xcp_d.interfaces.nilearn¶
Machine Learning module for NeuroImaging in python¶
Documentation is available in the docstrings and online at http://nilearn.github.io.
Contents¶
Nilearn aims at simplifying the use of the scikit-learn package in the context of neuroimaging. It provides specific input/output functions, algorithms and visualization tools.
Submodules¶
datasets — Utilities to download NeuroImaging datasets decoding — Decoding tools and algorithms decomposition — Includes a subject level variant of the ICA
algorithm called Canonical ICA
- connectome — Set of tools for computing functional connectivity matrices
and for sparse multi-subjects learning of Gaussian graphical models
- image — Set of functions defining mathematical operations
working on Niimg-like objects
maskers — Includes scikit-learn transformers. masking — Utilities to compute and operate on brain masks interfaces — Includes tools to preprocess neuro-imaging data
from various common interfaces like fMRIPrep.
- mass_univariate — Defines a Massively Univariate Linear Model
estimated with OLS and permutation test
plotting — Plotting code for nilearn region — Set of functions for extracting region-defined
signals, clustering methods, connected regions extraction
signal — Set of preprocessing functions for time series