infrequent errors will be filtered out. The Budapest Reference Connectome Server generates the kfrequent edges [12,13]. In the work [29] we have mapped the frequently appearing subgraphs of the human connectome.…”

Section: Robust Methodsmentioning

confidence: 99%

“…We have computed hundreds of braingraphs [5], and prepared the Budapest Reference Connectome Server, which generates the graph of k-frequent edges of the human connectome of n=477 people, where 1 ≤ k ≤ n, and the k-frequent edges are those, which are present in at least k braingraphs out of the n=477. The parameter k is selectable, along with other parameters at the webserver https://pitgroup.org/connectome/, and the resulting consensus graph can be visualized and downloaded from the site [12,13].…”

Section: The Graph-theoretical Analysis Of the Braingraphmentioning

confidence: 99%

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

Fellner

Varga

Grolmusz³

2019

2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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The human connectome has become the very frequent subject of study of brain-scientists, psychologists, and imaging experts in the last decade. With diffusion magnetic resonance imaging techniques, unified with advanced data processing algorithms, today we are able to compute braingraphs with several hundred, anatomically identified nodes and thousands of edges, corresponding to the anatomical connections of the brain. The analysis of these graphs without refined mathematical tools is hopeless. These tools need to address the high error rate of the MRI processing workflow, and need to find structural causes or at least correlations of psychological properties and cerebral connections. Until now, structural connectomics was only rarely able identifying such causes or correlations. In the present work, we study the frequent neighbor sets of the most deeply investigated brain area, the hippocampus. By applying the Frequent Network Neighborhood mapping method, we identified frequent neighbor-sets of the hippocampus, which may influence numerous psychological parameters, including intelligence-related ones. We have found neighbor sets, which have significantly higher frequency in subjects with high-scored Penn Matrix tests, and with low-scored Penn Word Memory tests. Our study utilizes the braingraphs, computed from the imaging data of the Human Connectome Project's 414 subjects, each with 463 anatomically identified nodes.

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“…If there were no any variability among the connectomes of the individual subjects, then in each connectome, the left hippocampus would be connected to the very same set of other nodes or ROIs. However, there is a considerable variability of these connections between distinct subjects [19,20,21]. Therefore, no such common neighbor-set exists for any vertex in the braingraphs.…”

Section: Our Contribution: the Frequent Network Neighborhood Mappingmentioning

confidence: 99%

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

2020

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In the study of the human connectome, the vertices and the edges of the network of the human brain are analyzed: the vertices of the graphs are the anatomically identified gray matter areas of the subjects; this set is exactly the same for all the subjects. The edges of the graphs correspond to the axonal fibers, connecting these areas. In the biological applications of graph theory, it happens very rarely that scientists examine numerous large graphs on the very same, labeled vertex set. Exactly this is the case in the study of the connectomes. Because of the particularity of these sets of graphs, novel, robust methods need to be developed for their analysis. Here we introduce 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. We apply the novel method for mapping the neighborhoods of the human hippocampus and discover strong statistical asymmetries between the connectomes of the sexes, computed from the Human Connectome Project. We analyze 413 braingraphs, each with 463 nodes. We show that the hippocampi of men have much more significantly frequent neighbor sets than women; therefore, in a sense, the connections of the hippocampi are more regularly distributed in men and more varied in women. Our results are in contrast to the volumetric studies of the human hippocampus, where it was shown that the relative volume of the hippocampus is the same in men and women.

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Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0

Cited by 56 publications

References 7 publications

The frequent subgraphs of the connectome of the human brain

The frequent subgraphs of the connectome of the human brain

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

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

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