nilearn.connectome: Functional Connectivity

Tools for computing functional connectivity matrices and also implementation of algorithm for sparse multi subjects learning of Gaussian graphical models.

Classes

ConnectivityMeasure([cov_estimator, kind, ...])	A class that computes different kinds of functional connectivity matrices.
GroupSparseCovariance([alpha, tol, ...])	Covariance and precision matrix estimator.
GroupSparseCovarianceCV([alphas, ...])	Sparse inverse covariance w/ cross-validated choice of the parameter.

        classDiagram
  BaseEstimator <|-- ConnectivityMeasure
  BaseEstimator <|-- GroupSparseCovariance
  BaseEstimator <|-- GroupSparseCovarianceCV
  CacheMixin <|-- GroupSparseCovariance
  CacheMixin <|-- GroupSparseCovarianceCV
  TransformerMixin <|-- ConnectivityMeasure
  _HTMLDocumentationLinkMixin <|-- BaseEstimator
  _MetadataRequester <|-- BaseEstimator
  _SetOutputMixin <|-- TransformerMixin
    

Functions

sym_matrix_to_vec(symmetric[, discard_diagonal])	Return the flattened lower triangular part of an array.
vec_to_sym_matrix(vec[, diagonal])	Return the symmetric matrix given its flattened lower triangular part.
group_sparse_covariance(subjects, alpha[, ...])	Compute sparse precision matrices and covariance matrices.
cov_to_corr(covariance)	Return correlation matrix for a given covariance matrix.
prec_to_partial(precision)	Return partial correlation matrix for a given precision matrix.