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.threshold_stats_img¶
- nilearn.glm.threshold_stats_img(stat_img=None, mask_img=None, alpha=0.001, threshold=3.0, height_control='fpr', cluster_threshold=0, two_sided=True)[source]¶
Compute the required threshold level and return the thresholded map.
- Parameters:
- stat_imgNiimg-like object or None, default=None
Statistical image (presumably in z scale) whenever height_control is ‘fpr’ or None, stat_img=None is acceptable. If it is ‘fdr’ or ‘bonferroni’, an error is raised if stat_img is None.
- mask_imgNiimg-like object, default=None
Mask image
- alpha
floatorlist, default=0.001 Number controlling the thresholding (either a p-value or q-value). Its actual meaning depends on the height_control parameter. This function translates alpha to a z-scale threshold.
- threshold
float, default=3.0 Desired threshold in z-scale. This is used only if height_control is None.
- height_control
str, or None optional, default=’fpr’ False positive control meaning of cluster forming threshold: None|’fpr’|’fdr’|’bonferroni’
- cluster_threshold
float, default=0 cluster size threshold. In the returned thresholded map, sets of connected voxels (clusters) with size smaller than this number will be removed.
- two_sided
bool, default=True Whether the thresholding should yield both positive and negative part of the maps. In that case, alpha is corrected by a factor of 2.
- Returns:
- thresholded_mapNifti1Image,
The stat_map thresholded at the prescribed voxel- and cluster-level.
- threshold
float The voxel-level threshold used actually.
See also
nilearn.image.threshold_imgApply an explicit voxel-level (and optionally cluster-level) threshold without correction.
Notes
If the input image is not z-scaled (i.e. some z-transformed statistic) the computed threshold is not rigorous and likely meaningless
Examples using nilearn.glm.threshold_stats_img¶
Intro to GLM Analysis: a single-run, single-subject fMRI dataset
