mbfmri.utils.coef2map

mbfmri.utils.coef2map.reconstruct(array, mask)

reconstruct flattened array to 3D with the given mask.

Parameters

• array (numpy.ndarray) – array with shape of (N,) or (N,1)

• mask (numpy.ndarray) – 3D binary mask where sum(mask)==N

Returns

numpy.ndarray – Reconstructed 3D array

mbfmri.utils.coef2map.cluster_level_correction(brainmap, threshold, cluster_threshold)

thresholding and cluster-level correction

Parameters

• brainmap (nibabel.nifti1.Nifti1Image) – Nii image to be thresholded and cluster-level corrected.

• threshold (float) – Threshold value to cutoff the image Both negative and positie values will be zero if abs(v) <= threshold

• cluster_threshold (int) – Threshold for the number of points in a cluster to be cutoff

Returns

nibabel.nifti1.Nifti1Image – Thresholded and cluster-level corrected nii image.

mbfmri.utils.coef2map.get_map(coefs, voxel_mask, experiment_name, standardize=True, save_path='.', smoothing_fwhm=0, threshold=0, cluster_threshold=0)

make nii image file from coefficients of model.

Parameters

• coefs (list of numpy.array or numpy.array) – List of coefficients extracted from MVPA models

• voxel_mask (nibabel.nifti1.Nifti1Image) – Nii image of mask

• experiment_name (str) – Name of experiment. It will be used to name the resulting image.

• standardize (bool, default=False) – Indicate if resulting brain map is required to be standardized.

• save_path (str or pathlib.PosixPath) – Path to save created map.

• smoothing_fwhm (float, default=0) – Size in millimeters of the spatial smoothing of each reconstructed map.

• threshold (float, default=0) – Threshold value for thresholding resulting image.

• cluster_threshold (int, default=0) – Threshold for the number of points in a cluster to be cutoff resulting image.

Returns

• nibabel.nifti1.Nifti1Image – Nii file for resulting brain map.

• pathlib.PosixPath – Path where the resulting image is saved.