spectralbrain.build_geometric_connectome#

spectralbrain.build_geometric_connectome(parcel_descriptors, *, method='wasserstein', **kwargs)[source]#

Build a ROI × ROI geometric connectome from parcel descriptors.

For each pair of parcels, computes the distance between their descriptor distributions.

Parameters:

  • parcel_descriptors (dict of {label: ndarray}) – Mapping from parcel label to descriptor array. Each value is shape (N_parcel, T) or (N_parcel,).

  • method (str) – Distance method (see descriptor_distance()).

  • **kwargs – Extra args for the distance function.

Returns:

  • matrix (ndarray, shape (R, R)) – Symmetric distance matrix.

  • labels (list) – Ordered parcel labels corresponding to matrix rows/columns.

Return type:

tuple[ndarray[tuple[Any, …], dtype[floating]], list]

Examples

>>> parcels = sb.io.apply_parcellation(verts, faces, labels)
>>> descs = {}
>>> for lab, (v, f) in parcels.items():
...     mesh = BrainMesh(v, f)
...     decomp = mesh.decompose(k=30)
...     descs[lab] = compute_hks(decomp, n_times=20)
>>> C, labs = build_geometric_connectome(descs, method="wasserstein")