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=0)[source]¶
Report the proportion of active voxels for all clusters defined by the input threshold.
This implements the method described in Rosenblatt et al.[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, default=3.0
Cluster-forming threshold in z-scale.
- alphafloat or list, default=0.05
Level of control on the true positive rate, aka true discovery proportion.
- verboseint or bool, default=0
Verbosity mode.
- Returns:
- proportion_true_discoveries_imgNifti1Image
The statistical map that gives the true positive.
References
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