The Budapest Reference Connectome Server v2.0

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

doi:10.1016/j.neulet.2015.03.071

Cited by 43 publications

(60 citation statements)

References 8 publications

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“…2); versions 1.0 and 2.0 (that are also available at http://con nectome.pitgroup.org) were described in Szalkai et al (2015).…”

Section: Resultsmentioning

confidence: 99%

“…Version 1.0 of the Budapest Reference Connectome Server was prepared from six connectomes of five subjects, based on the data published in Hagmann et al (2008). Version 2.0 of the webserver (Szalkai et al 2015) was compiled from 96 connectomes, computed from the Human Connectome Project's (McNab et al 2013) 500-subjects release. We have reported a surprising and unforeseen discovery, found by changing the parameters of the version 2.0 of the webserver in Kerepesi et al (2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the presence or the absence of the edges, connecting the very same two anatomic regions, among multiple cortices. Previously, we have constructed the consensus braingraphs on 1015 vertices first in five, then in 96 subjects in the Budapest Reference Connectome Server v1.0 and v2.0, respectively. Here we report the construction of the version 3.0 of the server, generating the common edges of the connectomes of variously parameterizable subsets of the 1015-vertex connectomes of 477 subjects of the Human Connectome Project's 500-subject release. The consensus connectomes are downloadable in CSV and GraphML formats, and they are also visualized on the server's page. The consensus connectomes of the server can be considered as the ''average, healthy'' human connectome since all of their connections are present in at least k subjects, where the default value of k ¼ 209, but it can also be modified freely at the web server. The webserver is available at http://con nectome.pitgroup.org.

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“…2); versions 1.0 and 2.0 (that are also available at http://con nectome.pitgroup.org) were described in Szalkai et al (2015).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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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“…We have constructed the Budapest Reference Connectome Server [20,21] at the address https://pitgroup.org/connectome/, which is capable of generating consensus connectomes from the data of 477 subjects, consisting of kfrequent edges (i.e., edges that are present in at least k braingraphs), with user-selected k and other parameters.…”

Section: Previous Workmentioning

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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The Budapest Reference Connectome Server v2.0

Cited by 43 publications

References 8 publications

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

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

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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