Connectivity data

This page contains descriptions and examples to use Human Connectome Project (HCP) connectivity data.

Neuroimaging, particularly with functional and diffusion MRI, has become a leading tool to characterize human brain network organization in vivo and to identify network alterations in brain disorders. The ENIGMA TOOLBOX includes high-resolution structural (derived from diffusion-weighted tractography) and functional (derived from resting-state functional MRI) connectivity data from a cohort of unrelated healthy adults from the Human Connectome Project (HCP). Preprocessed cortico-cortical, subcortico-cortical, and subcortico-subcortical functional and structural connectivity matrices can be easily retrieved using the load_fc() and load_sc() functions.

High-resolution functional and structural data were parcellated according to the Desikan-Killiany, Glasser, as well as Schaefer 100, 200, 300, and 400 parcellations.

Normative functional connectivity matrices were generated by computing pairwise correlations between the time series of all cortical regions and subcortical (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus) regions; negative connections were set to zero. Subject-specific connectivity matrices were then z-transformed and aggregated across participants to construct a group-average functional connectome. Available cortico-cortical, subcortico-cortical, and subcortico-subcortical matrices are unthresholded.

Normative structural connectivity matrices were generated from preprocessed diffusion MRI data using MRtrix3. Anatomical constrained tractography was performed using different tissue types derived from the T1-weighted image, including cortical and subcortical gray matter, white matter, and cerebrospinal fluid. Multi-shell and multi-tissue response functions were estimated104 and constrained spherical deconvolution and intensity normalization were performed. The initial tractogram was generated with 40 million streamlines, with a maximum tract length of 250 and a fractional anisotropy cutoff of 0.06. Spherical-deconvolution informed filtering of tractograms (SIFT2) was applied to reconstruct whole-brain streamlines weighted by the cross-section multipliers. Reconstructed streamlines were mapped onto the 68 cortical and 14 subcortical (including hippocampus) regions to produce subject-specific structural connectivity matrices. The group-average normative structural connectome was defined using a distance-dependent thresholding procedure, which preserved the edge length distribution in individual patients, and was log transformed to reduce connectivity strength variance. As such, structural connectivity was defined by the number of streamlines between two regions (i.e., fiber density).

Additional details on subject inclusion and data preprocessing are provided in the ENIGMA TOOLBOX preprint.

One matrix 🎤

Are you looking for subcortico-subcortical connectivity? Or do you want cortical and subcortical connectivity in one matrix? If so, check out our functions: load_fc_as_one() here and load_sc_as_one() here!

Load cortical connectivity matrices

The ENIGMA TOOLBOX provides structural (diffusion MRI) and functional (resting-state functional MRI) connectivity matrices obtained from the HCP.

Python

Matlab

>>> from enigmatoolbox.datasets import load_sc, load_fc
>>> from nilearn import plotting

>>> # Load cortico-cortical functional connectivity data
>>> fc_ctx, fc_ctx_labels, _, _ = load_fc()

>>> # Load cortico-cortical structural connectivity data
>>> sc_ctx, sc_ctx_labels, _, _ = load_sc()

>>> # Plot cortico-cortical connectivity matrices
>>> fc_plot = plotting.plot_matrix(fc_ctx, figure=(9, 9), labels=fc_ctx_labels, vmax=0.8, vmin=0, cmap='Reds')
>>> sc_plot = plotting.plot_matrix(sc_ctx, figure=(9, 9), labels=sc_ctx_labels, vmax=10, vmin=0, cmap='Blues')

% Load cortico-cortical functional connectivity data
[fc_ctx, fc_ctx_labels, ~, ~] = load_fc();

% Load cortico-cortical structural connectivity data
[sc_ctx, sc_ctx_labels, ~, ~] = load_sc();

% Plot cortico-cortical connectivity matrices
f = figure,
  imagesc(fc_ctx, [0 0.8]);
  axis square;
  colormap(Reds);
  colorbar;
  set(gca, 'YTick', 1:1:length(fc_ctx_labels), ...
      'YTickLabel', fc_ctx_labels)

f = figure,
  imagesc(sc_ctx, [0 10]);
  axis square;
  colormap(Blues);
  colorbar;
  set(gca, 'YTick', 1:1:length(sc_ctx_labels), ...
      'YTickLabel', sc_ctx_labels)
../../_images/ctx_conn.png

Following the example below, we can also extract seed-based cortico-cortical connectivity, using the left middle temporal gyrus as example seed.

Python

Matlab

>>> from enigmatoolbox.utils.parcellation import parcel_to_surface
>>> from enigmatoolbox.plotting import plot_cortical

>>> # Extract seed-based connectivity
>>> seed = "L_middletemporal"
>>> seed_conn_fc = fc_ctx[[i for i, item in enumerate(fc_ctx_labels) if seed in item], ]
>>> seed_conn_sc = sc_ctx[[i for i, item in enumerate(sc_ctx_labels) if seed in item], ]

>>> # Map parcellated data to the surface
>>> seed_conn_fc_fsa5 = parcel_to_surface(seed_conn_fc, 'aparc_fsa5')
>>> seed_conn_sc_fsa5 = parcel_to_surface(seed_conn_sc, 'aparc_fsa5')

>>> # Project the results on the surface brain
>>> plot_cortical(array_name=seed_conn_fc_fsa5, surface_name="fsa5", size=(800, 400),
...               cmap='Reds', color_bar=True, color_range=(0.2, 0.7))

>>> plot_cortical(array_name=seed_conn_sc_fsa5, surface_name="fsa5", size=(800, 400),
...               cmap='Blues', color_bar=True, color_range=(2, 10))

% Extract seed-based connectivity
seed = 'L_middletemporal'
seed_conn_fc = fc_ctx(find(strcmp(fc_ctx_labels, seed)), :)
seed_conn_sc = sc_ctx(find(strcmp(sc_ctx_labels, seed)), :)

% Map parcellated data to the surface
seed_conn_fc_fsa5 = parcel_to_surface(seed_conn_fc, 'aparc_fsa5');
seed_conn_sc_fsa5 = parcel_to_surface(seed_conn_sc, 'aparc_fsa5');

% Project the results on the surface brain
f = figure,
    plot_cortical(seed_conn_fc_fsa5, 'cmap', 'Reds', 'color_range', [0.2 0.7])

f = figure,
    plot_cortical(seed_conn_sc_fsa5, 'cmap', 'Blues', 'color_range', [2 10])
../../_images/ctx_sbconn.png

Load subcortical connectivity matrices

Subcortico-cortical as well as subcortico-subcortical connectivity matrices are also included in the ENIGMA TOOLBOX. As above, we can load these structural and functional matrices and extract seed-based connectivity from subcortical seeds.

Python

Matlab

>>> from enigmatoolbox.datasets import load_sc, load_fc
>>> from nilearn import plotting

>>> # Load subcortico-cortical functional connectivity data
>>> _, _, fc_sctx, fc_sctx_labels = load_fc()

>>> # Load subcortico-cortical structural connectivity data
>>> _, _, sc_sctx, sc_sctx_labels = load_sc()

>>> # Plot subcortico-cortical connectivity matrices
>>> fc_plot = plotting.plot_matrix(fc_sctx, figure=(9, 9), labels=fc_sctx_labels, vmax=0.5, vmin=0, cmap='Reds')
>>> sc_plot = plotting.plot_matrix(sc_sctx, figure=(9, 9), labels=sc_sctx_labels, vmax=10, vmin=0, cmap='Blues')

% Load subcortico-cortical functional connectivity data
[~, ~, fc_sctx, fc_sctx_labels] = load_fc();

% Load subcortico-cortical structural connectivity data
[~, ~, sc_sctx, sc_sctx_labels] = load_sc();

% Plot subcortico-cortical connectivity matrices
f = figure,
  imagesc(fc_sctx, [0 0.5]);
  axis square;
  colormap(Reds);
  colorbar;
  set(gca, 'YTick', 1:1:length(fc_sctx_labels), ...
      'YTickLabel', fc_sctx_labels)

f = figure,
  imagesc(sc_sctx, [0 10]);
  axis square;
  colormap(Blues);
  colorbar;
  set(gca, 'YTick', 1:1:length(sc_sctx_labels), ...
      'YTickLabel', sc_sctx_labels)
../../_images/sctx_conn.png

As described above, we can also extract seed-based subcortico-cortical connectivity, using the left hippocampus as example seed.

Python

Matlab

>>> from enigmatoolbox.plotting import plot_cortical

>>> # Extract seed-based connectivity
>>> seed = "Lhippo"
>>> seed_conn_fc = fc_sctx[[i for i, item in enumerate(fc_sctx_labels) if seed in item],]
>>> seed_conn_sc = sc_sctx[[i for i, item in enumerate(sc_sctx_labels) if seed in item],]

>>> # Map parcellated data to the surface
>>> seed_conn_fc_fsa5 = parcel_to_surface(seed_conn_fc, 'aparc_fsa5')
>>> seed_conn_sc_fsa5 = parcel_to_surface(seed_conn_sc, 'aparc_fsa5')

>>> # Project the results on the surface brain
>>> plot_cortical(array_name=seed_conn_fc_fsa5, surface_name="fsa5", size=(800, 400),
...               cmap='Reds', color_bar=True, color_range=(0.1, 0.3))

>>> plot_cortical(array_name=seed_conn_sc_fsa5, surface_name="fsa5", size=(800, 400),
...               cmap='Blues', color_bar=True, color_range=(1, 10))

% Extract seed-based connectivity
seed = 'Lhippo'
seed_conn_fc = fc_sctx(find(strcmp(fc_sctx_labels, seed)), :)
seed_conn_sc = sc_sctx(find(strcmp(sc_sctx_labels, seed)), :)

% Map parcellated data to the surface
seed_conn_fc_fsa5 = parcel_to_surface(seed_conn_fc, 'aparc_fsa5');
seed_conn_sc_fsa5 = parcel_to_surface(seed_conn_sc, 'aparc_fsa5');

% Project the results on the surface brain
f = figure,
    plot_cortical(seed_conn_fc_fsa5, 'cmap', 'Reds', 'color_range', [0.1 0.3])

f = figure,
    plot_cortical(seed_conn_sc_fsa5, 'cmap', 'Blues', 'color_range', [1 10])
../../_images/sctx_sbconn.png