Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.glm.cluster_level_inference

nilearn.glm.cluster_level_inference(stat_img, mask_img=None, threshold=3.0, alpha=0.05, verbose=False)

Report the proportion of active voxels for all clusters defined by the input threshold.

This implements the method described in [1].

Parameters:

stat_imgNiimg-like object or None, optional

statistical image (presumably in z scale)

mask_imgNiimg-like object, optional,

mask image

thresholdlist of floats, optional

Cluster-forming threshold in z-scale. Default=3.0.

alphafloat or list, optional

Level of control on the true positive rate, aka true dsicovery proportion. Default=0.05.

verbosebool, optional

Verbosity mode. Default=False.

Returns:

proportion_true_discoveries_imgNifti1Image

The statistical map that gives the true positive.

References

[1]

Jonathan D. Rosenblatt, Livio Finos, Wouter D. Weeda, Aldo Solari, and Jelle J. Goeman. All-resolutions inference for brain imaging. NeuroImage, 181:786–796, November 2018. doi:10.1016/j.neuroimage.2018.07.060.

Examples using nilearn.glm.cluster_level_inference

Second-level fMRI model: true positive proportion in clusters

Second-level fMRI model: true positive proportion in clusters