budapest_connectome — Budapest Reference Connectome 3.0

Description

Consensus human structural brain networks (connectomes) from the Budapest Reference Connectome Server v3.0, built by B. Szalkai, C. Kerepesi, B. Varga and V. Grolmusz from the diffusion MRI data of 477 healthy adult subjects of the Human Connectome Project. For each subject, a connectome was computed by tractography (with the Connectome Mapper Toolkit), in which nodes are 1,015 anatomical regions of the brain (1,000 cortical and 15 subcortical) and edges connect regions linked by tracked fibers (streamlines). The individual connectomes were then combined into a consensus graph, where an edge is present if it occurs in at least one of the subjects.

There are nine networks, depending on the subject population (all subjects, female or male only) and on the total number of fibers tracked per subject (20k, 200k or 1m), named <population>_<fibers>.

The networks have the following properties:

(Node) dn_name: the name of the region, prefixed by the hemisphere (e.g. rh.lateralorbitofrontal_9).
(Node) dn_fsname: the region name without the hemisphere prefix.
(Node) dn_hemisphere: the hemisphere (left or right).
(Node) dn_region: whether the region is cortical or subcortical.
(Edge) occurences: the number of subjects in whose connectome the edge is present.
(Edge) fiber_count_mean and fiber_count_median: the mean and median number of fibers connecting the two regions.
(Edge) fiber_length_mean and fiber_length_median: the mean and median average length of those fibers.
(Edge) fractional_anisotropy_mean and fractional_anisotropy_median: the mean and median average fractional anisotropy along the fibers.
(Edge) electrical_connectivity_mean and electrical_connectivity_median: the mean and median "electrical connectivity", i.e. the number of fibers divided by their average length. Means and medians are taken over the subjects in which the edge is present.

Tags

Biological Connectome Weighted

Added

2020-08-11

Citation

B. Szalkai, C. Kerepesi, B. Varga and V. Grolmusz, "Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0." Cognitive Neurodynamics 11, 113-116 (2017) 10.1007/s11571-016-9407-z Sci-Hub
B. Szalkai et al., "The Budapest Reference Connectome Server v2.0." Neuroscience Letters 595, 60-62 (2015) 10.1016/j.neulet.2015.03.071 Sci-Hub

Upstream URL OK

http://pitgroup.org/connectome/#manual

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
all_20k	1,015	71,604	141.09	71.68	179.75	50.94	0.03	0.56	5	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	1.4 MiB	2.1 MiB	2.1 MiB	1.6 MiB
all_200k	1,015	105,293	207.47	92.46	249.83	21.43	0.02	0.62	4	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	2.3 MiB	3.3 MiB	3.3 MiB	2.6 MiB
all_1m	1,015	121,755	239.91	100.24	282.59	16.06	0.01	0.64	4	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	2.8 MiB	4.0 MiB	4.1 MiB	3.1 MiB
female_20k	1,015	63,448	125.02	67.24	163.65	56.10	0.03	0.53	5	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	1.2 MiB	1.8 MiB	1.8 MiB	1.4 MiB
female_200k	1,015	96,857	190.85	89.91	234.30	23.01	0.01	0.60	4	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	2.1 MiB	3.0 MiB	3.0 MiB	2.3 MiB
female_1m	1,015	112,890	222.44	98.20	266.49	17.59	0.01	0.62	4	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	2.6 MiB	3.7 MiB	3.7 MiB	2.8 MiB
male_20k	1,015	54,480	107.35	57.54	140.71	71.76	0.04	0.52	5	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	1.1 MiB	1.5 MiB	1.6 MiB	1.2 MiB
male_200k	1,015	80,270	158.17	75.24	196.18	35.26	0.04	0.58	5	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	1.7 MiB	2.5 MiB	2.5 MiB	1.9 MiB
male_1m	1,015	93,708	184.65	83.05	223.99	25.78	0.03	0.60	4	1.00	Undirected	Unipartite	dn_fsname dn_hemisphere dn_name dn_region	fiber_count_mean fiber_length_mean fractional_anisotropy_mean electrical_connectivity_median fiber_count_median fiber_length_median fractional_anisotropy_median occurences	2.1 MiB	3.1 MiB	3.1 MiB	2.3 MiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.