neurosynth/analysis/decode.py
def _roi_association(self, imgs_to_decode, value='z', binarize=None):
        """ Computes the strength of association between activation in a mask
        and presence/absence of a semantic feature. This is essentially a
        generalization of the voxel-wise reverse inference z-score to the
        multivoxel case.
        """
        imgs_to_decode = imgs_to_decode.squeeze()
        x = average_within_regions(self.dataset, imgs_to_decode).astype(float)
        y = self.dataset.feature_table.data[self.feature_names].values
        if binarize is not None:
            y[y > binarize] = 1.
            y[y < 1.] = 0.
        r = self._xy_corr(x.T, y)
        if value == 'r':
            return r
        elif value == 'z':
            f_r = np.arctanh(r)
            return f_r * np.sqrt(y.shape[0] - 3)
def _roi_association(self, imgs_to_decode, value='z', binarize=None):
        """ Computes the strength of association between activation in a mask
        and presence/absence of a semantic feature. This is essentially a
        generalization of the voxel-wise reverse inference z-score to the
        multivoxel case.
        """
        imgs_to_decode = imgs_to_decode.squeeze()
        x = average_within_regions(self.dataset, imgs_to_decode).astype(float)
        y = self.dataset.feature_table.data[self.feature_names].values
        if binarize is not None:
            y[y > binarize] = 1.
            y[y < 1.] = 0.
        r = self._xy_corr(x.T, y)
        if value == 'r':
            return r
        elif value == 'z':
            f_r = np.arctanh(r)
            return f_r * np.sqrt(y.shape[0] - 3)
def _roi_association ( self , imgs_to_decode , value = 'z' , binarize = None ) : imgs_to_decode = imgs_to_decode . squeeze ( ) x = average_within_regions ( self . dataset , imgs_to_decode ) . astype ( float ) y = self . dataset . feature_table . data [ self . feature_names ] . values if binarize is not None : y [ y > binarize ] = 1. y [ y < 1. ] = 0. r = self . _xy_corr ( x . T , y ) if value == 'r' : return r elif value == 'z' : f_r = np . arctanh ( r ) return f_r * np . sqrt ( y . shape [ 0 ] - 3 )
Computes the strength of association between activation in a mask
        and presence/absence of a semantic feature. This is essentially a
        generalization of the voxel-wise reverse inference z-score to the
        multivoxel case.
Computes the strength of association between activation in a mask and presence / absence of a semantic feature . This is essentially a generalization of the voxel - wise reverse inference z - score to the multivoxel case .