brainstat.context.meta_analysis.surface_decode_nimare

brainstat.context.meta_analysis.surface_decode_nimare(pial, white, stat_labels, mask_labels, interpolation='linear', data_dir=None, verbose=True, correction='fdr_bh', feature_group=None, features=None)[source]

Meta-analytic decoding of surface maps using NeuroSynth or Brainmap.

Parameters

  • pial (str, BSPolyData, list) – Path of a pial surface file, BSPolyData of a pial surface or a list containing multiple of the aforementioned.

  • white (str, BSPolyData, list) – Path of a white matter surface file, BSPolyData of a pial surface or a list containing multiple of the aforementioned.

  • stat_labels (str, numpy.ndarray, list) – Path to a label file for the surfaces, numpy array containing the labels, or a list containing multiple of the aforementioned.

  • mask_labels (str, numpy.ndarray, list) – Path to a mask file for the surfaces, numpy array containing the mask, or a list containing multiple of the aforementioned. If None all vertices are included in the mask. Defaults to None.

  • interpolation (str, optional) – Either ‘nearest’ for nearest neighbor interpolation, or ‘linear’ for trilinear interpolation, by default ‘linear’.

  • data_dir (str, optional) – The directory of the nimare dataset. If none exists, a new dataset will be downloaded and saved to this path. If None, the directory defaults to your home directory, by default None.

  • verbose (bool, optional) – If true prints additional output to the console, by default True.

  • correction (str, optional) – Multiple comparison correction. Valid options are None and ‘fdr_bh’, by default ‘fdr_bh’.

Returns

pandas.DataFrame – Table with each label and the following values associated with each label: ‘pForward’, ‘zForward’, ‘likelihoodForward’,‘pReverse’, ‘zReverse’, and ‘probReverse’.