Dynanets

The purpose of this code is to track communities in dynamic networks.

Simplified analysis

... for a simplified example of the method, check out the README in this branch.

Dependices

Download the following software packages, required to track dynamic communities and analyze the networks.

• Download the The Brain Connectivity Toolbox
• Download this "generalized Louvain" MATLAB code for community detection GenLouvain2.1
• Download the dynamic plex percolation method
• Download the export_fig, a MATLAB toolbox for exporting publication quality figures
• Download the chronux toolbox

Initial MATLAB setup

Rename the file dynanets_defaults_local_example.m --> dynanets_defaults_local.m

• Line 7, replace 'My-MAC' with the name of the computer that's running the code. To find this name, type system('hostname') at the MATLAB command line.
• Line 8, replace '/Users/me/analysis/toolboxes/dpp/' with a string to the directory where DPPM was installed.
• Line 9, replace '/Users/me/analysis/toolboxes/BCT' with a string to the directory where the Brain Connectivity Toolbox was installed.
• Line 10, replace '/Users/me/analysis/toolboxes/GenLouvain-2.1' with a string to the directory where the generalized Louvain code was installed.
• Line 11, replace '/Users/me/analysis/toolboxes/export_fig' with a string to the directory where export-fig was installed.
• Line 12, replace '/Volumes/Data/Output_Data/' with a string to an output write directory for data.
• Line 13, replace '/Volumes/Data/Output_Data/' with a string to an output write directory for figures.

You can enter information for a second system in Lines 13-18, if useful.

Code organization

The central file of the pipeline is main_dynanets.m, which calls all other routines. The code is organized into subfolders according to the tasks being done:

• 1-build: start a simulation or load existing data
• 2-preprocess: apply simple preprocessing, like filtering
• 3-infer: runs the network inference procedure (currently based on maximum cross-correlation)
• 4-track: runs the different community tracking algorithms implemented (DPPM, CPM, MMM)
• 5-analyze: runs basic analysis of the results, and outputs visualizations.
• simulation: contains the code to run the simulations analyzed in the paper.

This pipeline configurations is based on the use of a structure called cfg where all the settings are stored (inspired by the approach implemented in Fieldtrip toolbox). Here are the options of the configuration:

% Build simulation data
cfg.data.run = true;
cfg.data.patients = {'P1', 'P2'}; % list of the patients to be analyzed (used to search in folders)
cfg.data.seizures = {{'S1', 'S2'}, {'S1', 'S2', 'S3'}}; % list of seizures for each patients
cfg.data.build_fun = @my_build_fun; % function called to build the data segment to be analyzed (e.g., will create P1_S1.mat file)
cfg.data.padding = [0 0]; % how much data to include pre- and post-seizure

% Preprocessing settings
cfg.preprocess.run = true; % runs the preprocessing step
cfg.preprocess.ref = ''; % Choose the reference for re-referencing: '' (do nothing), 'cavg' (common average), 'bipolar' (only for simulations)
cfg.preprocess.filt = 'firls'; % choice for the filter function
cfg.preprocess.band = [4 50]; % Frequency band to keep

% Inference settings
cfg.infer.run = true; % runs the inference step
cfg.infer.method = 'corr_0_lag'; % Choose the inference method: 'corr', 'corr_0_lag'
cfg.infer.windowsize = 1; % Window size for net inference (s).
cfg.infer.windowstep = 0.5;  % Window overlap (s)
cfg.infer.smooth = false; % Use vote to smooth networks in time
cfg.infer.scale  = true; % Scale variance of correlation using all time.

% Network tracking through time
cfg.track.run = true; % runs the tracking step
cfg.track.method = { % list of methods to apply with their parameters (can add as many as wanted)
    struct('name', 'dpp', 'k', 2, 'm', 4);
    struct('name', 'mmm', 'gamma', 1, 'omega', 0.1);
    struct('name', 'cpm', 'min_clique', 4);
    };

% Settings for figures
cfg.fig.run = true; % runs the analysis step
cfg.fig.type = '-djpeg'; % Choose the output format: '-djpeg', '-depsc', '-dpdf'
cfg.fig.plotpadding = [0 0]; % Used in xlim in some figs, e.g. [0 0], PADDING
cfg.fig.mmmthresh = 4; % used to clean the MMM plots (minimum com size, 0 = all)
cfg.fig.analyze_seizures = true; % runs the seizure by seizure analysis step
cfg.fig.custom_node_sort = @my_order; % define a different order for the nodes in the plots [optional]
cfg.fig.custom_stats = @my_additional_stats; % run another function for additional analyses [optional]
cfg.fig.analyze_population_fun = @population_results; % analyze patient by patient results [optional]
cfg.fig.usetitle = true; % choose to use titles in plots
cfg.fig.fontsize = 12; % font size used in plots

To run and analyze multiple scenarios, each multiple times, see the example cfg description in the simulation folder, dynanets_sim_all.m.

To run and analyze a single simulation scenario a single time, see the example cfg description in the simulation folder, dynanets_sim_individual.m

Simulations

The simulation folder contains the code to run the simulations from the paper. More specifically:

• simulation\7-node-example contains the code to generate Fig 1C
• simulation\9-node-example contains the code to generate Fig 1D
• all the other files are used to generate the data used in Fig 2-4. dynanets_sim_all.m is the core file that setups the parameters of the simulations and run the pipeline.

Example simulation and network tracking analysis

To run an example simulation and analysis, first define dynanets_defaults_local.m as described above. Then, from the root dppm folder, run the following commands at the MATLAB prompt:

>> dynanets_defaults_local
>> run simulation/dynanets_sim_individual