- data: numpy array, pandas dataframe, or a list of numpy arrays/dataframes
Each numpy array (or dataframe) should have size timepoints by features.
If a list of arrays are passed, there should be one array per subject.
- weights_function: a function of the form func(T, params) where
T is a non-negative integer specifying the number of timepoints to consider.
The function should return a T by T array containing the timepoint-specific
weights for each consecutive time point from 0 to T (not including T).
Default: gaussian_weights; options: laplace_weights, gaussian_weights,
t_weights, eye_weights, mexican_hat_weights
- weights_params: used to pass parameters to the weights_params function. This
can be specified in any format (e.g. a scalar, list, object, dictionary,
etc.).
Default: None (use default parameters for the given weights function).
Options: gaussian_params, laplace_params, t_params, eye_params,
mexican_hat_params.
- include_timepoints: determines which timepoints are used to estimate the correlations
at each timepoint. This is applied after the weights function to further constrain
which timepoints may be considered in computing the correlations at each timepoint.
Options: ‘all’ (default; include all timepoints), ‘pre’ (only include timepoints before
the given timepoint), ‘post’ (only include timepoints after the given timepoint).
- exclude_timepoints: additional option, used to filter out any timepoints less than x units
of the timepoint whose correlations are being estimated. For example, passing
exclude_timepoints=3 will exclude any timepoints 3 or more samples from the given timepoint.
When exclude timepoints is negative, it works inversely– e.g. exclude_timepoints=-5 will
exclude any timepoints within 5 or fewer samples of the given timepoint. Real-valued scalars
are supported but are rounded to the nearest Integer.
Default: None (no filtering).
- combine: a function applied to either a single matrix of vectorized correlation
matrices, or a list of such matrices. The function should return either
a numpy array or a list of numpy arrays.
Default: helpers.null_combine (a function that returns its input). Other
useful functions:
helpers.corrmean_combine: take the element-wise average correlations across matrices
helpers.tstat_combine: return element-wise t-statistics across matrices
- cfun: function to apply to the data array(s)
This function should be of the form
func(data, weights)
The function should support data as a numpy array or list of numpy
arrays. When a list of numpy arrays is passed, the function should
apply the “across subjects” version of the analysis. When a single
numpy array is passed, the function should apply the “within subjects”
version of the analysis.
weights is a numpy array with per-timepoint weights
The function should return a single numpy array with 1 row and an
arbitrary number of columns (the number of columns may be determined by
the function).
Default: A continuous verison of Inter-Subject Functional Connectivity
(Simony et al. 2017). If only one data array is passed (rather than a
list), the default cfun returns the moment-by-moment correlations for
that array. (Reference: http://www.nature.com/articles/ncomms12141)
- rfun: function to use for dimensionality reduction.
All hypertools and scikit-learn functions are supported: PCA, IncrementalPCA, SparsePCA,
MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD,
DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap,
SpectralEmbedding, LocallyLinearEmbedding, MDS, and UMAP.
Can be passed as a string, but for finer control of the model
parameters, pass as a dictionary, e.g.
reduction={‘model’ : ‘PCA’, ‘params’ : {‘whiten’ : True}}.
See scikit-learn specific model docs for details on parameters supported
for each model.
Another option is to use graph theoretic measures computed for each node.
The following measures are supported (via the brainconn toolbox):
eigenvector_centrality, pagerank_centrality, and strength. (Each
of these must be specified as a string; dictionaries not supported.)
Default: None (no dimensionality reduction)