/
ProcessCellMetrics.m
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ProcessCellMetrics.m
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function cell_metrics = ProcessCellMetrics(varargin)
% This function calculates cell metrics for a given recording/session
% Most metrics are single value per cell, either numeric or string type, but
% certain metrics are vectors like the autocorrelograms or cell with double content like waveforms.
% The metrics are based on a number of features: spikes, waveforms, PCA features,
% the ACG and CCGs, LFP, theta, ripples and so fourth
%
% Check the website of CellExplorer for more details: https://cellexplorer.org/
%
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% INPUTS
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
%
% varargin (Variable-length input argument list; see below)
%
% - Parameters defining the session to process -
% basepath - 1. Path to session (base directory)
% sessionName - 3. Database sessionName
% sessionID - 4. Database numeric id
% session - 5. Session struct. Must contain a basepath
%
% - Parameters for the processing - parameters.*
% showGUI - Show GUI dialog to adjust settings/parameters
% metrics - Metrics that will be calculated. A cell with strings
% Examples: 'waveform_metrics','PCA_features','acg_metrics','deepSuperficial',
% 'monoSynaptic_connections','theta_metrics','spatial_metrics',
% 'event_metrics','manipulation_metrics', 'state_metrics','psth_metrics'
% Default: 'all'
% excludeMetrics - Metrics to exclude. Default: 'none'
% removeMetrics - Metrics to remove (supports only deepSuperficial at this point)
% keepCellClassification - logical. Keep existing cell type classifications
% includeInhibitoryConnections - logical. Determines if inhibitory connections are included in the detection of synaptic connections
% manualAdjustMonoSyn - logical. Manually validate monosynaptic connections in the pipeline (requires user input)
% restrictToIntervals - time intervals to restrict the analysis to (in seconds)
% excludeIntervals - time intervals to exclude (in seconds)
% excludeManipulationIntervals - logical. Exclude time intervals around manipulations (loads *.manipulation.mat files and excludes defined manipulation intervals)
% ignoreEventTypes - exclude .events files of specific types
% ignoreManipulationTypes- exclude .manipulations files of specific types
% ignoreStateTypes - exclude .states files of specific types
% showGUI - logical. Show a GUI that allows you to adjust the input parameters/settings
% forceReload - logical. Recalculate existing metrics
% forceReloadSpikes - logical. Reloads spikes and other cellinfo structs
% submitToDatabase - logical. Submit cell metrics to database
% saveMat - logical. Save metrics to cell_metrics.mat
% saveAs - name of .mat file
% saveBackup - logical. Whether a backup of existing metrics should be created
%
% summaryFigures - logical. Plot a summary figure for each cell
% sessionSummaryFigure - logical. Plot summary figure for the whole session
% showFigures - logical. if false, turns off plots from different stages of the processing
% showWaveforms - logical. Shows waveform extraction (turn off to speed up the processing)
%
% debugMode - logical. Activate a debug mode avoiding try/catch
% transferFilesFromClusterpath - logical. Moves previosly generated files from clusteringpath to basepath (new file structure)
%
% - Example calls:
% cell_metrics = ProcessCellMetrics % Load from current path, assumed to be a basepath
% cell_metrics = ProcessCellMetrics('session',session) % Load session from session struct
% cell_metrics = ProcessCellMetrics('basepath',basepath) % Load from basepath
% cell_metrics = ProcessCellMetrics('basepath',basepath,'includeInhibitoryConnections',true) % Load from basepath
% cell_metrics = ProcessCellMetrics('basepath',basepath,'metrics',{'waveform_metrics'}) % Load from basepath
%
% cell_metrics = ProcessCellMetrics('sessionName','rec1') % Load session from database session name
% cell_metrics = ProcessCellMetrics('sessionID',10985) % Load session from database session id
%
% cell_metrics = ProcessCellMetrics('session', session,'fileFormat','nwb','showGUI',true); % saves cell_metrics to a nwb file
%
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% OUTPUT
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
%
% cell_metrics : structure described in details at: https://cellexplorer.org/datastructure/standard-cell-metrics/
% By Peter Petersen
% Last edited: 27-02-2021
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Parsing parameters
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
p = inputParser;
addParameter(p,'sessionID',[],@isnumeric);
addParameter(p,'sessionName',[],@isstr);
addParameter(p,'session',[],@isstruct);
addParameter(p,'spikes',[],@isstruct);
addParameter(p,'basepath',pwd,@isstr);
addParameter(p,'metrics','all',@iscellstr);
addParameter(p,'excludeMetrics',{'none'},@iscellstr);
addParameter(p,'removeMetrics',{'none'},@isstr);
addParameter(p,'restrictToIntervals',[],@isnumeric);
addParameter(p,'excludeIntervals',[],@isnumeric);
addParameter(p,'ignoreEventTypes',{'MergePoints'},@iscell);
addParameter(p,'ignoreManipulationTypes',{'cooling'},@iscell);
addParameter(p,'ignoreStateTypes',{'StateToIgnore'},@iscell);
addParameter(p,'excludeManipulationIntervals',true,@islogical);
addParameter(p,'metricsToExcludeManipulationIntervals',{'waveform_metrics','PCA_features','acg_metrics','monoSynaptic_connections','theta_metrics','spatial_metrics','event_metrics','psth_metrics'},@iscell);
addParameter(p,'keepCellClassification',true,@islogical);
addParameter(p,'manualAdjustMonoSyn',true,@islogical);
addParameter(p,'getWaveformsFromDat',true,@islogical);
addParameter(p,'includeInhibitoryConnections',false,@islogical);
addParameter(p,'showGUI',false,@islogical);
addParameter(p,'forceReload',false,@islogical);
addParameter(p,'forceReloadSpikes',false,@islogical);
addParameter(p,'submitToDatabase',false,@islogical);
addParameter(p,'saveMat',true,@islogical);
addParameter(p,'saveAs','cell_metrics',@isstr);
addParameter(p,'saveBackup',true,@islogical);
addParameter(p,'fileFormat','mat',@isstr);
addParameter(p,'transferFilesFromClusterpath',true,@islogical);
% Plot related parameters
addParameter(p,'showFigures',false,@islogical);
addParameter(p,'showWaveforms',true,@islogical);
addParameter(p,'summaryFigures',false,@islogical);
addParameter(p,'sessionSummaryFigure',true,@islogical);
addParameter(p,'debugMode',false,@islogical);
parse(p,varargin{:})
sessionID = p.Results.sessionID;
sessionin = p.Results.sessionName;
sessionStruct = p.Results.session;
basepath = p.Results.basepath;
parameters = p.Results;
timerCalcMetrics = tic;
% Verifying required toolboxes are installed
installedToolboxes = ver;
installedToolboxes = {installedToolboxes.Name};
requiredToolboxes = {'Curve Fitting Toolbox','Signal Processing Toolbox','Statistics and Machine Learning Toolbox'};
missingToolboxes = requiredToolboxes(~ismember(requiredToolboxes,installedToolboxes));
if ~isempty(missingToolboxes)
for i = 1:numel(missingToolboxes)
warning(['A toolbox required by CellExplorer must be installed: ' missingToolboxes{i}]);
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Loading session metadata from DB or sessionStruct
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if ~isempty(sessionID)
[session, basename, basepath] = db_set_session('sessionId',sessionID);
elseif ~isempty(sessionin)
[session, basename, basepath] = db_set_session('sessionName',sessionin);
elseif ~isempty(sessionStruct)
if isfield(sessionStruct.general,'basePath')
session = sessionStruct;
basename = session.general.name;
basepath = session.general.basePath;
else
[session, basename, basepath] = db_set_session('session',sessionStruct);
if isempty(session.general.entryID)
session.general.entryID = ''; % DB id
end
if isempty(session.spikeSorting{1}.entryID)
session.spikeSorting{1}.entryID = ''; % DB id
end
end
end
% If no session struct is provided it will look for a basename.session.mat file in the basepath and otherwise load the sessionTemplate and show the GUI gui_session
if ~exist('session','var')
basename = basenameFromBasepath(basepath);
if exist(fullfile(basepath,[basename,'.session.mat']),'file')
dispLog(['Loading ',basename,'.session.mat from basepath'],basename);
load(fullfile(basepath,[basename,'.session.mat']),'session');
session.general.basePath = basepath;
elseif exist(fullfile(basepath,'session.mat'),'file')
dispLog('Loading session.mat from basepath',basename);
load(fullfile(basepath,'session.mat'),'session');
session.general.basePath = basepath;
else
cd(basepath)
session = sessionTemplate(basepath);
parameters.showGUI = true;
end
end
% If no arguments are given, the GUI is shown
if nargin==0
parameters.showGUI = true;
end
% Loading preferences
preferences = preferences_ProcessCellMetrics(session);
% Validating format of electrode groups and spike groups (must be of type cell)
if isfield(session.extracellular,'spikeGroups') && isfield(session.extracellular.spikeGroups,'channels') && isnumeric(session.extracellular.spikeGroups.channels)
session.extracellular.spikeGroups.channels = num2cell(session.extracellular.spikeGroups.channels,2);
end
if isfield(session.extracellular,'electrodeGroups') && isfield(session.extracellular.electrodeGroups,'channels') && isnumeric(session.extracellular.electrodeGroups.channels)
session.extracellular.electrodeGroups.channels = num2cell(session.extracellular.electrodeGroups.channels,2)';
end
% Non-standard parameters: vertical spacing and layout
if (~isfield(session,'extracellular') || ~isfield(session.extracellular,'chanCoords') || ~isfield(session.extracellular.chanCoords,'verticalSpacing'))
session.extracellular.chanCoords.verticalSpacing = preferences.general.probesVerticalSpacing;
disp(' Using vertical spacing from preferences')
end
if (~isfield(session,'extracellular') || ~isfield(session.extracellular,'chanCoords') || ~isfield(session.extracellular.chanCoords,'layout'))
session.extracellular.chanCoords.layout = preferences.general.probesLayout;
disp(' Using layout from preferences')
end
if ~isfield(session,'extracellular') || ~isfield(session.extracellular,'electrodeGroups') || ~isfield(session.extracellular.electrodeGroups,'channels') || isempty([session.extracellular.electrodeGroups.channels{:}])
if isfield(session.extracellular,'spikeGroups')
warning('No electrode group has been defined. Copied from spike groups')
session.extracellular.electrodeGroups = session.extracellular.spikeGroups;
session.extracellular.nElectrodeGroups = session.extracellular.nSpikeGroups;
end
end
% Validating that electrode groups and spike groups are 1-indexed
if any([session.extracellular.electrodeGroups.channels{:}]==0)
error('session.extracellular.electrodeGroups.channels contains 0. Must be 1-indexed')
elseif any([session.extracellular.spikeGroups.channels{:}]==0)
error('session.extracellular.spikeGroups.channels contains 0. Must be 1-indexed')
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% showGUI
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if parameters.showGUI
session.general.basePath = basepath;
parameters.preferences = preferences;
[session,parameters,status] = gui_session(session,parameters);
if status==0
dispLog('Cell metrics processing canceled by user',basename)
return
end
basename = session.general.name;
basepath = session.general.basePath;
preferences.putativeCellType.classification_schema = parameters.preferences.putativeCellType.classification_schema;
cd(basepath)
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Moving files generated by CellExplorer (basename.*.mat) from relative spike sorting path (clusteringpath) to the basepath
% Previous version of CellExplorer would save cell_metrics and cellinfo derived files in the clusteringpath
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if parameters.transferFilesFromClusterpath && isfield(session,'spikeSorting') && ~isempty(session.spikeSorting{1}.relativePath)
fileList = dir(fullfile(basepath,session.spikeSorting{1}.relativePath,[basename, '.*.mat']));
fileListClusterpath = {fileList.name};
fileList = dir(fullfile(basepath,[basename, '.*.mat']));
fileListBasepath = {fileList.name};
[fileListToMove,~] = setdiff(fileListClusterpath,fileListBasepath);
if numel(fileListToMove)>0
disp('Moving files from cluster folder to basepath')
for i = 1:numel(fileListToMove)
disp([num2str(i) ,'. transfer: ', fileListToMove{i}]);
movefile(fullfile(basepath,session.spikeSorting{1}.relativePath,fileListToMove{i}),fullfile(basepath,fileListToMove{i}));
end
disp(['File transfer complete (', num2str(numel(fileListToMove)),' files)'])
end
DuplicatedfileList = fileListClusterpath(ismember(fileListClusterpath,fileListBasepath));
if ~isempty(DuplicatedfileList)
warning(['Files existing in both directories are not transferred. Please verify these files should not be transferred: ' DuplicatedfileList{:}])
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Getting spikes - excluding user specified- and manipulation intervals
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
sr = session.extracellular.sr;
if ~isempty(parameters.spikes)
dispLog('Using spikes provided as input',basename)
spikes{1} = parameters.spikes;
parameters.spikes = [];
else
dispLog('Getting spikes',basename)
spikes{1} = loadSpikes('session',session,'labelsToRead',preferences.loadSpikes.labelsToRead,'getWaveformsFromDat',parameters.getWaveformsFromDat,'showWaveforms',parameters.showWaveforms);
end
if parameters.getWaveformsFromDat && (~isfield(spikes{1},'processinginfo') || ~isfield(spikes{1}.processinginfo.params,'WaveformsSource') || ~strcmp(spikes{1}.processinginfo.params.WaveformsSource,'dat file') || spikes{1}.processinginfo.version<3.5 || parameters.forceReloadSpikes == true)
spikes{1} = loadSpikes('forceReload',true,'spikes',spikes{1},'session',session,'labelsToRead',preferences.loadSpikes.labelsToRead,'showWaveforms',parameters.showWaveforms);
end
spikes{1}.numcells = length(spikes{1}.times);
if ~isfield(spikes{1},'total')
spikes{1}.total = cellfun(@(X) length(X),spikes{1}.times);
end
if ~isfield(spikes{1},'spindices')
spikes{1}.spindices = generateSpinDices(spikes{1}.times);
end
if ~isfield(spikes{1},'cluID')
disp('Generating cluIDs from UIDs')
spikes{1}.cluID = spikes{1}.UID;
end
if ~isempty(parameters.restrictToIntervals)
if size(parameters.restrictToIntervals,2) ~= 2
error('restrictToIntervals has to be a Nx2 matrix')
else
dispLog('Restricting analysis to provided intervals',basename)
spikes_indices = cellfun(@(X) ce_InIntervals(X,double(parameters.restrictToIntervals)),spikes{1}.times,'UniformOutput',false);
spikes{1}.times = cellfun(@(X,Y) X(Y),spikes{1}.times,spikes_indices,'UniformOutput',false);
if isfield(spikes{1},'ts')
spikes{1}.ts = cellfun(@(X,Y) X(Y),spikes{1}.ts,spikes_indices,'UniformOutput',false);
end
if isfield(spikes{1},'ids')
spikes{1}.ids = cellfun(@(X,Y) X(Y),spikes{1}.ids,spikes_indices,'UniformOutput',false);
end
if isfield(spikes{1},'amplitudes')
spikes{1}.amplitudes = cellfun(@(X,Y) X(Y),spikes{1}.amplitudes,spikes_indices,'UniformOutput',false);
end
% Removing empty units from structure
unitsToRemove = find(cellfun(@isempty,spikes{1}.times));
fieldsToProcess = fieldnames(spikes{1});
fieldsToProcess = fieldsToProcess(structfun(@(X) (isnumeric(X) || iscell(X)) && numel(X)==numel(spikes{1}.times),spikes{1}));
for iField = 1:numel(fieldsToProcess)
spikes{1}.(fieldsToProcess{iField})(unitsToRemove) = [];
end
spikes{1}.total = cell2mat(cellfun(@(X,Y) length(X),spikes{1}.times,'UniformOutput',false));
spikes{1}.numcells = numel(spikes{1}.times);
if isempty(spikes{1}.total)
error(['CellExplorer: No spikes in the specified interval (' num2str(parameters.restrictToIntervals(1)) ,' - ', num2str(parameters.restrictToIntervals(2)),')'])
end
spikes{1}.spindices = generateSpinDices(spikes{1}.times);
end
end
if parameters.excludeManipulationIntervals
manipulationFiles = dir(fullfile(basepath,[basename,'.*.manipulation.mat']));
manipulationFiles = {manipulationFiles.name};
manipulationFiles(find(contains(manipulationFiles,parameters.ignoreManipulationTypes)))=[];
if ~isempty(manipulationFiles)
dispLog('Excluding manipulation events',basename)
for iEvents = 1:length(manipulationFiles)
eventName = strsplit(manipulationFiles{iEvents},'.');
eventName = eventName{end-2};
eventOut = load(fullfile(basepath,manipulationFiles{iEvents}));
if size(eventOut.(eventName).timestamps,2) == 2
disp([' Excluding manipulation type: ' eventName])
parameters.excludeIntervals = [parameters.excludeIntervals;eventOut.(eventName).timestamps];
else
warning('manipulation timestamps has to be a Nx2 matrix')
end
end
end
end
if ~isempty(parameters.excludeIntervals)
% Checks if intervals are formatted correctly
if size(parameters.excludeIntervals,2) ~= 2
error('excludeIntervals has to be a Nx2 matrix')
else
disp([' Excluding ',num2str(size(parameters.excludeIntervals,1)),' intervals in spikes (' num2str(sum(diff(parameters.excludeIntervals'))),' seconds)'])
spikes{2} = spikes{1};
spikes_indices = cellfun(@(X) ~ce_InIntervals(X,double(parameters.excludeIntervals)),spikes{1}.times,'UniformOutput',false);
spikes{2}.times = cellfun(@(X,Y) X(Y),spikes{1}.times,spikes_indices,'UniformOutput',false);
if isfield(spikes{1},'ts')
try
spikes{2}.ts = cellfun(@(X,Y) X(Y),spikes{1}.ts,spikes_indices,'UniformOutput',false);
end
end
if isfield(spikes{1},'ids')
spikes{2}.ids = cellfun(@(X,Y) X(Y),spikes{1}.ids,spikes_indices,'UniformOutput',false);
end
if isfield(spikes{1},'amplitudes')
spikes{2}.amplitudes = cellfun(@(X,Y) X(Y),spikes{1}.amplitudes,spikes_indices,'UniformOutput',false);
end
spikes{2}.total = cell2mat(cellfun(@(X,Y) length(X),spikes{2}.times,'UniformOutput',false));
spikes{2}.spindices = generateSpinDices(spikes{2}.times);
end
else
parameters.metricsToExcludeManipulationIntervals = {'none'};
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Initializing cell_metrics struct
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
saveAsFullfile = fullfile(basepath,[basename,'.',parameters.saveAs,'.cellinfo.',parameters.fileFormat]);
if exist(saveAsFullfile,'file') && ~parameters.forceReload
dispLog(['Loading existing metrics: ' saveAsFullfile],basename)
load(saveAsFullfile)
elseif exist(fullfile(basepath,[parameters.saveAs,'.',parameters.fileFormat]),'file') && ~parameters.forceReload
% For legacy naming convention
warning(['Loading existing legacy metrics: ' parameters.saveAs])
load(fullfile(basepath,[parameters.saveAs,'.mat']))
else
cell_metrics = [];
end
% Importing fields from spikes struct to cell_metrics
spikes_fields = fieldnames(spikes{1});
spikes_fields = setdiff(spikes_fields,{'times','ts','rawWaveform','filtWaveform', 'rawWaveform_all', 'rawWaveform_std', 'filtWaveform_all', 'filtWaveform_std', 'timeWaveform', 'timeWaveform_all', 'channels_all', 'peakVoltage_sorted', 'maxWaveform_all', 'peakVoltage_expFitLengthConstant', 'processinginfo', 'numcells', 'UID', 'sessionName'});
spikes_type = structfun(@(X) (iscell(X) || isnumeric(X)) && all(size(X) == [1,spikes{1}.numcells]), spikes{1},'uni',0);
for j = 1:numel(spikes_fields)
if spikes_type.(spikes_fields{j}) && iscell(spikes{1}.(spikes_fields{j})) && all(cellfun(@ischar, spikes{1}.(spikes_fields{j})))
cell_metrics.(spikes_fields{j}) = spikes{1}.(spikes_fields{j});
elseif spikes_type.(spikes_fields{j}) && isnumeric(spikes{1}.(spikes_fields{j}))
cell_metrics.(spikes_fields{j}) = spikes{1}.(spikes_fields{j});
end
end
if parameters.saveBackup && ~isempty(cell_metrics)
% Creating backup of existing user adjustable metrics
backupDirectory = 'revisions_cell_metrics';
% dispLog(['Creating backup of existing user adjustable metrics in subfolder ''',backupDirectory,''''],basename);
backupFields = {'labels','tags','deepSuperficial','deepSuperficialDistance','brainRegion','putativeCellType','groundTruthClassification','groups'};
temp = {};
for i = 1:length(backupFields)
if isfield(cell_metrics,backupFields{i})
temp.cell_metrics.(backupFields{i}) = cell_metrics.(backupFields{i});
end
end
if isfield(temp,'cell_metrics')
try
if ~(exist(fullfile(basepath,backupDirectory),'dir'))
mkdir(fullfile(basepath,backupDirectory));
end
save(fullfile(basepath, backupDirectory, [parameters.saveAs, '_',datestr(clock,'yyyy-mm-dd_HHMMSS'), '.mat',]),'cell_metrics','-struct', 'temp')
catch
warning('Failed to save backup data in the CellExplorer pipeline')
end
end
end
cell_metrics.general.basepath = basepath;
cell_metrics.general.basename = basename;
cell_metrics.general.cellCount = numel(spikes{1}.times);
cell_metrics.general.saveAs = parameters.saveAs;
% Saving spike times to metrics
cell_metrics.spikes.times = spikes{1}.times;
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Waveform based calculations
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if any(contains(parameters.metrics,{'waveform_metrics','all'})) && ~any(contains(parameters.excludeMetrics,{'waveform_metrics'}))
spkExclu = setSpkExclu('waveform_metrics',parameters);
bad_channels = get_bad_channels(session);
if isempty(bad_channels)
good_channels = 1:session.extracellular.nChannels;
else
good_channels = setdiff(1:session.extracellular.nChannels,bad_channels);
end
if ~all(isfield(cell_metrics,{'waveforms','peakVoltage','troughToPeak','troughtoPeakDerivative','ab_ratio'})) || ~all(isfield(cell_metrics.waveforms,{'filt','filt_all'})) || parameters.forceReload == true
dispLog('Getting waveforms',basename);
field2copy = {'filtWaveform_std','rawWaveform','rawWaveform_std','rawWaveform_all','filtWaveform_all','timeWaveform_all','channels_all','filtWaveform','timeWaveform'};
field2copyNewNames = {'filt_std','raw','raw_std','raw_all','filt_all','time_all','channels_all','filt','time'};
if parameters.getWaveformsFromDat && (any(~isfield(spikes{spkExclu},field2copy)) || parameters.forceReload == true) && (spkExclu==2 || ~isempty(parameters.restrictToIntervals))
spikes{spkExclu} = getWaveformsFromDat(spikes{spkExclu},session,'showWaveforms',parameters.showWaveforms);
end
if isfield(spikes{spkExclu},'peakVoltage')
cell_metrics.peakVoltage = spikes{spkExclu}.peakVoltage;
else
cell_metrics.peakVoltage = nan(size(spikes{spkExclu}.times));
end
if isfield(spikes{spkExclu},'peakVoltage_expFitLengthConstant')
cell_metrics.peakVoltage_expFitLengthConstant = spikes{spkExclu}.peakVoltage_expFitLengthConstant(:)';
end
for j = 1:numel(field2copy)
if isfield(spikes{spkExclu},field2copy{j})
cell_metrics.waveforms.(field2copyNewNames{j}) = spikes{spkExclu}.(field2copy{j});
end
end
if isfield(spikes{spkExclu},'maxWaveform_all')
for j = 1:cell_metrics.general.cellCount
if numel(spikes{spkExclu}.maxWaveform_all)>=j && ~isempty(spikes{spkExclu}.maxWaveform_all{j})
nChannelFit = min([preferences.waveform.trilat_nChannels,length(spikes{spkExclu}.maxWaveform_all{j}),length(session.extracellular.electrodeGroups.channels{spikes{spkExclu}.shankID(j)})]);
cell_metrics.waveforms.bestChannels{j} = spikes{spkExclu}.maxWaveform_all{j}(1:nChannelFit);
else
cell_metrics.waveforms.bestChannels{j} = [];
end
end
end
if isfield(spikes{spkExclu},'filtWaveform_all')
for j = 1:cell_metrics.general.cellCount
cell_metrics.waveforms.peakVoltage_all{j} = nan(1,session.extracellular.nChannels);
cell_metrics.waveforms.peakVoltage_all{j}(good_channels) = range(spikes{spkExclu}.filtWaveform_all{j}(good_channels,:),2);
end
end
dispLog('Calculating waveform metrics',basename);
waveform_metrics = calc_waveform_metrics(spikes{spkExclu},sr,'showFigures',parameters.showFigures);
cell_metrics.troughToPeak = waveform_metrics.troughtoPeak;
cell_metrics.troughtoPeakDerivative = waveform_metrics.derivative_TroughtoPeak;
cell_metrics.ab_ratio = waveform_metrics.ab_ratio;
cell_metrics.polarity = waveform_metrics.polarity;
% Removing outdated fields
field2remove = {'derivative_TroughtoPeak','filtWaveform','filtWaveform_std','rawWaveform','rawWaveform_std','timeWaveform'};
test = isfield(cell_metrics,field2remove);
cell_metrics = rmfield(cell_metrics,field2remove(test));
end
% Channel coordinates map, trilateration and length constant determined from waveforms across channels
if ~all(isfield(cell_metrics,{'trilat_x','trilat_y','peakVoltage_expFit'})) || parameters.forceReload == true
chanCoordsFile = fullfile(basepath,[basename,'.chanCoords.channelInfo.mat']);
if isfield(session.extracellular,'chanCoords') && isfield(session.extracellular.chanCoords,'x') && isfield(session.extracellular.chanCoords,'y') && ~isempty(session.extracellular.chanCoords.x) && ~isempty(session.extracellular.chanCoords.y)
disp(' Using existing channel coordinates')
chanCoords = session.extracellular.chanCoords;
chanCoords.x = chanCoords.x(:);
chanCoords.y = chanCoords.y(:);
elseif exist(chanCoordsFile,'file')
disp([' Loading channel coordinates from file: ' chanCoordsFile])
load(chanCoordsFile,'chanCoords');
chanCoords.x = chanCoords.x(:);
chanCoords.y = chanCoords.y(:);
session.extracellular.chanCoords = chanCoords;
else
chanCoords = {};
if exist(fullfile(basepath,'chanMap.mat'),'file') % Will look for a chanMap file with default name (compatible with KiloSort)
disp(' Importing chanCoords from chanMap.mat file (e.g. from KiloSort)')
chanMap = load(fullfile(basepath,'chanMap.mat'));
chanCoords.x = chanMap.xcoords(:);
chanCoords.y = chanMap.ycoords(:);
elseif isfield(session,'analysisTags') && isfield(session.analysisTags,'chanMapFile')
% You can use a different filename that must be specified in: session.analysisTags.chanMapFile
chanMap = load(fullfile(basepath,session.analysisTags.chanMapFile));
disp([' Loading channel coordinates from chanCoords file: ' chanMap])
chanCoords.x = chanMap.xcoords(:);
chanCoords.y = chanMap.ycoords(:);
else
disp(' Generating chanCoords')
chanCoords = generateChanCoords(session);
end
session.extracellular.chanCoords = chanCoords;
end
cell_metrics.general.chanCoords = chanCoords;
% Fit exponential
fit_eqn = fittype('a*exp(-x/b)+c','dependent',{'y'},'independent',{'x'},'coefficients',{'a','b','c'});
xdata = {};
ydata = {};
if parameters.debugMode
fig100 = figure;
handle_subfig1 = subplot(2,1,1); hold on
title(handle_subfig1,'Spike amplitude (all)'), xlabel(handle_subfig1,'Distance (µm)'), ylabel(handle_subfig1,'µV')
handle_subfig2 = subplot(2,2,3); hold on
ylabel(handle_subfig2,'Length constant (µm)'), xlabel(handle_subfig2,'Peak voltage (µV)')
handle_subfig3 = subplot(2,2,4); hold off
plot(handle_subfig3,cell_metrics.general.chanCoords.x,cell_metrics.general.chanCoords.y,'.k'), hold on
xlabel(handle_subfig3,'x position (µm)'), ylabel(handle_subfig3,'y position (µm)')
drawnow
end
for j = 1:cell_metrics.general.cellCount
if ~isnan(cell_metrics.peakVoltage(j)) && isfield(cell_metrics.waveforms,'filt_all')
% Trilateration
peakVoltage = range(cell_metrics.waveforms.filt_all{j}');
peakVoltage(bad_channels) = NaN;
filt_all = cell_metrics.waveforms.filt_all{j}';
filt_all(:,bad_channels) = 0;
[~,idx] = sort(range(filt_all),'descend');
clear filt_all
trilat_nChannels = min([preferences.waveform.trilat_nChannels,numel(peakVoltage)]);
bestChannels = cell_metrics.waveforms.channels_all{j}(idx(1:trilat_nChannels));
beta0 = [cell_metrics.general.chanCoords.x(bestChannels(1)),cell_metrics.general.chanCoords.y(bestChannels(1))]; % initial position
trilat_pos = trilat(cell_metrics.general.chanCoords.x(bestChannels),cell_metrics.general.chanCoords.y(bestChannels),peakVoltage(idx(1:trilat_nChannels)),beta0,0); % ,1,cell_metrics.waveforms.filt_all{j}(bestChannels,:)
cell_metrics.trilat_x(j) = trilat_pos(1);
cell_metrics.trilat_y(j) = trilat_pos(2);
% Length constant
x1 = cell_metrics.general.chanCoords.x;
y1 = cell_metrics.general.chanCoords.y;
u = cell_metrics.trilat_x(j);
v = cell_metrics.trilat_y(j);
[channel_distance,idx2] = sort(hypot((x1(:)-u),(y1(:)-v)));
nChannelFit = min([trilat_nChannels,length(session.extracellular.electrodeGroups.channels{spikes{spkExclu}.shankID(j)})]);
x = 1:nChannelFit;
y = peakVoltage(idx(x));
x2 = channel_distance(1:nChannelFit)';
xdata{j} = x2;
ydata{j} = y;
f0 = fit(x2',y',fit_eqn,'StartPoint',[cell_metrics.peakVoltage(j), 30, 5],'Lower',[1, 0.001, 0],'Upper',[5000, 200, 1000]);
fitCoeffValues = coeffvalues(f0);
cell_metrics.peakVoltage_expFit(j) = fitCoeffValues(2);
if parameters.debugMode && ishandle(handle_subfig1)
fig100.Name = ['Cell ',num2str(j),'/',num2str(cell_metrics.general.cellCount)];
delete(handle_subfig1.Children)
plot(handle_subfig1,xdata{j},ydata{j},'.-b'), hold on
plot(handle_subfig1,x2,fitCoeffValues(1)*exp(-x2/fitCoeffValues(2))+fitCoeffValues(3),'r'),
plot(handle_subfig2,cell_metrics.peakVoltage(j),cell_metrics.peakVoltage_expFit(j),'ok')
plot(handle_subfig3,cell_metrics.trilat_x(j),cell_metrics.trilat_y(j),'ob'),
drawnow
end
else
cell_metrics.trilat_x(j) = nan;
cell_metrics.trilat_y(j) = nan;
cell_metrics.peakVoltage_expFit(j) = nan;
xdata{j} = [];
ydata{j} = [];
end
end
if parameters.showFigures
fig1 = figure('Name', 'Length constant and Trilateration','position',[100,100,900,700],'visible','off');
movegui(fig1,'center')
subplot(2,2,1), hold on
for j = 1:cell_metrics.general.cellCount
plot(xdata{j},ydata{j})
end
title('Spike amplitude (all)'), xlabel('Distance (µm)'), ylabel('µV')
subplot(2,2,2), hold off,
histogram(cell_metrics.peakVoltage_expFit,20), xlabel('Length constant (µm)')
subplot(2,2,3), hold on
plot(cell_metrics.peakVoltage,cell_metrics.peakVoltage_expFit,'ok')
ylabel('Length constant (µm)'), xlabel('Peak voltage (µV)')
subplot(2,2,4), hold on
plot(cell_metrics.general.chanCoords.x,cell_metrics.general.chanCoords.y,'.k'), hold on
plot(cell_metrics.trilat_x,cell_metrics.trilat_y,'ob'), xlabel('x position (µm)'), ylabel('y position (µm)')
set(fig1,'visible','on')
end
end
% Common coordinate framework
ccf_file = fullfile(basepath,[basename,'.ccf.channelInfo.mat']);
if exist(ccf_file,'file') %&& (~all(isfield(cell_metrics,{'ccf_x','ccf_y','ccf_z'})) || parameters.forceReload == true)
dispLog('Importing common coordinate framework',basename);
load(ccf_file,'ccf');
if all(isfield(ccf,{'x','y','z'}))
cell_metrics.general.ccf = ccf;
cell_metrics.ccf_x = cell_metrics.general.ccf.x(cell_metrics.maxWaveformCh1)';
cell_metrics.ccf_y = cell_metrics.general.ccf.y(cell_metrics.maxWaveformCh1)';
cell_metrics.ccf_z = cell_metrics.general.ccf.z(cell_metrics.maxWaveformCh1)';
for j = 1:cell_metrics.general.cellCount
if ~isnan(cell_metrics.peakVoltage(j))
peakVoltage = range(cell_metrics.waveforms.filt_all{j}');
trilat_nChannels = min([preferences.waveform.trilat_nChannels,numel(peakVoltage)]);
[~,idx] = sort(peakVoltage,'descend');
bestChannels = cell_metrics.waveforms.channels_all{j}(idx(1:trilat_nChannels));
beta0 = [cell_metrics.general.ccf.x(bestChannels(1)),cell_metrics.general.ccf.y(bestChannels(1)),cell_metrics.general.ccf.z(bestChannels(1))]; % initial position
if isnan(beta0)
cell_metrics.ccf_x(j) = nan;
cell_metrics.ccf_y(j) = nan;
cell_metrics.ccf_z(j) = nan;
else
trilat_pos = trilat3([cell_metrics.general.ccf.x(bestChannels),cell_metrics.general.ccf.y(bestChannels),cell_metrics.general.ccf.z(bestChannels)],peakVoltage(idx(1:trilat_nChannels)),beta0,0);
cell_metrics.ccf_x(j) = trilat_pos(1);
cell_metrics.ccf_y(j) = trilat_pos(2);
cell_metrics.ccf_z(j) = trilat_pos(3);
end
else
cell_metrics.ccf_x(j) = nan;
cell_metrics.ccf_y(j) = nan;
cell_metrics.ccf_z(j) = nan;
end
end
if parameters.showFigures
figure
plot3(cell_metrics.general.ccf.x,cell_metrics.general.ccf.z,cell_metrics.general.ccf.y,'.k'), hold on
plot3(cell_metrics.ccf_x,cell_metrics.ccf_z,cell_metrics.ccf_y,'ob'),
xlabel('x ( Anterior-Posterior; µm)'), zlabel('y (Superior-Inferior; µm)'), ylabel('z (Left-Right; µm)'), axis equal, set(gca, 'ZDir','reverse')
if exist('plotBrainGrid.m','file')
plotAllenBrainGrid, hold on
end
end
end
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% PCA features based calculations: Isolation distance and L-ratio
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% This code must be updated to reflect various spike sorting formats
%
% if any(contains(parameters.metrics,{'PCA_features','all'})) && ~any(contains(parameters.excludeMetrics,{'PCA_features'}))
% spkExclu = setSpkExclu('PCA_features',parameters);
% dispLog('PCA classifications: Isolation distance, L-Ratio',basename)
% if ~all(isfield(cell_metrics,{'isolationDistance66','lRatio'})) || parameters.forceReload == true
% if strcmp(session.spikeSorting{1}.method,{'Neurosuite','KlustaKwik'})
% disp('Getting PCA features for KlustaKwik')
% PCA_features = LoadNeurosuiteFeatures(spikes,session,parameters.excludeIntervals); %(session.spikeSorting{1}.relativePath,basename,sr,parameters.excludeIntervals);
% for j = 1:cell_metrics.general.cellCount
% cell_metrics.isolationDistance(j) = PCA_features.isolationDistance(find(PCA_features.UID == spikes{spkExclu}.UID(j)));
% cell_metrics.lRatio(j) = PCA_features.lRatio(find(PCA_features.UID == spikes{spkExclu}.UID(j)));
% end
% elseif strcmp(session.spikeSorting{1}.method,{'KiloSort'})
% disp('Getting masked PCA features for KiloSort')
% [clusterIDs, unitQuality, contaminationRate] = sqKilosort.maskedClusterQuality(basepath);
% cell_metrics.unitQuality = nan(1,spikes{spkExclu}.numcells);
% cell_metrics.contaminationRate = nan(1,spikes{spkExclu}.numcells);
% for i = 1:spikes{spkExclu}.numcells
% if any(cell_metrics.cluID(i) == clusterIDs)
% idx = find(cell_metrics.cluID(i) == clusterIDs)
% cell_metrics.unitQuality(i) = unitQuality(idx);
% cell_metrics.contaminationRate(i) = contaminationRate(idx);
% end
% end
% keyboard
% elseif strcmp(session.spikeSorting{1}.method,{'MaskedKlustakwik','klusta'})
% disp('Getting masked PCA features for MaskedKlustakwik')
% [clusterIDs, unitQuality, contaminationRate] = sqKwik.maskedClusterQuality(basepath);
% keyboard
% % getPCAfeatures(session)
% % disp(' No PCAs available')
% end
% end
% end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% ACG & CCG based classification
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if any(contains(parameters.metrics,{'acg_metrics','all'})) && ~any(contains(parameters.excludeMetrics,{'acg_metrics'}))
spkExclu = setSpkExclu('acg_metrics',parameters);
if isfield(cell_metrics, 'acg') && isnumeric(cell_metrics.acg)
field2remove = {'acg','acg2'};
test = isfield(cell_metrics,field2remove);
cell_metrics = rmfield(cell_metrics,field2remove(test));
end
if ~all(isfield(cell_metrics,{'acg','thetaModulationIndex','burstIndex_Royer2012','burstIndex_Doublets','acg_tau_decay','acg_tau_rise'})) || parameters.forceReload == true
dispLog('ACG classifications: ThetaModulationIndex, BurstIndex_Royer2012, BurstIndex_Doublets',basename)
acg_metrics = calc_ACG_metrics(spikes{spkExclu},sr,'showFigures',parameters.debugMode);
cell_metrics.acg.wide = acg_metrics.acg_wide; % Wide: 1000ms wide CCG with 1ms bins
cell_metrics.acg.narrow = acg_metrics.acg_narrow; % Narrow: 100ms wide CCG with 0.5ms bins
cell_metrics.thetaModulationIndex = acg_metrics.thetaModulationIndex; % cell_tmi
cell_metrics.burstIndex_Royer2012 = acg_metrics.burstIndex_Royer2012; % cell_burstRoyer2012
cell_metrics.burstIndex_Doublets = acg_metrics.burstIndex_Doublets;
dispLog('Fitting triple exponential to ACG',basename)
fit_params = fit_ACG(acg_metrics.acg_narrow,parameters.debugMode);
cell_metrics.acg_tau_decay = fit_params.acg_tau_decay;
cell_metrics.acg_tau_rise = fit_params.acg_tau_rise;
cell_metrics.acg_c = fit_params.acg_c;
cell_metrics.acg_d = fit_params.acg_d;
cell_metrics.acg_asymptote = fit_params.acg_asymptote;
cell_metrics.acg_refrac = fit_params.acg_refrac;
cell_metrics.acg_fit_rsquare = fit_params.acg_fit_rsquare;
cell_metrics.acg_tau_burst = fit_params.acg_tau_burst;
cell_metrics.acg_h = fit_params.acg_h;
end
if ~isfield(cell_metrics,'acg') || ~isfield(cell_metrics.acg,{'log10'}) || parameters.forceReload == true
dispLog('Calculating log10 ACGs',basename)
acg = calc_logACGs(spikes{spkExclu}.times,'showFigures',parameters.showFigures);
cell_metrics.acg.log10 = acg.log10;
cell_metrics.general.acgs.log10 = acg.log10_bins;
end
if ~isfield(cell_metrics,'isi') || ~isfield(cell_metrics.isi,{'log10'}) || parameters.forceReload == true
dispLog('Calculating log10 ISIs',basename)
isi = calc_logISIs(spikes{spkExclu}.times,'showFigures',parameters.showFigures);
cell_metrics.isi.log10 = isi.log10;
cell_metrics.general.isis.log10 = isi.log10_bins;
end
if ~(isfield(preferences,'acg_metrics') && isfield(preferences.acg_metrics,'population_modIndex') && ~preferences.acg_metrics.population_modIndex) && (~isfield(cell_metrics,{'population_modIndex'}) || ~isfield(cell_metrics.general,'responseCurves') || ~isfield(cell_metrics.general.responseCurves,'meanCCG') || numel(cell_metrics.general.responseCurves.meanCCG.x_bins) ~= 51 || parameters.forceReload == true)
dispLog('Calculating population modulation index',basename)
[meanCCG,tR,population_modIndex] = detectDownStateCells(spikes{spkExclu},sr,'showFigures',parameters.showFigures);
cell_metrics.responseCurves.meanCCG = num2cell(meanCCG,1);
cell_metrics.general.responseCurves.meanCCG.x_bins = tR;
cell_metrics.population_modIndex = population_modIndex;
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Putative MonoSynaptic connections
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if any(contains(parameters.metrics,{'monoSynaptic_connections','all'})) && ~any(contains(parameters.excludeMetrics,{'monoSynaptic_connections'}))
spkExclu = setSpkExclu('monoSynaptic_connections',parameters);
dispLog('MonoSynaptic connections',basename)
if ~exist(fullfile(basepath,[basename,'.mono_res',erase(parameters.saveAs,'cell_metrics'),'.cellinfo.mat']),'file')
mono_res = ce_MonoSynConvClick(spikes{spkExclu},'includeInhibitoryConnections',parameters.includeInhibitoryConnections,'sr',sr);
if parameters.manualAdjustMonoSyn
dispLog('Loading MonoSynaptic GUI for manual adjustment',basename)
mono_res = gui_MonoSyn(mono_res);
end
save(fullfile(basepath,[basename,'.mono_res',erase(parameters.saveAs,'cell_metrics'),'.cellinfo.mat']),'mono_res','-v7.3','-nocompression');
else
disp(' Loading previous detected MonoSynaptic connections')
load(fullfile(basepath,[basename,'.mono_res',erase(parameters.saveAs,'cell_metrics'),'.cellinfo.mat']),'mono_res');
if parameters.includeInhibitoryConnections && (~isfield(mono_res,'sig_con_inhibitory') || (isfield(mono_res,'sig_con_inhibitory') && isempty(mono_res.sig_con_inhibitory_all)))
disp(' Detecting MonoSynaptic inhibitory connections')
mono_res_old = mono_res;
mono_res = ce_MonoSynConvClick(spikes{spkExclu},'includeInhibitoryConnections',parameters.includeInhibitoryConnections,'sr',sr);
mono_res.sig_con_excitatory = mono_res_old.sig_con;
mono_res.sig_con = mono_res_old.sig_con;
if parameters.manualAdjustMonoSyn
dispLog('Loading MonoSynaptic GUI for manual adjustment',basename)
mono_res = gui_MonoSyn(mono_res);
end
save(fullfile(basepath,[basename,'.mono_res',erase(parameters.saveAs,'cell_metrics'),'.cellinfo.mat']),'mono_res','-v7.3','-nocompression');
elseif parameters.forceReload == true && parameters.manualAdjustMonoSyn
mono_res = gui_MonoSyn(mono_res);
save(fullfile(basepath,[basename,'.mono_res',erase(parameters.saveAs,'cell_metrics'),'.cellinfo.mat']),'mono_res','-v7.3','-nocompression');
end
end
field2remove = {'putativeConnections'};
test = isfield(cell_metrics,field2remove);
cell_metrics = rmfield(cell_metrics,field2remove(test));
if ~isempty(mono_res.sig_con)
if isfield(mono_res,'sig_con_excitatory')
cell_metrics.putativeConnections.excitatory = mono_res.sig_con_excitatory; % Vectors with cell pairs
else
cell_metrics.putativeConnections.excitatory = mono_res.sig_con; % Vectors with cell pairs
end
if isfield(mono_res,'sig_con_inhibitory')
cell_metrics.putativeConnections.inhibitory = mono_res.sig_con_inhibitory;
else
cell_metrics.putativeConnections.inhibitory = [];
end
cell_metrics.synapticEffect = repmat({'Unknown'},1,cell_metrics.general.cellCount);
cell_metrics.synapticEffect(cell_metrics.putativeConnections.excitatory(:,1)) = repmat({'Excitatory'},1,size(cell_metrics.putativeConnections.excitatory,1)); % cell_synapticeffect ['Inhibitory','Excitatory','Unknown']
if ~isempty(cell_metrics.putativeConnections.inhibitory)
cell_metrics.synapticEffect(cell_metrics.putativeConnections.inhibitory(:,1)) = repmat({'Inhibitory'},1,size(cell_metrics.putativeConnections.inhibitory,1));
end
cell_metrics.synapticConnectionsOut = zeros(1,cell_metrics.general.cellCount);
cell_metrics.synapticConnectionsIn = zeros(1,cell_metrics.general.cellCount);
[a,b]=hist(cell_metrics.putativeConnections.excitatory(:,1),unique(cell_metrics.putativeConnections.excitatory(:,1)));
cell_metrics.synapticConnectionsOut(b) = a;
cell_metrics.synapticConnectionsOut = cell_metrics.synapticConnectionsOut(1:cell_metrics.general.cellCount);
[a,b]=hist(cell_metrics.putativeConnections.excitatory(:,2),unique(cell_metrics.putativeConnections.excitatory(:,2)));
cell_metrics.synapticConnectionsIn(b) = a;
cell_metrics.synapticConnectionsIn = cell_metrics.synapticConnectionsIn(1:cell_metrics.general.cellCount);
% Connection strength
disp(' Determining transmission probabilities')
ccg2 = mono_res.ccgR;
ccg2(isnan(ccg2)) = 0;
% Excitatory connections
for i = 1:size(cell_metrics.putativeConnections.excitatory,1)
[trans,prob,prob_uncor,pred] = ce_GetTransProb(ccg2(:,cell_metrics.putativeConnections.excitatory(i,1),cell_metrics.putativeConnections.excitatory(i,2)), spikes{spkExclu}.total(cell_metrics.putativeConnections.excitatory(i,1)), mono_res.binSize, 0.020);
cell_metrics.putativeConnections.excitatoryTransProb(i) = trans;
end
% Inhibitory connections
for i = 1:size(cell_metrics.putativeConnections.inhibitory,1)
[trans,prob,prob_uncor,pred] = ce_GetTransProb(ccg2(:,cell_metrics.putativeConnections.inhibitory(i,1),cell_metrics.putativeConnections.inhibitory(i,2)), spikes{spkExclu}.total(cell_metrics.putativeConnections.inhibitory(i,1)), mono_res.binSize, 0.020);
cell_metrics.putativeConnections.inhibitoryTransProb(i) = trans;
end
else
cell_metrics.putativeConnections.excitatory = [];
cell_metrics.putativeConnections.inhibitory = [];
cell_metrics.synapticConnectionsOut = zeros(1,cell_metrics.general.cellCount);
cell_metrics.synapticConnectionsIn = zeros(1,cell_metrics.general.cellCount);
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Deep-Superficial by ripple polarity reversal
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if any(contains(parameters.metrics,{'deepSuperficial','all'})) && ~any(contains(parameters.excludeMetrics,{'deepSuperficial'}))
spkExclu = setSpkExclu('deepSuperficial',parameters);
if (~exist(fullfile(basepath,[basename,'.ripples.events.mat']),'file')) && isfield(session,'channelTags') && isfield(session.channelTags,'Ripple') && isnumeric(session.channelTags.Ripple.channels)
dispLog('Finding ripples',basename)
if ~exist(fullfile(session.general.basePath,[session.general.name, '.lfp']),'file')
disp('Creating lfp file')
ce_LFPfromDat(session)
end
if isfield(session.channelTags,'RippleNoise')
disp(' Using RippleNoise reference channel')
RippleNoiseChannel = double(LoadBinary([basename, '.lfp'],'nChannels',session.extracellular.nChannels,'channels',session.channelTags.RippleNoise.channels,'precision','int16','frequency',session.extracellular.srLfp)); % 0.000050354 *
ripples = bz_FindRipples(basepath,session.channelTags.Ripple.channels-1,'durations',preferences.deepSuperficial.ripples_durations,'passband',preferences.deepSuperficial.ripples_passband,'noise',RippleNoiseChannel);
else
ripples = ce_FindRipples(session,'durations',preferences.deepSuperficial.ripples_durations,'passband',preferences.deepSuperficial.ripples_passband);
end
end
deepSuperficial_file = fullfile(basepath, [basename,'.deepSuperficialfromRipple.channelinfo.mat']);
if exist(fullfile(basepath,[basename,'.ripples.events.mat']),'file') && (~all(isfield(cell_metrics,{'deepSuperficial','deepSuperficialDistance'})) || parameters.forceReload == true)
dispLog('Deep-Superficial by ripple polarity reversal',basename)
if ~exist(deepSuperficial_file,'file')
if ~isfield(session.extracellular,'chanCoords')
session.extracellular.chanCoords = generateChanCoords(session);
end
classification_DeepSuperficial(session);
end
load(deepSuperficial_file,'deepSuperficialfromRipple')
cell_metrics.general.SWR = deepSuperficialfromRipple;
deepSuperficial_ChDistance = deepSuperficialfromRipple.channelDistance; %
deepSuperficial_ChClass = deepSuperficialfromRipple.channelClass;% cell_deep_superficial
cell_metrics.general.deepSuperficial_file = deepSuperficial_file;
for j = 1:cell_metrics.general.cellCount
cell_metrics.deepSuperficial(j) = deepSuperficial_ChClass(spikes{spkExclu}.maxWaveformCh1(j)); % cell_deep_superficial OK
cell_metrics.deepSuperficialDistance(j) = deepSuperficial_ChDistance(spikes{spkExclu}.maxWaveformCh1(j)); % cell_deep_superficial_distance
end
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Theta related activity
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if any(contains(parameters.metrics,{'theta_metrics','all'})) && ~any(contains(parameters.excludeMetrics,{'theta_metrics'})) && exist(fullfile(basepath,[basename,'.animal.behavior.mat']),'file') && isfield(session.channelTags,'Theta') %&& (~isfield(cell_metrics,'thetaEntrainment') || parameters.forceReload == true)
spkExclu = setSpkExclu('theta_metrics',parameters);
dispLog('Theta metrics',basename);
InstantaneousTheta = calcInstantaneousTheta2(session);
load(fullfile(basepath,[basename,'.animal.behavior.mat']),'animal');
theta_bins = preferences.theta.bins;
cell_metrics.thetaPhasePeak = nan(1,cell_metrics.general.cellCount);
cell_metrics.thetaPhaseTrough = nan(1,cell_metrics.general.cellCount);
% cell_metrics.responseCurves.thetaPhase = nan(length(theta_bins)-1,cell_metrics.general.cellCount);
cell_metrics.thetaEntrainment = nan(1,cell_metrics.general.cellCount);
spikes2 = spikes{spkExclu};
if isfield(cell_metrics,'thetaPhaseResponse')
cell_metrics = rmfield(cell_metrics,'thetaPhaseResponse');
end
for j = 1:size(spikes{spkExclu}.times,2)
Theta_channel = session.channelTags.Theta.channels(1);
spikes2.ts{j} = spikes2.ts{j}(spikes{spkExclu}.times{j} < length(InstantaneousTheta.signal_phase{Theta_channel})/session.extracellular.srLfp);
spikes2.times{j} = spikes2.times{j}(spikes{spkExclu}.times{j} < length(InstantaneousTheta.signal_phase{Theta_channel})/session.extracellular.srLfp);
spikes2.ts_eeg{j} = ceil(spikes2.ts{j}*session.extracellular.srLfp/session.extracellular.sr);
spikes2.theta_phase{j} = InstantaneousTheta.signal_phase{Theta_channel}(spikes2.ts_eeg{j});
spikes2.speed{j} = interp1(animal.time,animal.speed,spikes2.times{j});
if sum(spikes2.speed{j} > 10)> preferences.theta.min_spikes % only calculated if the unit has above min_spikes (default: 500)
[counts,centers] = histcounts(spikes2.theta_phase{j}(spikes2.speed{j} > preferences.theta.speed_threshold),theta_bins, 'Normalization', 'probability');
counts = nanconv(counts,[1,1,1,1,1]/5,'edge');
[~,tem2] = max(counts);
[~,tem3] = min(counts);
cell_metrics.responseCurves.thetaPhase{j} = counts(:);
cell_metrics.thetaPhasePeak(j) = centers(tem2)+diff(centers([1,2]))/2;
cell_metrics.thetaPhaseTrough(j) = centers(tem3)+diff(centers([1,2]))/2;
cell_metrics.thetaEntrainment(j) = max(counts)/min(counts);
else
cell_metrics.responseCurves.thetaPhase{j} = nan(length(theta_bins)-1,1);
end
end
cell_metrics.general.responseCurves.thetaPhase.x_bins = theta_bins(1:end-1)+diff(theta_bins([1,2]))/2;
if parameters.showFigures
figure, subplot(2,2,1)
plot(cell_metrics.general.responseCurves.thetaPhase.x_bins,horzcat(cell_metrics.responseCurves.thetaPhase{:})),title('Theta entrainment during locomotion'), xlim([-1,1]*pi)
subplot(2,2,2)
plot(cell_metrics.thetaPhaseTrough,cell_metrics.thetaPhasePeak,'o'),xlabel('Trough'),ylabel('Peak')
subplot(2,2,3)
histogram(cell_metrics.thetaEntrainment,30),title('Theta entrainment')
subplot(2,2,4)
histogram(cell_metrics.thetaPhaseTrough,[-1:0.2:1]*pi),title('Theta trough and peak'), hold on
histogram(cell_metrics.thetaPhasePeak,[-1:0.2:1]*pi), legend({'Trough','Peak'})
end
end
%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Spatial related metrics
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if any(contains(parameters.metrics,{'spatial_metrics','all'})) && ~any(contains(parameters.excludeMetrics,{'spatial_metrics'}))
spkExclu = setSpkExclu('spatial_metrics',parameters);
if ~isempty(dir(fullfile(basepath,[basename,'.*.firingRateMap.mat'])))
dispLog('Spatial metrics',basename);
end
% Cleaning legacy fields
field2remove = {'firingRateMap_CoolingStates','firingRateMap_LeftRight','firingRateMaps','firingRateMap','firing_rate_map_states','firing_rate_map','placecell_stability','SpatialCoherence','place_cell','placefield_count','placefield_peak_rate','FiringRateMap','FiringRateMap_CoolingStates','FiringRateMap_StimStates','FiringRateMap_LeftRight'};
test = isfield(cell_metrics,field2remove);
cell_metrics = rmfield(cell_metrics,field2remove(test));
% General firing rate map
if exist(fullfile(basepath,[basename,'.ratemap.firingRateMap.mat']),'file')
temp2 = load(fullfile(basepath,[basename,'.ratemap.firingRateMap.mat']));
if isfield(temp2,'ratemap')
disp(' Loaded ratemap succesfully');
firingRateMap = temp2.ratemap;
if cell_metrics.general.cellCount == length(firingRateMap.map)
cell_metrics.firingRateMaps.firingRateMap = firingRateMap.map;
if isfield(firingRateMap,'x_bins')
cell_metrics.general.firingRateMaps.firingRateMap.x_bins = firingRateMap.x_bins;
end
if isfield(firingRateMap,'boundaries')
cell_metrics.general.firingRateMaps.firingRateMap.boundaries = firingRateMap.boundaries;
end
for j = 1:cell_metrics.general.cellCount
cell_metrics.spatialPeakRate(j) = max(firingRateMap.map{j});
% Finding place cells/fields
temp = place_cell_condition(firingRateMap.map{j});
cell_metrics.spatialCoherence(j) = temp.SpatialCoherence;
cell_metrics.placeCell(j) = temp.condition;
cell_metrics.placeFieldsCount(j) = temp.placefield_count;
temp3 = cumsum(sort(firingRateMap.map{j},'descend'));
if ~all(temp3==0 | isnan(temp3))
cell_metrics.spatialCoverageIndex(j) = find(temp3>0.75*temp3(end),1)/(length(temp3)*0.75); % Spatial coverage index (Royer, NN 2012)