├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── data ├── Seizure_1.mat └── Seizure_2.mat ├── examples ├── Estimation.m └── generateData.m ├── figures ├── connectivity │ ├── Patient 1 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 10 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 11 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 13 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 15 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 2 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 3 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 5 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 6 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 7 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ ├── Patient 8 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif │ └── Patient 9 │ │ ├── Ex-Py.tif │ │ ├── In-Py.tif │ │ ├── Py-Ex.tif │ │ ├── Py-In.tif │ │ └── input-Py.tif └── energy │ ├── Patient 1.tif │ ├── Patient 10.tif │ ├── Patient 11.tif │ ├── Patient 13.tif │ ├── Patient 15.tif │ ├── Patient 2.tif │ ├── Patient 3.tif │ ├── Patient 4.tif │ ├── Patient 6.tif │ ├── Patient 7.tif │ ├── Patient 8.tif │ └── Patient 9.tif └── src ├── g.m ├── prop_mean_and_cov.m └── set_params.m /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # Windows Installer files 12 | *.cab 13 | *.msi 14 | *.msm 15 | *.msp 16 | 17 | # Windows shortcuts 18 | *.lnk 19 | 20 | # ========================= 21 | # Operating System Files 22 | # ========================= 23 | 24 | # OSX 25 | # ========================= 26 | 27 | .DS_Store 28 | .AppleDouble 29 | .LSOverride 30 | 31 | # Thumbnails 32 | ._* 33 | 34 | # Files that might appear in the root of a volume 35 | .DocumentRevisions-V100 36 | .fseventsd 37 | .Spotlight-V100 38 | .TemporaryItems 39 | .Trashes 40 | .VolumeIcon.icns 41 | 42 | # Directories potentially created on remote AFP share 43 | .AppleDB 44 | .AppleDesktop 45 | Network Trash Folder 46 | Temporary Items 47 | .apdisk 48 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Driven-Estimation 2 | Neural model state and parameter estimation from data 3 | 4 | Copyright (C) 2018 Dean R. Freestone, Philippa J. Karoly - All Rights Reserved 5 | 6 | You may use, distribute and modify this code under the terms of the GNU General Public license v2.0. You can find a copy of this license in file LICENSE.md. Note than when distributing derived works, the source code of the work must be made available under the same license. 7 | 8 | Figures in this repository are licensed under Creative Commons with conditions: 9 | - Attribution: please cite Seizure Pathways: A model-based investigation, P.J. Karoly, L. Kuhlmann, D. Soudry, D.B. Grayden, M.J. Cook and D.R. Freestone (2018) *PLoS Computational Biology* (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006403) 10 | 11 | To cite this code in academic research please reference: 12 | Seizure Pathways: A model-based investigation, P.J. Karoly, L. Kuhlmann, D. Soudry, D.B. Grayden, M.J. Cook and D.R. Freestone (2018) *PLoS Computational Biology* (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006403) 13 | 14 | To cite the data in academic researh please reference: 15 | Melbourne University NeuroVista Seizure Prediction Data (https://doi.org/10.26188/5b6a999fa2316) and Cook, M. J., O'Brien, T. J., Berkovic, S. F., Murphy, M., Morokoff, A., Fabinyi, G., ... & Hosking, S. (2013). Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. The Lancet Neurology, 12(6), 563-571. 16 | 17 | ## Code 18 | Source code is located in src folder 19 | - **set_params.m**: A function that sets the parameters of the neural mass model (Jansen & Rit 1995) 20 | - **g.m**: A function that computes the erf sigmoid 21 | - **prop_mean_covariance.m**: A function that computes the posterior mean and covariance of the state/parameter distribution propagated through the neural mass model 22 | 23 | Example code is located in Example folder 24 | - **Example.m**: Runs the estimation for a single seizure 25 | - **generateData.m**: Runs the model at different values of input and generates simulated data 26 | 27 | ## Data 28 | One example seizure is provided in data folder. Additional seizure data can be downloaded online from the Epilepsy Ecosystem. Data are available as “Melbourne Seizure Prediction Trial Seizure Data” from https://www.epilepsyecosystem.org/howitworks/#data. After registering you will be emailed an invitation to download the data from figshare. The data are licensed under a Creative Commons license with conditions on ATTRIBUTION, NON-COMMERCIAL use and SHARE-ALIKE. Users will be required to create an account and sign a terms of use agreement that requires no commercial use, and restricts all works derived from the data to be made publicly available under a Creative Commons license. 29 | 30 | Data is a single .mat file with a variable, *Seizure* that has dimension T x N, where T is the number of samples, and N is the number of electrode channels (16). The seizure onset is 5 minutes from the start of the data and seizure offset is 1 minute from the end of the data. Data is sampled at 400Hz. 31 | 32 | ## Supplementary Figures 33 | These figures relate specifically to the results presented in Karoly et al (2018). We provide additional figures for connectivity parameter estimation and signal energy showing all 16 channels. 34 | 35 | ## Notes on Filter Implementation 36 | If you are not familiar with Kalman filtering, a review of the recommended resources (or similar) is strongly advised before implementing this code. Density filters can be plagued by numerical instability and in practise fine-tuning of filter parameters is often required to run the filter on real-world data. We provide a non-exhaustive list of some gotchas and heuristics that we have observed over the years. 37 | 38 | - Data range is inconsistent with the model (scale your data so it lies not to far from the bounds of what your model can simulate) 39 | - State/parameter values differ by many orders of magnitude (scale your model) 40 | - Mismatched DC between model and data (add one constant offset parameter to the estimate) 41 | - Covariance, P becomes asymmetric (enforce symmetry with P = (P + P') / 2) 42 | - Covariance becomes too small, breaking the numerics of the filter (add a very small amount to the diagonal of Q, initialise P larger) 43 | - Estimates don't converge (try using annealing to gradually increase R) 44 | - Too many parameters to estimate (maybe you need to lump them together) 45 | 46 | ## Further References 47 | More information can be found in the following refrences 48 | 1. [Freestone, D. R., Karoly, P. J., Neši?, D., Aram, P., Cook, M. J., & Grayden, D. B. (2014). 49 | Estimation of effective connectivity via data-driven neural modeling. Frontiers in neuroscience, 8, 383](https://www.frontiersin.org/articles/10.3389/fnins.2014.00383/full) 50 | 51 | 2. [Ahmadizadeh, S., Karoly, P. J., Nešic, D., Grayden, D. B., Cook, M. J., Soudry, D., & Freestone, D. R. (2018). 52 | Bifurcation analysis of two coupled Jansen-Rit neural mass models. PloS one, 13(3)](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0192842) 53 | 54 | 3. [Bifurcation-Estimation (Repository)](https://github.com/pkaroly/Bifurcation-Estimation) 55 | 56 | 4. Kuhlmann, L., Freestone, D. R., Manton, J. H., Heyse, B., Vereecke, H. E., Lipping, T., ... & Liley, D. T. (2016). 57 | Neural mass model-based tracking of anesthetic brain states. NeuroImage, 133, 438-456. 58 | 59 | ## Recommended Resources 60 | 61 | 1. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, Dan Simon and [the authors example code!](http://academic.csuohio.edu/simond/) 62 | 63 | 3. Neural Control Engineering: The Emerging Intersection Between Control Theory, Steve Schiff (esp Chapter 2 and Chapter 5) and [all the example code](https://www.dropbox.com/sh/b23je0226el37wx/AABQJlWFxiI36u_cJba33NeXa?dl=0&preview=Code+Archives+Neural+Control+Engineering+062512.zip) 64 | -------------------------------------------------------------------------------- /data/Seizure_1.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pkaroly/Data-Driven-Estimation/c12f68492741070be16e6f894d5aa4bc99f1adfe/data/Seizure_1.mat -------------------------------------------------------------------------------- /data/Seizure_2.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pkaroly/Data-Driven-Estimation/c12f68492741070be16e6f894d5aa4bc99f1adfe/data/Seizure_2.mat -------------------------------------------------------------------------------- /examples/Estimation.m: -------------------------------------------------------------------------------- 1 | %% Estimation 2 | % runs the state/parameter estimation and plots results for a single 3 | % channel at a time 4 | 5 | %% 6 | % Dean Freestone, Philippa Karoly 2016 7 | % This code is licensed under the MIT License 2018 8 | 9 | 10 | %% 11 | clear 12 | close all 13 | clc 14 | 15 | load('../data/Seizure_1.mat'); % change this path to load alternative data 16 | addpath(genpath('../src/')); 17 | 18 | input = 300; 19 | input_offset = []; 20 | % generate some data 21 | % 22 | time = 5; 23 | Fs = 0.4e3; 24 | [A,B,C,N_states,N_syn,N_inputs,N_samples,xi, ... 25 | v0,varsigma,Q,R,H,y] = set_params(input,input_offset,time,Fs); 26 | 27 | if ~isempty(input_offset) 28 | % reset the offset 29 | my = mean(y); 30 | input_offset = -my/0.0325; % Scale (mV) to convert constant input to a steady-state effect on pyramidal membrane. NB DIVIDE BY 10e3 for VOLTS 31 | [A,B,C,N_states,N_syn,N_inputs,N_samples,xi, ... 32 | v0,varsigma,Q,R,H,y] = set_params(input,input_offset); 33 | end 34 | 35 | xi_hat_init = mean(xi(:,N_samples/2:end),2); % to ignore inital transient take the mean of the second half of the test data 36 | P_hat_init = 10*cov(xi(:,N_samples/2:end)'); 37 | P_hat_init(2*N_syn+1:end,2*N_syn+1:end) = eye(N_syn+N_inputs)*10e-2; % open up the error in parameters 38 | 39 | % set inital conditions for the KF 40 | xi_hat = zeros(N_states,N_samples); % initialize for speed 41 | P_hat = zeros(N_states,N_states,N_samples); % initialize for speed 42 | P_diag = zeros(N_states,N_samples); % initialize for speed 43 | 44 | xi_hat(:,1) = xi_hat_init; % to ignore inital transient take the mean of the second half of the test data 45 | P_hat(:,:,1) = P_hat_init; 46 | 47 | anneal_on = 1; 48 | kappa_0 = 10000; 49 | t_end_anneal = N_samples/20; 50 | 51 | % loop through 16 chans 52 | iCh = 1; 53 | 54 | fprintf('Channel %02d ...',iCh); 55 | 56 | % get one channel at a time 57 | % NB - portal data is inverted. we need to scale it to some 58 | % 'reasonable' range for the model, but still capture amplitude 59 | % differences bw seizures 60 | y = -0.5*Seizure(:,iCh); 61 | N_samples = length(y); 62 | 63 | for t=2:N_samples % N_samples 64 | 65 | xi_0p = squeeze(xi_hat(:,t-1)); 66 | P_0p = squeeze(P_hat(:,:,t-1)); 67 | 68 | % predict 69 | % 70 | [xi_1m, P_1m] = prop_mean_and_cov(N_syn,N_states,N_inputs,A,B,C,P_0p,xi_0p,varsigma,v0,Q); 71 | 72 | if (t<=t_end_anneal) && anneal_on 73 | kappa = kappa_0^((t_end_anneal-t)/(t_end_anneal-1)); 74 | else 75 | kappa = 1; 76 | end 77 | 78 | K = P_1m*H'/(H*P_1m*H' + kappa*R); 79 | 80 | % correct 81 | % 82 | xi_hat(:,t) = xi_1m + K*(y(t) - H*xi_1m); 83 | P_hat(:,:,t) = (eye(N_states) - K*H)*P_1m; 84 | P_diag(:,t) = diag(squeeze(P_hat(:,:,t))); 85 | 86 | if t > 2 87 | fprintf('\b\b\b\b'); 88 | end 89 | fprintf('%03d%%', round(100*t/N_samples)); 90 | end 91 | 92 | close all 93 | figure 94 | x = (1:N_samples)/Fs; 95 | y = H*xi_hat; 96 | plot(x,y,'k'); 97 | set(gca,'box','off') 98 | xlabel('Time (s)') 99 | ylabel('ECoG (mv)') 100 | 101 | % this scale is from set_params (used for numerical stability) 102 | scale = 50; 103 | figure('name','membrane potential estimates' ,'units','normalized','position',[0 0 1 1] ) 104 | subplot(411),plot(x,xi_hat(1,:)/scale) 105 | title('Inhibitory -> Pyramidal'); 106 | subplot(412),plot(x,xi_hat(3,:)/scale) 107 | title('Pyramidal -> Inhibitory'); 108 | subplot(413),plot(x,xi_hat(5,:)/scale) 109 | title('Pyramidal -> Excitatory'); 110 | subplot(414),plot(x,xi_hat(7,:)/scale) 111 | title('Excitatory -> Pyramidal'); 112 | xlabel('Time (s)') 113 | ylabel('post-synaptic membrane potential (mV)'); 114 | 115 | % Units of these are not meaningful because they are lumped parameters. 116 | % Typically we assess them relative to some other state (i.e. background) 117 | figure('name','parameter estimates' ,'units','normalized','position',[0 0 1 1] ) 118 | subplot(511),plot(x,xi_hat(9,:)) 119 | title('Input'); 120 | subplot(512),plot(x,xi_hat(10,:)) 121 | title('Inhibitory -> Pyramidal'); 122 | subplot(513),plot(x,xi_hat(11,:)) 123 | title('Pyramidal -> Inhibitory'); 124 | subplot(514),plot(x,xi_hat(12,:)) 125 | title('Pyramidal -> Excitatory'); 126 | subplot(515),plot(x,xi_hat(13,:)) 127 | title('Excitatory -> Pyramidal'); 128 | -------------------------------------------------------------------------------- /examples/generateData.m: -------------------------------------------------------------------------------- 1 | %% generateData 2 | % plots simulated data from the neural mass model at different input values 3 | 4 | 5 | %% 6 | % Dean Freestone, Philippa Karoly 2016 7 | % This code is licensed under the MIT License 2018 8 | 9 | %% 10 | clear 11 | clc 12 | close all 13 | 14 | addpath(genpath('../src/')); 15 | 16 | time = 60; 17 | Fs = 1e3; 18 | x = 1/Fs:1/Fs:time; 19 | sigma_R = 0; 20 | for input = 0:10:320 21 | [A,B,C,N_states,N_syn,N_inputs,N_samples,xi, ... 22 | v0,varsigma,Q,R,H,y] = set_params(input,[],time,Fs, sigma_R); 23 | plot(x,y,'k'); 24 | set(gca,'box','off','xtick',[0 time]); 25 | xlabel('Time (s)'); 26 | ylabel('ECoG (mV)'); 27 | title(sprintf('input / drive: %d',input)); 28 | drawnow; 29 | pause(0.5); 30 | end 31 | 32 | %% 33 | function plotPotentials(xi) 34 | figure 35 | figure('name','parameter estimates' ,'units','normalized','position',[0 0 1 1] ) 36 | subplot(411),plot(xi(1,:)) 37 | title('Inhibitory -> Pyramidal'); 38 | subplot(412),plot(xi(3,:)) 39 | title('Pyramidal -> Inhibitory'); 40 | subplot(413),plot(xi(5,:)) 41 | title('Pyramidal -> Excitatory'); 42 | subplot(414),plot(xi(7,:)) 43 | title('Excitatory -> Pyramidal'); 44 | end 45 | 46 | function plotAlpha(xi) 47 | figure('name','parameter estimates' ,'units','normalized','position',[0 0 1 1] ) 48 | subplot(511),plot(xi(9,:)) 49 | title('Input'); 50 | subplot(512),plot(xi(10,:)) 51 | title('Inhibitory -> Pyramidal'); 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function out = g(v,v0,varsigma) 4 | 5 | out = 0.5*erf((v - v0) / (sqrt(2)*varsigma)) + 0.5; 6 | % out = 1 ./ (1 + exp(varsigma*(-v+v0))); 7 | 8 | end -------------------------------------------------------------------------------- /src/prop_mean_and_cov.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pkaroly/Data-Driven-Estimation/c12f68492741070be16e6f894d5aa4bc99f1adfe/src/prop_mean_and_cov.m -------------------------------------------------------------------------------- /src/set_params.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pkaroly/Data-Driven-Estimation/c12f68492741070be16e6f894d5aa4bc99f1adfe/src/set_params.m --------------------------------------------------------------------------------