Datasets and demos

Datasets

The following is a collection of example large scale cortical connectivity data sets. In these datasets nodes represent cortical areas and links represent large corticocortical tracts or functional associations.
  • Meta-analysis network of human whole-brain functional coactivations
    with comparable resting-state fMRI network and node coordinates.
  • Coactivation_matrix.matGroupAverage_rsfMRI_matrix.mat (WU networks).
    638 nodes, 18625 links
    Reference: Crossley et al. (2013).
    Contributor: NC.
  • Macaque cortical connectivity.
    macaque71.mat (BD network).
    71 nodes, 746 links.
    Reference: Young (1993).
    Contributor: OS.

  • Two versions of the macaque visual cortex.
    fve30.mat; fve32.mat (BD networks).
    fve30.mat: 30 nodes, 311 links.
    fve32.mat: 32 nodes, 320 links.
    Reference: Felleman and van Essen (1991).
    Contributor: OS.

  • Macaque large-scale visual and sensorimotor area
    corticocortical connectivity.
    macaque47.mat (BD network).
    47 nodes; 505 links.
    Used in e.g. Honey et al. (2007).
    Contributor: RK.

  • Connection matrices of cat cortex.
    cat.mat (WD networks).
    CIJall contains all cortical and thalamic areas: 95 nodes, 2126 links.
    CIJctx contains only 52 cortical areas: 52 nodes, 820 links.
    Reference: Scannell et al. (1999).
    Contributor: OS.

Demos

These demos illustrate the use of generative growth models and efficiency measures.
  • Generative growth models: These generative model functions generate a wide range of synthetic networks using a combination of spatial and topological growth rules, and evaluate the accuracy of correspondence between the input networks and the generated synthetic. The demo functions show the usage of these models, and require the associated demo_generative_models_data.mat file.

    demo_generative_models_geometric.m (BU networks);
    demo_generative_models_neighbors.m (BU networks);
    demo_generative_models_data.mat (BU networks).
    Contributor: RB.

  • Efficiency measures on synthetic datasets: This demo function demonstrates the computation of efficiency function on several synthetic datasets. This function requires the associated demo_efficiency_measures_data.mat file.

    demo_efficiency_measures.m (BU, WU networks);
    demo_efficiency_measures_data.mat (BU, WU networks).
    Contributors: JG, AA.