Human_Amygdala_MUA_sEEG_FearVideo/code_MATLAB/Figure_2.m

function fig = Figure_2( strNIXFolderPath, varargin )

% Figure_2.m reproduces Figure 2 in the dataset publication
% fig = Figure_2(strNIXFolderPath) reproduces Figure 2
% strNIXFolderPath is the path of the folder with NIX files
% fig is the figure handle
% Default neuron number plotted is 30
% fig = Figure_2(strNIXFolderPath,nNexampleNeuron) reproduces Figure 2 with the
% example neuron nNexampleNeuron
%
% Add the toolbox 'gramm' to the MATLAB path
% The toolbox can be downloaded from https://github.com/piermorel/gramm

% Example neuron
if(nargin>1)
    nExampleNeuron = varargin{1};
else
    nExampleNeuron = 30;
end

SPIKES_All = [];
% Load data for all neurons
for nSubject = 1:9
    % File name
    strNIXFileName = sprintf('Data_Subject_%.2d_Session_01.h5',nSubject);
    % Read the NIX file
    f = nix.File([strNIXFolderPath,filesep,strNIXFileName],nix.FileMode.ReadOnly);
    % NIX data
    block = f.blocks{1};
    % Multitags for spike times
    group_MultiTagsSpikes = block.openGroup('Spike times multitags');
    multiTags_SpikeTimes = group_MultiTagsSpikes.multiTags;

    % If there are no neurons, continue
    if(isempty(multiTags_SpikeTimes))
        continue;
    end
    % List of neurons and trials
    % Format for the name is
    % 'Multitag_Spike_Times_Unit_<neuron number>_<micro wire name>_Trial_<trial number>'
    strSpikeTimeLabels = cellfun(@(x) x.name,multiTags_SpikeTimes,'UniformOutput',0);
    strSpikeTimeLabels = cellfun(@(x) strsplit(x,'_'),strSpikeTimeLabels,'UniformOutput',0);
    nNeuronsTrialsList = [cell2mat(cellfun(@(x) str2double(x{5}),strSpikeTimeLabels,'UniformOutput',0)),...
        cell2mat(cellfun(@(x) str2double(x{9}),strSpikeTimeLabels,'UniformOutput',0))];
    ranNeurons = unique(nNeuronsTrialsList(:,1));
    ranTrials = unique(nNeuronsTrialsList(:,2));
    
    % Load spike times and waveforms for each neuron
    SPIKES = [];
    for iNeuron = 1:length(ranNeurons)
        nNeuron = ranNeurons(iNeuron);
        spikes = [];
        for iTrial = 1:length(ranTrials)
            nTrial = ranTrials(iTrial);
            nDataArray = (nNeuronsTrialsList(:,1)==nNeuron)&(nNeuronsTrialsList(:,2)==nTrial);
            % Data array for the selected neuron and trial
            dataArray = multiTags_SpikeTimes{nDataArray}.openPositions;
            % Read spike times
            spike_times = dataArray.readAllData';
            spikes = [spikes;[spike_times,nTrial*ones(length(spike_times),1)]];
        end
        % Store spike times and trial numbers
        SPIKES(iNeuron).spikes = spikes;
        
        % Read waveform for the selected neuron
        dataArray_Waveform = multiTags_SpikeTimes{nDataArray}.features{1}.openData;
        % Store waveforms
        SPIKES(iNeuron).waveform = dataArray_Waveform.readAllData';
     end
    SPIKES_All = [SPIKES_All,SPIKES];    
end


%% Firing rates
FiringRatesHz = [];
tTrialDuration = 26; % seconds
for iNeuron = 1:length(SPIKES_All)
    nNumberOfTrials = max(SPIKES_All(iNeuron).spikes(:,2));
    FiringRatesHz = [FiringRatesHz,length(SPIKES_All(iNeuron).spikes(:,1))/(tTrialDuration*nNumberOfTrials)];
end

%% ISI
isis = [];
for iNeuron = 1:length(SPIKES_All)
    isis_ = [];
    for iTrial = 1:max(SPIKES_All(iNeuron).spikes(:,2))
        isis_ = [isis_;diff(sort(SPIKES_All(iNeuron).spikes(SPIKES_All(iNeuron).spikes(:,2)==iTrial,1))*1000)];
    end
    isis = [isis;(length(isis_(isis_<3))/length(isis_))*100];
end

%% SNR
snr = [];
waveform = [];
for iNeuron = 1:length(SPIKES_All)
    snr_0 = max(abs(SPIKES_All(iNeuron).waveform(:,1)))/mean(SPIKES_All(iNeuron).waveform(:,2));
    snr = [snr snr_0];
    waveform = [waveform;SPIKES_All(iNeuron).waveform(:,1)'];
end

FR = SPIKES_All(nExampleNeuron).spikes;

%% Firing rate for a single neuron
c = [];
fs = -2;
ls = 24;
binSize = 0.1;
ranTrials = {};
for i = 1:max(FR(:,2))
    for iTrial = [0 1] % 1 - red, 0 - blue
        if mod(i,2) == iTrial % odd - neutral, even - aversiv
            spikeTimes = FR(FR(:,2) == i,1);
            ranTrials{i} = spikeTimes;
            c = [c iTrial];
            temp = histc(spikeTimes,fs:binSize:ls);
            binned{i} = smooth(temp(1:end-1)/binSize,10);
            % we set here bin centers
            bins{i} = (fs+binSize/2):binSize:(ls-binSize/2);
        end
    end
end

%% Plot Figure 2
clear g
fig = figure;
% FR
g(1) = gramm( 'x',bins,'y',binned,'color',c );
n = length(binned);
custom_statfun = @(y)([mean(y);
    mean(y) - std(y)/sqrt(17);
    mean(y) + std(y)/sqrt(17)]);
g(1).stat_summary('setylim',true,'type',custom_statfun);
g(1).set_names( 'x','','y','Rate [Hz]');
g(1).set_layout_options('position',[0.05,0.7,0.9,0.3]);
g(1).no_legend();
g(1).axe_property('xlim',[fs 20],'XColor','none');
% Inset
g(6) = gramm('x',((1:64)-20)/32,'y',waveform(nExampleNeuron,:));
g(6).geom_line();
g(6).set_names('x','Time (ms)','y','uV' );
g(6).set_layout_options('position',[0.1,0.9,0.1,0.1]);
g(6).set_text_options('base_size',4);
g(6).set_color_options('chroma',0,'lightness',30);
g(6).set_line_options('base_size',1);
g(6).no_legend();
% g(6).axe_property('xlim',[fs 20],'XColor','none');
% raster
g(2) = gramm('x',ranTrials,'color',c);
g(2).geom_raster('geom','point');
g(2).set_point_options('base_size',3);
g(2).set_names('x','Time [s]','y','Trial no. (reordered)');
g(2).set_layout_options('position',[0.05,0.43,0.9,0.3]);
g(2).no_legend();
g(2).axe_property('xlim',[fs,20],'ylim',[-1,9]);
% ISI
g(3) = gramm('x',isis);
g(3).stat_bin();
g(3).set_layout_options('position',[0.05,0.1,0.25,0.3]);
g(3).set_names('x','Percentage of ISI < 3 ms','y','No. of units');
g(3).set_color_options('hue_range',[155,200]);
% FR all
g(4) = gramm('x',FiringRatesHz);
g(4).stat_bin();
g(4).set_layout_options('position',[0.38,0.1,0.25,0.3]);
g(4).set_names('x','Firing rate [Hz]','y','No. of units');
g(4).axe_property('xlim',[0 20]);
g(4).set_color_options('hue_range',[155 200]);
% SNR
g(5) = gramm('x',snr);
g(5).stat_bin();
g(5).set_layout_options('position',[0.71,0.1,0.25,0.3]);
g(5).set_names('x','Waveform peak SNR','y','No. of neurons');
g(5).axe_property('xlim',[0 10]);
g(5).set_color_options('hue_range',[155 200]);
% Labels
x = [0,0,0,0.35,0.68];
y = [0.97,0.7,0.37,0.37,0.37];
l = ['a','b','c','d','e'];
g(7) = gramm('x',x,'y',y,'label',l);
g(7).geom_label('Color','k','fontweight','bold');
g(7).axe_property('XColor','none','YColor','none','xlim',[0,1],...
    'ylim',[0,1],'Visible','off');
g(7).set_layout_options('position',[0,0,1,1]);
g.draw();

% Save figure
print('-dtiff','-r300','fig_2.tiff')

end