Autolabeller

This tool can automatically classify noisy spatial maps of brain activity, and generate anatomical and functional labels of the spatial maps and a reordered functional network connectivity matrix.

Prerequisites

Autolabeller is written in Matlab and requires several Matlab toolboxes to run. Please download the following toolboxes and add to your Matlab path.

• GIFT
• SPM12
• BCT Toolbox (March 2019 release)

Using the autolabeller

Example code can be found in src/example_label_ic.m.

% add requirements to path
addpath( genpath( '../bin/GroupICATv4.0b/' ) )      % GIFT toolbox
addpath( genpath( '../bin/CanlabCore' ) )       % Canlab toolbox
addpath( '../bin/spm12/' )      % SPM12 toolbox
addpath( '../bin/2019_03_03_BCT' )       % Brain connectivity toolbox
addpath( '../bin/autolabeller/' )       % add the autolabeller src folder only

% GICA example with fbirn dataset
clear params;
params.param_file = './fbirnp3_rest_ica_parameter_info.mat';
params.outpath = './results/fbirn/';
params.fit_method = 'mnr';
params.n_corr = 3;
params.skip_noise = 0;
params.skip_anatomical = 0;
params.skip_functional = 0;
params.noise_training_set = 'pre_fbirn_sub';
params.anatomical_atlas = 'aal';
params.threshold = 3;
params.functional_atlas = 'yeo_buckner';
disp( 'Running the autolabeller on FBIRN dataset' )
label_auto_main( params );

% Spatial map example with the Neuromark template
clear params;
params.sm_path = './NetworkTemplate_High_VarNor.nii';
params.mask_path = './Mask.img';
params.outpath = './results/neuromark/';
params.fit_method = 'mnr';
params.n_corr = 3;
params.skip_noise = 0;
params.skip_anatomical = 0;
params.skip_functional = 0;
params.noise_training_set = 'pre_aggregate';
params.anatomical_atlas = 'aal';
params.threshold = 3;
params.functional_atlas = 'yeo_buckner';
disp( 'Running the autolabeller on NeuroMark dataset' )
label_auto_main( params );

Parameters & outputs

Inputs

• params.param_file Location of GICA parameter file
• params.sm_path Location of NIFTI data containing spatial maps. Use this if you are not running GICA.
• params.outpath Output directory
• params.n_corr How many ROI top correlations to calculate for anatomical/functional labeling. Default = 3
• params.threshold Threshold value for the spatial maps. Default = 3
• params.skip_noise If you do not want to run or already ran artifact detection step, set to 1. Otherwise set to 0 by default.
• params.skip_anatomical If you do not want to run or already ran anatomical labeling step, set to 1. Otherwise set to 0 by default.
• params.skip_functional If you do not want to run or already ran functional labeling step, set to 1. Otherwise set to 0 by default.
• params.noise_training_set Which dataset to use to train the noisecloud model. Options: pre_fbirn_sub, pre_aggregate
  • pre_fbirn_sub: when both spatial maps and timecourses are available, as in a GIFT output
  • pre_aggregate: when only spatial maps are available
• params.anatomical_atlas Which atlas to use for anatomical labeling. Options: aal
• params.functional_atlas Which atlas to use for functional labeling. Options: yeo_buckner, gordon2016, caren. Default = yeo_buckner.

Outputs

The following files are written into params.outpath folder: * network_labels.csv is a vector of 0/1 corresponding to the input spatial maps; 0=artifact, 1=network * anatomical_labels.csv has the following columns: * volume 1-N where N is the number of input spatial maps * network a vector of 0/1 corresponding to the input spatial maps; 0=artifact, 1=network * region_1,spatial_corr_1 AAL anatomical region with the highest spatial correlation to the spatial maps, and the correlation value * region_2,spatial_corr_2,region_3,spatial_corr_3 AAL anatomical region with the second and third highest spatial correlations to the spatial maps, and the corresponding correlation values * functional_labels_[atlas].csv has the following columns: * volume 1-N where N is the number of input spatial maps * network a vector of 0/1 corresponding to the input spatial maps; 0=artifact, 1=network * region_1,spatial_corr_1 Functional parcellation from [atlas] with highest spatial correlation to the spatial maps, and the correlation value. Current available atlas are Yeo/BucknerLab, Gordon (2016), and CAREN * region_2,spatial_corr_2,region_3,spatial_corr_3 Functional parcellations with the second and third highest correlations to the spatial maps, and the corresponding correlation values * sorted_IC_idx_[atlas].csv sorted index of the input spatial maps corresponding to the brain networks (artifact-related component indexes are removed) * sorted_fnc_[atlas].csv sorted functional network connectivity (FNC) matrix of the brain networks * nc folder contains the noisecloud toolbox output. It has the following files: * *.nii template nii files warped into the same space as the input spatial maps. * nc_class_labels.txt a vector of 0/1 corresponding to the input spatial maps; 0=artifact, 1=network * training/testing_features.csv contains the training/testing input data features used by the noisecloud toolbox in classification.

Result

The following figures are generated using the ./src/example_plot_fnc.m script. You can update the ICA parameter file and autolabeller output folder locations in the above to generate new figures. The script uses ICA parameter file to load the FNC from the ICA post-process result.

Customizing the output

The autolabeler outputs can be easily updated based on visual observation as follows: - Change the network(1)/noise(0) labels corresponding to the IC you want to update in network_labels.csv. - Set params.skip_noise = 1 - Run the autolabeller with the original parameters again.

This will generate the updated anatomical/functional label files and IC order for the FNC matrix.

Citation

Salman, M. S., Wager, T., Damaraju, E., Abrol, A., Vergara, V., Fu, Z., & Calhoun, V. (2021). An Approach to Automatically Label & Order Brain Activity/Component Maps. Brain Connectivity, brain.2020.0950. https://doi.org/10.1089/brain.2020.0950