You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: ISC.m
    path of file: ./repos/CanlabCore/CanlabCore/@brainpathway_multisubject
    
    the code of the file until where you have to start completion: function bs = ISC(bs)
% Calculate brain-wide inter-subject correlations in patterns across parcel means
%
% :Usage:
% ::
%
%     bs = ISC(bs)
%
% For objects: Type methods(object_name) for a list of special commands
%              Type help object_name.method_name for help on specific
%              methods.
%
% ..
%     Author and copyright information:
%
%     Copyright (C)2020 Tor Wager
%
%     This program is free software: you can redistribute it and/or modify
%     it under the terms of the GNU General Public License as published by
%     the Free Software Foundation, either version 3 of the License, or
%     (at your option) any later version.
%
%     This program is distributed in the hope that it will be useful,
%     but WITHOUT ANY WARRANTY; without even the implied warranty of
%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%     GNU General Public License for more details.
%
%     You should have received a copy of the GNU General Public License
%     along with this program.  If not, see <http://www.gnu.org/licenses/>.
% ..
%
% :Inputs:
%
%   **bs:**
%        A brainpathway_multisubject object
%

    
function bs = ISC(bs)
% Calculate brain-wide inter-subject correlations in patterns across parcel means
%
% :Usage:
% ::
%
%     bs = ISC(bs)
%
% For objects: Type methods(object_name) for a list of special commands
%              Type help object_name.method_name for help on specific
%              methods.
%
% ..
%     Author and copyright information:
%
%     Copyright (C)2020 Tor Wager
%
%     This program is free software: you can redistribute it and/or modify
%     it under the terms of the GNU General Public License as published by
%     the Free Software Foundation, either version 3 of the License, or
%     (at your option) any later version.
%
%     This program is distributed in the hope that it will be useful,
%     but WITHOUT ANY WARRANTY; without even the implied warranty of
%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%     GNU General Public License for more details.
%
%     You should have received a copy of the GNU General Public License
%     along with this program.  If not, see <http://www.gnu.org/licenses/>.
% ..
%
% :Inputs:
%
%   **bs:**
%        A brainpathway_multisubject object
%
%
% :Optional Inputs:
%   **'nofactor':**
%        Omit factor analysis
%
%
% :Outputs:
%
%   **bs:**
%        A brainpathway_multisubject object
%        Output is added to relevant object properties:
%
%         bs.connectivity.regions.isc = intersubject correlation matrix;
%         bs.connectivity.regions.isc_subj_unusualness = 1/mean isc with others for each subject. 1/isc = [0 Inf] (bounded at zero);
%         bs.connectivity.regions.isc_outliers = subjects with isc_subj_unusualness > 2 SD above the mean
%         bs.connectivity.regions.isc_numfactors = estimated number of factors from elbow of PCA eigenvalues
%         bs.connectivity.regions.isc_Lambda = Subject factor loadings, can be correlated with other variables
%         bs.connectivity.regions.isc_Lambda_descrip = 'Lambdas are n_subjects x numfactors factor loadings, describing dimensions of subject similarity here';
%
%
% :Examples:
% ::
%
%    % None yet - example from HCP data
%
% :References:
%
% :See also:
%   - methods(brainpathway_multisubject)
%

% ..
%    Programmers' notes:
%    12/2020 Created by Tor Wager
% ..


fprintf('Flattening conn matrices. ')
mat = bs.flatten_conn_matrices('replacenans');

% Find NaNs and replace with subject mean (so regions will have min=zero influence on isc)
% Done now in flatten_conn_matrices
% fprintf('Imputing missing values. ')
%
% whnan = isnan(mat);
% subjmean = nanmean(mat');
%
% for i = 1:size(mat, 1)
% 	mat(i, whnan(i, :)) = subjmean(i);
% end