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playground.m
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%% Hemidiff in V (V-V_mirror) - keep for future reference
% (PUT THIS INTO A FUNCTION)
U_r = reshape(U_master(:,:,1:n_vs),[],n_vs);
U_mirror_r = reshape(AP_reflect_widefield(U_master(:,:,1:n_vs)),[],n_vs);
mirror_matrix = U_r'*U_mirror_r;
fluor_allcat_deconv_mirror = reshape(transpose( ...
mirror_matrix*reshape(fluor_allcat_deconv,[],n_vs)'),size(fluor_allcat_deconv));
% sanity check: plot regular and mirrored
use_trials = trial_contrast_allcat > 0 & trial_side_allcat == 1 & ...
trial_choice_allcat == -1 & move_t > 0.1 & move_t < 0.3;
V_mean = squeeze(nanmean(fluor_allcat(use_trials,:,:),1))';
V_mirror_mean = squeeze(nanmean(fluor_allcat_mirror(use_trials,:,:),1))';
px_mean = svdFrameReconstruct(U_master(:,:,1:n_vs),V_mean);
px_mirror_mean = svdFrameReconstruct(U_master(:,:,1:n_vs),V_mirror_mean);
AP_imscroll([px_mean,px_mirror_mean],t);
caxis([-prctile(abs(px_mean(:)),99),prctile(abs(px_mean(:)),99)]);
colormap(brewermap([],'*RdBu'));
axis image;
V_hemidiff = V_mean - V_mirror_mean;
px_hemidiff = svdFrameReconstruct(U_master(:,:,1:n_vs),V_hemidiff);
AP_imscroll(px_hemidiff,t);
caxis([-prctile(abs(px_hemidiff(:)),99),prctile(abs(px_hemidiff(:)),99)]);
colormap(brewermap([],'*RdBu'));
axis image;
% sanity check
use_trials = trial_contrast_allcat > 0 & trial_side_allcat == 1 & ...
trial_choice_allcat == -1 & move_t > 0.1 & move_t < 0.3;
V_check = squeeze(nanmean(fluor_allcat_downsamp_filt(use_trials,:,:),1))';
px_check = svdFrameReconstruct(U_master(:,:,1:n_vs),V_check);
AP_imscroll(px_check,t);
caxis([-prctile(abs(px_check(:)),100),prctile(abs(px_check(:)),100)]);
colormap(brewermap([],'*RdBu'));
axis image;
AP_reference_outline('ccf_aligned','k');
% compatible with code:
mirror_matrix = reshape(U_master(:,:,1:n_vs),[],n_vs)'* ...
reshape(AP_reflect_widefield(U_master(:,:,1:n_vs)),[],n_vs);
fluor_allcat_downsamp_mirror = reshape(transpose( ...
mirror_matrix*reshape(fluor_allcat_downsamp,[],n_vs)'),size(fluor_allcat_downsamp));
fluor_allcat_downsamp = fluor_allcat_downsamp - fluor_allcat_downsamp_mirror;
%% Plot template waveform across channels
curr_template = 9;
figure; axes('YDir','reverse','visible','off'); hold on;
p = arrayfun(@(x) plot(0,0,'k','linewidth',2),1:size(templates,3));
template_xscale = 3;
template_yscale = 0.5;
template_y = permute(mean(templates(curr_template,:,:),1),[3,2,1]);
template_y = -template_y*template_yscale + channel_positions(:,2);
template_x = (1:size(templates,2)) + channel_positions(:,1)*template_xscale;
template_channel_amp = squeeze(range(templates(curr_template,:,:),2));
template_thresh = max(template_channel_amp)*0.2;
template_use_channels = template_channel_amp > template_thresh;
[~,max_channel] = max(max(abs(templates(curr_template,:,:)),[],2),[],3);
arrayfun(@(ch) set(p(ch),'XData',template_x(ch,:),'YData',template_y(ch,:)),1:size(templates,3));
arrayfun(@(ch) set(p(ch),'Color','r'),find(template_use_channels));
arrayfun(@(ch) set(p(ch),'Color','k'),find(~template_use_channels));
set(p(max_channel),'Color','b');
yrange = range(channel_positions(:,2))*0.03.*[-1,1];
ylim(channel_positions(max_channel,2) + yrange);
title(curr_template);
%% Waveform grab test
animal = 'AP024';
day = '2017-09-29';
experiment = 1;
[kilosort_path,kilsort_exists] = AP_cortexlab_filename(animal,day,[],'ephys');
[ephys_ap_filename,ephys_ap_exists] = AP_cortexlab_filename(animal,day,[],'ephys_ap');
[ephys_path,ephys_exists] = AP_cortexlab_filename(animal,day,[],'ephys_dir');
spike_times = readNPY([kilosort_path filesep 'spike_times.npy']);
spike_clusters = readNPY([kilosort_path filesep 'spike_clusters.npy']);
templates_whitened = readNPY([kilosort_path filesep 'templates.npy']);
channel_positions = readNPY([kilosort_path filesep 'channel_positions.npy']);
channel_map = readNPY([kilosort_path filesep 'channel_map.npy']);
winv = readNPY([kilosort_path filesep 'whitening_mat_inv.npy']);
% Default channel map/positions are from end: make from surface
channel_positions(:,2) = max(channel_positions(:,2)) - channel_positions(:,2);
% Unwhiten templates
templates = zeros(size(templates_whitened));
for t = 1:size(templates_whitened,1)
templates(t,:,:) = squeeze(templates_whitened(t,:,:))*winv;
end
gwfparams.dataDir = kilosort_path;
gwfparams.fileName = ephys_ap_filename;
gwfparams.dataType = 'int16';
gwfparams.nCh = 384;
gwfparams.wfWin = [-40 80];
gwfparams.nWf = 200;
gwfparams.spikeTimes = spike_times;
gwfparams.spikeClusters = spike_clusters;
wf = getWaveForms(gwfparams);
cluster_mean_waveforms = permute(wf.waveFormsMean,[1,3,2]);
% wf_car_mean = permute(nanmean(wf.waveForms - nanmedian(wf.waveForms,2),2),[1,4,3,2]);
wf_car_mean = permute(wf.waveFormsMean,[1,3,2]);
curr_template = 1;
figure; hold on; axis off;
p = arrayfun(@(x) plot(0,0,'k','linewidth',2),1:size(wf_car_mean,3));
yscale = 40;
xscale = 7;
y = permute(wf_car_mean(curr_template,:,:),[3,2,1]);
y = y - channel_positions(:,2)*yscale;
x = (1:size(wf_car_mean,2)) + channel_positions(:,1)*xscale;
template_channel_amp = squeeze(range(wf_car_mean(curr_template,:,:),2));
template_thresh = max(template_channel_amp)*0.2;
template_use_channels = template_channel_amp > template_thresh;
[~,max_channel] = max(max(abs(wf_car_mean(curr_template,:,:)),[],2),[],3);
arrayfun(@(ch) set(p(ch),'XData',x(ch,:),'YData',y(ch,:)),1:size(wf_car_mean,3));
arrayfun(@(ch) set(p(ch),'Color','r'),find(template_use_channels));
arrayfun(@(ch) set(p(ch),'Color','k'),find(~template_use_channels));
set(p(max_channel),'Color','b');
yrange = range(channel_positions(:,2))*yscale*0.08.*[-1,1];
ylim([-channel_positions(max_channel,2)*yscale + yrange]);
title(curr_template);
%% Plot mean waveforms from save_phy
spike_clusters = readNPY(['C:\data_temp\phy\spike_clusters.npy']);
unique_clusters = unique(spike_clusters);
channel_positions = readNPY(['C:\data_temp\phy\channel_positions.npy']);
cluster_mean_waveforms_norm = ...
(cluster_mean_waveforms-cluster_mean_waveforms(:,1,:))./ ...
abs(max(max(cluster_mean_waveforms,[],2),[],3));
figure; hold on;
p = arrayfun(@(x) plot(0,0,'k'),1:size(cluster_mean_waveforms_norm,3));
for curr_template = 1:size(cluster_mean_waveforms_norm,1)
y = permute(cluster_mean_waveforms_norm(curr_template,:,:),[3,2,1]);
y = y + (max(channel_positions(:,2)) - channel_positions(:,2))/100;
x = (1:size(cluster_mean_waveforms_norm,2)) + channel_positions(:,1)*8;
nonzero_channels = squeeze(any(cluster_mean_waveforms_norm(curr_template,:,:),2));
[~,max_channel] = max(max(abs(cluster_mean_waveforms_norm(curr_template,:,:)),[],2),[],3);
arrayfun(@(ch) set(p(ch),'XData',x(ch,:),'YData',y(ch,:)),1:size(cluster_mean_waveforms_norm,3));
arrayfun(@(ch) set(p(ch),'Color','r'),find(nonzero_channels));
arrayfun(@(ch) set(p(ch),'Color','k'),find(~nonzero_channels));
set(p(max_channel),'Color','b');
% ylim([min(reshape(y(nonzero_channels,:),[],1)), ...
% max(reshape(y(nonzero_channels,:),[],1))]);
curr_cluster = unique_clusters(curr_template);
title(curr_cluster);
waitforbuttonpress;
end
%% (to plot fluorescence by trial group)
% Get conditions for each trial, plot selected
contrasts = [0,0.06,0.125,0.25,0.5,1];
sides = [-1,1];
choices = [-1,1];
contrast_side_col = colormap_BlueWhiteRed(5);
contrast_side_col(6,:) = 0;
contrast_side_val = unique(sort([-contrasts,contrasts]))';
% contrast, side, choice
plot_conditions = ...
[contrasts,contrasts; ...
-ones(1,7),ones(1,5); ...
ones(1,6),-ones(1,6)]';
% plot_conditions = ...
% [contrasts(2:end),contrasts(2:end); ...
% -ones(1,5),ones(1,5); ...
% ones(1,5),-ones(1,5)]';
% plot_conditions = ...
% [contrasts(2:end),contrasts(2:end); ...
% -ones(1,5),ones(1,5); ...
% -ones(1,5),ones(1,5)]';
% plot_conditions = ...
% [1,0,1,1,0,1; ...
% 1,-1,-1,-1,-1,1; ...
% -1,-1,-1,1,1,1]';
% plot_conditions = ...
% [1,0,1,1; ...
% -1,-1,1,-1; ...
% -1,-1,-1,1]';
% plot_conditions = ...
% [1; ...
% 1,; ...
% -1]';
% plot_conditions = ...
% [0.06,0.06,0.06; ...
% 1,1,-1; ...
% -1,1,-1]';
use_rxn = move_t > 0 & move_t < 0.5;
[~,plot_id] = ismember( ...
[trial_contrast_allcat,trial_side_allcat,trial_choice_allcat], ...
plot_conditions,'rows');
spacing = nanstd(fluor_roi_deconv(:))*10;
% fluor_roi_deconv_hemidiff = ...
% (fluor_roi_deconv(:,:,1:size(wf_roi,1))-fluor_roi_taskpred_reduced(:,:,1:size(wf_roi,1),2)) - ...
% (fluor_roi_deconv(:,:,size(wf_roi,1)+1:end)-fluor_roi_taskpred_reduced(:,:,size(wf_roi,1)+1:end,2));
% fluor_roi_deconv_hemidiff = fluor_roi_deconv(:,:,1:size(wf_roi,1)) - fluor_roi_deconv(:,:,size(wf_roi,1)+1:end);
figure; hold on;
for curr_plot_condition = 1:size(plot_conditions,1)
curr_trials = plot_id == curr_plot_condition & use_rxn;
% curr_data = fluor_roi_deconv_hemidiff(curr_trials,:,:);
curr_data = fluor_roi_deconv(curr_trials,:,:) - fluor_roi_taskpred_reduced(curr_trials,:,:,2);
% curr_data = fluor_roi_deconv(curr_trials,:,:);
% Re-align to movement onset
t_leeway = 0.5;
leeway_samples = round(t_leeway*sample_rate);
curr_move_idx = move_idx(curr_trials);
for i = 1:size(curr_data,1)
curr_data(i,:,:) = circshift(curr_data(i,:,:),-curr_move_idx(i)+leeway_samples,2);
end
curr_data_mean = squeeze(nanmean(curr_data,1));
curr_contrast_side = max(trial_contrast_allcat(curr_trials))*sign(max(trial_side_allcat(curr_trials)));
contrast_side_idx = find(curr_contrast_side == contrast_side_val);
curr_col = contrast_side_col(contrast_side_idx,:);
curr_linewidth = mean(trial_choice_allcat(curr_trials))+2;
if curr_contrast_side == 0
switch max(trial_choice_allcat(curr_trials))
case -1
curr_col = 'm';
case 1
curr_col = 'c';
end
end
AP_stackplot(curr_data_mean,t,spacing,false,curr_col,{wf_roi.area});
end
line([0,0],ylim,'color','k');
xlabel('Time from stim');
title('Widefield ROI');
% (rank difference)
trial_groups = {'Stim','Move onset','Outcome'};
figure;
for curr_group = 1:length(trial_groups)
curr_regressor_idx = strcmp(trial_groups{curr_group},regressor_labels);
curr_fluor = fluor_roi_deconv - fluor_roi_taskpred_reduced(:,:,:,curr_regressor_idx);
% curr_fluor = curr_fluor(:,:,1:size(wf_roi,1)) - curr_fluor(:,:,size(wf_roi,1)+1:end);
% (if movement, align to move onset)
if strcmp(trial_groups{curr_group},'Move onset')
t_leeway = -t(1);
leeway_samples = round(t_leeway*(sample_rate));
for i = 1:size(fluor_allcat_deconv,1)
curr_fluor(i,:,:) = circshift(curr_fluor(i,:,:),-move_idx(i)+leeway_samples,2);
end
end
% Set trials and grouping (-1,1) to use
switch trial_groups{curr_group}
case 'Stim'
use_trials = move_t < 1;
trial_group = trial_side_allcat;
group1 = 1;
group2 = -1;
case 'Move onset'
use_trials = move_t < 1 & trial_side_allcat == 1;
trial_group = trial_choice_allcat;
group1 = -1;
group2 = 1;
case 'Reward'
use_trials = move_t < 1;
trial_group = trial_choice_allcat == -trial_side_allcat;
group1 = 1;
group2 = 0;
end
act_rank = tiedrank(curr_fluor(use_trials,:,:));
act_rank_difference = squeeze(( ...
nanmean(act_rank(trial_group(use_trials) == group1,:,:),1) - ...
nanmean(act_rank(trial_group(use_trials) == group2,:,:),1))./sum(use_trials));
p1 = subplot(1,length(trial_groups),curr_group);
hold on; set(gca,'ColorOrder',jet(size(curr_fluor,3)));
plot(t,act_rank_difference,'linewidth',2);
axis tight; line([0,0],ylim,'color','k');
xlabel('Time');
ylabel('Mean rank difference');
title([trial_groups{curr_group}]);
end
% Get velocity and bins
% (to use max velocity regardless of final choice)
max_vel = AP_signed_max(wheel_velocity_allcat_move(:,t > 0 & t < 0.5),2);
% (to use summed velocity regardless of final choice)
% max_vel = sum(wheel_velocity_allcat_move(:,t > 0 & t < 0.5),2);
% (to use maximum cumulative velocity regardless of final choice)
% max_vel = AP_signed_max(cumsum(wheel_velocity_allcat_move(:,t > 0 & t < 0.5),2),2);
n_vel_bins = 3;
vel_amp_edges = prctile(abs(max_vel),linspace(0,100,n_vel_bins+1));
% vel_amp_edges = linspace(prctile(abs(max_vel),10),prctile(abs(max_vel),90),n_vel_bins);
vel_amp_edges = sort([vel_amp_edges,-vel_amp_edges]);
vel_amp_bins = discretize(max_vel,vel_amp_edges);
vel_amp_bins(vel_amp_bins == n_vel_bins+1) = NaN;
% Make move-aligned fluorescence
fluor_allcat_deconv_move = fluor_allcat_deconv;
t_leeway = -t(1);
leeway_samples = round(t_leeway*(sample_rate));
for i = 1:size(fluor_allcat_deconv,1)
fluor_allcat_deconv_move(i,:,:,:) = circshift(fluor_allcat_deconv_move(i,:,:,:),-move_idx(i)+leeway_samples,2);
end
% Plot pixel fluorescence
plot_rxn_time = [0.5,Inf];
% plot_trials = ...
% trial_contrast_allcat > 0 & ...
% trial_side_allcat == 1 & ...
% trial_choice_allcat == -1 & ...
% move_t >= plot_rxn_time(1) & ...
% move_t <= plot_rxn_time(2);
plot_trials = ...
trial_stim_allcat > 0 & ...
trial_choice_allcat == -1 & ...
move_t >= plot_rxn_time(1) & ...
move_t <= plot_rxn_time(2);
% plot_trials = ...
% trial_contrast_allcat == 0 & ...
% vel_amp_bins == 1 & ...
% move_t >= plot_rxn_time(1) & ...
% move_t <= plot_rxn_time(2);
% plot_trials = ...
% trial_contrast_allcat > 0 & ...
% trial_side_allcat == 1 & ...
% vel_amp_bins == 1 & ...
% move_t >= plot_rxn_time(1) & ...
% move_t <= plot_rxn_time(2);
% plot_px = svdFrameReconstruct(U_master(:,:,1:n_vs), ...
% squeeze(nanmean( ...
% fluor_allcat_deconv_move(plot_trials,:,:) - ...
% fluor_taskpred_reduced_allcat_move(plot_trials,:,:,2),1))');
plot_px = svdFrameReconstruct(U_master(:,:,1:n_vs), ...
squeeze(nanmean(fluor_allcat_deconv(plot_trials,:,:)))');
% plot_px = svdFrameReconstruct(U_master(:,:,1:n_vs), ...
% squeeze(nanmean(fluor_allcat_deconv_move(plot_trials,:,:)))');
AP_imscroll(plot_px,t);
axis image;
caxis([-max(abs(caxis)),max(abs(caxis))]);
colormap(brewermap([],'PrGn'));
AP_reference_outline('ccf_aligned','k');
% Plot fluorescence mirroring and combining sides (side = ipsi/contra now)
mirror_matrix = reshape(U_master(:,:,1:n_vs),[],n_vs)'* ...
reshape(AP_reflect_widefield(U_master(:,:,1:n_vs)),[],n_vs);
fluor_allcat_deconv_mirror = reshape(transpose( ...
mirror_matrix*reshape(fluor_allcat_deconv,[],n_vs)'),size(fluor_allcat_deconv));
fluor_allcat_deconv_move_mirror = reshape(transpose( ...
mirror_matrix*reshape(fluor_allcat_deconv_move,[],n_vs)'),size(fluor_allcat_deconv_move));
% fluor_allcat_deconv_move_mirror = reshape(transpose( ...
% mirror_matrix*reshape(fluor_allcat_deconv_move - ...
% fluor_taskpred_reduced_allcat_move(:,:,:,2), ...
% [],n_vs)'),size(fluor_allcat_deconv_move));
plot_rxn_time = [0,0.5];
plot_side = 1;
plot_choice = -1;
plot_contrast = trial_contrast_allcat > 0;
plot_move_t = move_t >= plot_rxn_time(1) & move_t <= plot_rxn_time(2);
curr_trials_standard = trial_side_allcat == plot_side & ...
trial_choice_allcat == plot_choice & ...
plot_contrast & plot_move_t;
% vel_amp_bins == 3;
curr_trials_mirror = trial_side_allcat == -plot_side & ...
trial_choice_allcat == -plot_choice & ...
plot_contrast & plot_move_t;
% vel_amp_bins == 5;
curr_px = svdFrameReconstruct(U_master(:,:,1:n_vs),squeeze(nanmean( ...
[fluor_allcat_deconv_move(curr_trials_standard,:,:); ...
fluor_allcat_deconv_move_mirror(curr_trials_mirror,:,:)],1))');
% curr_px = svdFrameReconstruct(U_master(:,:,1:n_vs),squeeze(nanmean( ...
% [fluor_allcat_deconv_move(curr_trials_standard,:,:); ...
% fluor_allcat_deconv_move_mirror(curr_trials_mirror,:,:)],1))');
% curr_px = svdFrameReconstruct(U_master(:,:,1:n_vs),squeeze(nanmean( ...
% [fluor_allcat_deconv_move(curr_trials_standard,:,:)-fluor_taskpred_reduced_allcat_move(curr_trials_standard,:,:,2); ...
% fluor_allcat_deconv_move_mirror(curr_trials_mirror,:,:)],1))');
AP_imscroll(curr_px,t);
axis image;
caxis([-max(abs(caxis)),max(abs(caxis))]);
colormap(brewermap([],'*RdBu'));
AP_reference_outline('ccf_aligned','k');
% (for passive)
% Get trials with movement during stim to exclude
wheel_thresh = 0.025;
quiescent_trials = ~any(abs(wheel_allcat(:,t >= 0 & t <= 0.5)) > wheel_thresh,2);
fluor_stim = nan(length(unique(D_allcat.stimulus)),length(t),n_vs);
for curr_v = 1:n_vs
fluor_stim(:,:,curr_v) = grpstats(fluor_allcat_deconv(quiescent_trials,:,curr_v),D_allcat.stimulus(quiescent_trials),'nanmean');
end
fluor_stim_px = cell2mat(permute(arrayfun(@(x) svdFrameReconstruct(U_master(:,:,1:n_vs), ...
squeeze(fluor_stim(x,:,:))'),1:length(unique(D_allcat.stimulus)),'uni',false),[1,3,4,2]));
AP_imscroll(fluor_stim_px,t);
axis image;
caxis([-max(abs(caxis)),max(abs(caxis))]);
colormap(brewermap([],'*RdBu'));
AP_reference_outline('ccf_aligned','k');
wheel_thresh = 0.025;
quiescent_trials = ~any(abs(wheel_allcat(:,t >= 0 & t <= 0.5)) > wheel_thresh,2);
plot_trials = quiescent_trials & trial_contrastside_allcat == 1;
plot_px = svdFrameReconstruct(U_master(:,:,1:n_vs), ...
squeeze(nanmean(fluor_allcat_deconv(plot_trials,:,:)))');
AP_imscroll(plot_px,t);
axis image;
caxis([-max(abs(caxis)),max(abs(caxis))]);
colormap(brewermap([],'*RdBu'));
AP_reference_outline('ccf_aligned','k');
%% Cortex -> Striatum regression around select events
clear all
disp('Choiceworld trial activity')
n_aligned_depths = 4;
% Regression parameters
regression_params.use_svs = 1:50;
regression_params.skip_seconds = 20;
regression_params.upsample_factor = 1;
regression_params.kernel_t = [-0.5,0.5];
regression_params.zs = [false,false];
regression_params.cvfold = 5;
regression_params.use_constant = true;
animals = {'AP024','AP025','AP026','AP027','AP028','AP029'};
% Initialize all saved variables for indexing
fluor_all = cell(1,1);
mua_all = cell(1,1);
mua_ctxpred_all = cell(1,1);
mua_taskpred_k_all = cell(1,1);
mua_taskpred_all = cell(1,1);
mua_taskpred_reduced_all = cell(1,1);
mua_ctxpred_taskpred_k_all = cell(1,1);
mua_ctxpred_taskpred_all = cell(1,1);
mua_ctxpred_taskpred_reduced_all = cell(1,1);
fluor_taskpred_k_all = cell(1,1);
fluor_taskpred_all = cell(1,1);
fluor_taskpred_reduced_all = cell(1,1);
wheel_ctxpred_all = cell(1,1);
ctx_str_k_all = cell(1,1);
ctx_wheel_k_all = cell(1,1);
wheel_all = cell(1,1);
outcome_all = cell(1,1);
D_all = cell(1,1);
for curr_animal = 1:length(animals)
animal = animals{curr_animal};
protocol = 'vanillaChoiceworld';
experiments = AP_find_experiments(animal,protocol);
experiments = experiments([experiments.imaging] & [experiments.ephys]);
load_parts.cam = false;
load_parts.imaging = true;
load_parts.ephys = true;
disp(['Loading ' animal]);
for curr_day = 1:length(experiments)
day = experiments(curr_day).day;
experiment = experiments(curr_day).experiment;
% Load experiment
str_align = 'kernel';
AP_load_experiment;
% Prepare fluorescence
% Convert U to master U
load('C:\Users\Andrew\OneDrive for Business\Documents\CarandiniHarrisLab\analysis\wf_ephys_choiceworld\wf_processing\wf_alignment\U_master.mat');
Udf_aligned = single(AP_align_widefield(animal,day,Udf));
fVdf_recast = ChangeU(Udf_aligned,fVdf,U_master);
% Set components to keep
use_components = 1:200;
% (aligned striatum depths)
n_depths = n_aligned_depths;
depth_group = aligned_str_depth_group;
% Get event-aligned activity
raster_window = [-0.5,2];
upsample_factor = 1;
raster_sample_rate = 1/(framerate*upsample_factor);
t = raster_window(1):raster_sample_rate:raster_window(2);
% Align (only to stim)
use_align = stimOn_times;
use_align(isnan(use_align)) = 0;
t_peri_event = bsxfun(@plus,use_align,t);
%%% Fluorescence
event_aligned_V = ...
interp1(frame_t,fVdf_recast(use_components,:)',t_peri_event);
%%% MUA
event_aligned_mua = nan(length(stimOn_times),length(t),n_depths);
t_bins = [t_peri_event-raster_sample_rate/2,t_peri_event(:,end)+raster_sample_rate/2];
for curr_depth = 1:n_depths
curr_spikes = spike_times_timeline(depth_group == curr_depth);
% (for only msns in depth group)
% curr_spikes = spike_times_timeline(depth_group == curr_depth & ...
% ismember(spike_templates,find(msn)));
event_aligned_mua(:,:,curr_depth) = cell2mat(arrayfun(@(x) ...
histcounts(curr_spikes,t_bins(x,:)), ...
[1:size(t_peri_event,1)]','uni',false))./raster_sample_rate;
end
%%% Regressed fluorescence -> MUA
% Get time points to bin
sample_rate = framerate*regression_params.upsample_factor;
time_bins = frame_t(find(frame_t > ...
regression_params.skip_seconds,1)):1/sample_rate: ...
frame_t(find(frame_t-frame_t(end) < ...
-regression_params.skip_seconds,1,'last'));
time_bin_centers = time_bins(1:end-1) + diff(time_bins)/2;
% Get upsampled dVdf's
dfVdf_resample = interp1(conv2(frame_t,[1,1]/2,'valid'), ...
diff(fVdf,[],2)',time_bin_centers)';
%%%% TESTING: DECONV
fVdf_deconv = AP_deconv_wf(fVdf);
fVdf_deconv(isnan(fVdf_deconv)) = 0;
fVdf_deconv_resample = interp1(frame_t,fVdf_deconv',time_bin_centers)';
%%%%
binned_spikes = zeros(n_depths,length(time_bin_centers));
for curr_depth = 1:n_depths
curr_spike_times = spike_times_timeline(depth_group == curr_depth);
binned_spikes(curr_depth,:) = histcounts(curr_spike_times,time_bins);
end
binned_spikes_std = binned_spikes./nanstd(binned_spikes,[],2);
binned_spikes_std(isnan(binned_spikes_std)) = 0;
% Load lambda from previously estimated and saved
lambda_fn = 'C:\Users\Andrew\OneDrive for Business\Documents\CarandiniHarrisLab\analysis\wf_ephys_choiceworld\ephys_processing\ctx-str_lambda';
load(lambda_fn);
curr_animal_idx = strcmp(animal,{ctx_str_lambda.animal});
if any(curr_animal_idx)
curr_day_idx = strcmp(day,ctx_str_lambda(curr_animal_idx).day);
if any(curr_day_idx)
lambda = ctx_str_lambda(curr_animal_idx).best_lambda(curr_day_idx);
end
end
%%%%% TEST
warning('OVERRIDING LAMBDA')
lambda = 5;
%%%%%%%
kernel_frames = round(regression_params.kernel_t(1)*sample_rate): ...
round(regression_params.kernel_t(2)*sample_rate);
% %%%% TESTING DECONV
% [ctx_str_k,predicted_spikes_std,explained_var] = ...
% AP_regresskernel(fVdf_deconv_resample(regression_params.use_svs,:), ...
% binned_spikes_std,kernel_frames,lambda, ...
% regression_params.zs,regression_params.cvfold, ...
% true,regression_params.use_constant);
%%%%%%%%%%%% TESTING USING SPECIFIC TIMES FOR REGRESSION
% Only predict 0.5 seconds around leftward movement
%%% To use stim right times
% Get right stim samples
stim_R_trace = histcounts(stimOn_times( ...
signals_events.trialSideValues(1:length(stimOn_times)) == 1),time_bins);
stim_R_surround_samples = round(0.5*framerate);
stim_R_surround = conv(stim_R_trace,ones(1,stim_R_surround_samples),'same') > 0;
% Set regression discontinuities
sample_num = 1:length(stim_R_surround);
discontinuities = [1,diff(sample_num(stim_R_surround)) > 1];
% Do cortex->striatum regression around right stim
[ctx_str_k,~,explained_var] = ...
AP_regresskernel(fVdf_deconv_resample(regression_params.use_svs,stim_R_surround), ...
binned_spikes_std(:,stim_R_surround),kernel_frames,lambda, ...
regression_params.zs,regression_params.cvfold, ...
true,regression_params.use_constant,discontinuities);
% %%% To use movement L times
% % Get trial-aligned wheel velocity
% event_aligned_wheel = interp1(Timeline.rawDAQTimestamps, ...
% wheel_velocity,t_peri_event);
%
% % Get reaction times
% [move_trial,move_idx] = max(abs(event_aligned_wheel) > 0.02,[],2);
% move_idx(~move_trial) = NaN;
% move_t = nan(size(move_idx));
% move_t(~isnan(move_idx) & move_trial) = t(move_idx(~isnan(move_idx) & move_trial))';
%
% % Get leftward movement samples
% move_time_L_absolute = arrayfun(@(x) t_peri_event(x,move_idx(x)), ...
% find(~isnan(move_idx) & trial_choice(1:length(stimOn_times))' == -1));
% move_L_trace = histcounts(move_time_L_absolute,time_bins);
%
% move_L_surround_samples = round(0.5*framerate);
% move_L_surround = conv(move_L_trace,ones(1,move_L_surround_samples),'same') > 0;
%
% % Set regression discontinuities
% sample_num = 1:length(move_L_surround);
% discontinuities = [1,diff(sample_num(move_L_surround)) > 1];
%
% % Do cortex->striatum regression around leftward movements
% [ctx_str_k,~,explained_var] = ...
% AP_regresskernel(fVdf_deconv_resample(regression_params.use_svs,move_L_surround), ...
% binned_spikes_std(:,move_L_surround),kernel_frames,lambda, ...
% regression_params.zs,regression_params.cvfold, ...
% true,regression_params.use_constant,discontinuities);
% Apply that kernel to the whole experiment
predicted_spikes_std = nan(size(binned_spikes_std));
for curr_depth = 1:n_depths
curr_conv = zeros(1,size(binned_spikes_std,2));
for curr_v = 1:length(regression_params.use_svs)
curr_conv = curr_conv + convn(fVdf_deconv_resample(curr_v,:), ...
ctx_str_k{1}(curr_v,:,curr_depth),'same');
end
predicted_spikes_std(curr_depth,:) = ...
curr_conv + ctx_str_k{2}(curr_depth);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Shove the k's into the master to save small V's
ctx_str_k_recast = reshape(ChangeU(Udf_aligned(:,:,regression_params.use_svs), ...
reshape(ctx_str_k{1},size(ctx_str_k{1},1),[]),U_master(:,:,regression_params.use_svs)), ...
size(ctx_str_k{1}));
% Re-scale the prediction (subtract offset, multiply, add scaled offset)
predicted_spikes = (predicted_spikes_std - squeeze(ctx_str_k{end})).* ...
nanstd(binned_spikes,[],2) + ...
nanstd(binned_spikes,[],2).*squeeze(ctx_str_k{end});
event_aligned_mua_ctxpred = ...
interp1(time_bin_centers,predicted_spikes',t_peri_event)./raster_sample_rate;
%%%%
%%%% TESTING CTX->WHEEL VELOCITY
% Resample wheel, predict both velocity and speed
wheel_velocity_resample = interp1(Timeline.rawDAQTimestamps,wheel_velocity,time_bin_centers);
wheel_velspeed_resample = [wheel_velocity_resample;abs(wheel_velocity_resample)];
wheel_velspeed_resample_std = wheel_velspeed_resample./std(wheel_velocity_resample);
[ctx_wheel_k,predicted_wheel_velspeed_std,explained_var] = ...
AP_regresskernel(fVdf_deconv_resample(regression_params.use_svs,:), ...
wheel_velspeed_resample_std,kernel_frames,lambda, ...
regression_params.zs,regression_params.cvfold, ...
false,false);
predicted_wheel_velspeed = predicted_wheel_velspeed_std.* ...
std(wheel_velocity_resample);
% Shove the k's into the master to save small V's
ctx_wheel_k_recast = reshape(ChangeU(Udf_aligned(:,:,regression_params.use_svs), ...
reshape(ctx_wheel_k,size(ctx_wheel_k,1),[]),U_master(:,:,regression_params.use_svs)), ...
size(ctx_wheel_k));
event_aligned_wheel_ctxpred = ...
interp1(time_bin_centers,predicted_wheel_velspeed(1,:)',t_peri_event);
%%%%
%%% Wheel velocity
event_aligned_wheel = interp1(Timeline.rawDAQTimestamps, ...
wheel_velocity,t_peri_event);
%%% Outcome (reward page 1, punish page 2)
% (note incorrect outcome imprecise from signals, but looks good)
event_aligned_outcome = zeros(size(t_peri_event,1),size(t_peri_event,2),2);
event_aligned_outcome(trial_outcome == 1,:,1) = ...
(cell2mat(arrayfun(@(x) ...
histcounts(reward_t_timeline,t_bins(x,:)), ...
find(trial_outcome == 1)','uni',false))) > 0;
event_aligned_outcome(trial_outcome == -1,:,2) = ...
(cell2mat(arrayfun(@(x) ...
histcounts(signals_events.responseTimes,t_bins(x,:)), ...
find(trial_outcome == -1)','uni',false))) > 0;
% Pick trials to use
use_trials = ...
trial_outcome ~= 0 & ...
~signals_events.repeatTrialValues(1:n_trials) & ...
stim_to_feedback < 1.5;
% Get behavioural data
D = struct;
D.stimulus = zeros(sum(use_trials),2);
L_trials = signals_events.trialSideValues(1:n_trials) == -1;
R_trials = signals_events.trialSideValues(1:n_trials) == 1;
D.stimulus(L_trials(use_trials),1) = signals_events.trialContrastValues(L_trials & use_trials);
D.stimulus(R_trials(use_trials),2) = signals_events.trialContrastValues(R_trials & use_trials);
D.response = 3-(abs((trial_choice(use_trials)'+1)/2)+1);
D.repeatNum = ones(sum(use_trials),1);
D.outcome = reshape(trial_outcome(use_trials),[],1);
%%%%%%%%%%%%%%%%%%%%%%%%%%% TESTING: TASK REGRESSION (LONG TRACE)
%%% Regression (separate regressors to get partial explained variance)
% Get reaction time for building regressors
[move_trial,move_idx] = max(abs(event_aligned_wheel) > 0.02,[],2);
move_idx(~move_trial) = NaN;
move_t = nan(size(move_idx));
move_t(~isnan(move_idx) & move_trial) = t(move_idx(~isnan(move_idx) & move_trial))';
% Stim regressors
unique_stim = unique(contrasts(contrasts > 0).*sides');
stim_contrastsides = ...
signals_events.trialSideValues(1:length(stimOn_times)).* ...
signals_events.trialContrastValues(1:length(stimOn_times));
stim_regressors = zeros(length(unique_stim),length(time_bin_centers));
for curr_stim = 1:length(unique_stim)
curr_stim_times = stimOn_times(stim_contrastsides == unique_stim(curr_stim));
stim_regressors(curr_stim,:) = histcounts(curr_stim_times,time_bins);
end
% Stim move regressors (one for each stim when it starts to move)
stim_move_regressors = zeros(length(unique_stim),length(time_bin_centers));
for curr_stim = 1:length(unique_stim)
% (find the first photodiode flip after the stim azimuth has
% moved past a threshold)
curr_stimOn_times = stimOn_times(trial_outcome(1:length(stimOn_times)) ~= 0 & ...
stim_contrastsides == unique_stim(curr_stim));
azimuth_move_threshold = 5; % degrees to consider stim moved
stim_move_times_signals = ...
signals_events.stimAzimuthTimes( ...
abs(signals_events.stimAzimuthValues - 90) > azimuth_move_threshold);
curr_stim_move_times_signals = arrayfun(@(x) ...
stim_move_times_signals(find(stim_move_times_signals > ...
curr_stimOn_times(x),1)),1:length(curr_stimOn_times));
curr_stim_move_times_photodiode = arrayfun(@(x) ...
photodiode_flip_times(find(photodiode_flip_times > ...
curr_stim_move_times_signals(x),1)),1:length(curr_stim_move_times_signals));
stim_move_regressors(curr_stim,:) = histcounts(curr_stim_move_times_photodiode,time_bins);
end
% Stim center regressors (one for each stim when it's stopped during reward)
unique_contrasts = unique(contrasts(contrasts > 0));
stim_contrasts = ...
signals_events.trialContrastValues(1:length(stimOn_times));
stim_center_regressors = zeros(length(unique_contrasts),length(time_bin_centers));
for curr_contrast = 1:length(unique_contrasts)
% (find the last photodiode flip before the reward)
curr_stimOn_times = stimOn_times(trial_outcome(1:length(stimOn_times)) == 1 & ...
stim_contrasts == unique_contrasts(curr_contrast));
curr_reward_times = arrayfun(@(x) ...
reward_t_timeline(find(reward_t_timeline > ...
curr_stimOn_times(x),1)),1:length(curr_stimOn_times));
curr_prereward_photodiode_times = arrayfun(@(x) ...
photodiode_flip_times(find(photodiode_flip_times < ...
curr_reward_times(x),1,'last')),1:length(curr_reward_times));
stim_center_regressors(curr_contrast,:) = histcounts(curr_prereward_photodiode_times,time_bins);
end
% Move onset regressors (L/R)
move_time_L_absolute = arrayfun(@(x) t_peri_event(x,move_idx(x)), ...
find(~isnan(move_idx) & trial_choice(1:length(stimOn_times))' == -1));
move_time_R_absolute = arrayfun(@(x) t_peri_event(x,move_idx(x)), ...
find(~isnan(move_idx) & trial_choice(1:length(stimOn_times))' == 1));
move_onset_regressors = zeros(2,length(time_bin_centers));
move_onset_regressors(1,:) = histcounts(move_time_L_absolute,time_bins);
move_onset_regressors(2,:) = histcounts(move_time_R_absolute,time_bins);
% Move ongoing regressors (L/R)
wheel_velocity_interp = interp1(Timeline.rawDAQTimestamps,wheel_velocity,time_bin_centers);
move_ongoing_regressors = zeros(2,length(time_bin_centers));
move_ongoing_regressors(1,wheel_velocity_interp < 0) = 1;
move_ongoing_regressors(2,wheel_velocity_interp > 0) = 1;
% Go cue regressors - separate for early/late move
% (using signals timing - not precise but looks good)
if length(signals_events.interactiveOnTimes) ~= length(move_t)
error('Different number of interactive ons and move times')
end
go_cue_regressors = zeros(2,length(time_bin_centers));
go_cue_regressors(1,:) = histcounts( ...
signals_events.interactiveOnTimes(move_t <= 0.5),time_bins);
go_cue_regressors(2,:) = histcounts( ...
signals_events.interactiveOnTimes(move_t > 0.5),time_bins);
% Outcome regressors
% (using signals timing - not precise but looks good)
outcome_regressors = zeros(2,length(time_bin_centers));
outcome_regressors(1,:) = histcounts( ...
reward_t_timeline,time_bins);
outcome_regressors(2,:) = histcounts( ...
signals_events.responseTimes(trial_outcome == -1),time_bins);
% Concatenate regressors, set parameters
regressors = {stim_regressors;move_onset_regressors;go_cue_regressors;outcome_regressors};
regressor_labels = {'Stim','Move onset','Go cue','Outcome'};
t_shifts = {[0,0.5]; ... % stim
[-0.5,1]; ... % move
[-0.1,0.5]; ... % go cue
[-0.5,1]}; % outcome
sample_shifts = cellfun(@(x) round(x(1)*(sample_rate)): ...
round(x(2)*(sample_rate)),t_shifts,'uni',false);
lambda = 0;
zs = [false,false];
cvfold = 5;
use_constant = false;
return_constant = false;
% Regression task -> MUA
mua_taskpred_k = cell(length(regressors)+return_constant,n_depths);
mua_taskpred = nan(size(event_aligned_mua));
mua_taskpred_reduced = ...
repmat(nan(size(event_aligned_mua)),1,1,1,length(regressors));
for curr_depth = 1:n_depths
baseline = nanmean(reshape(event_aligned_mua(:,t < 0,curr_depth),[],1)*raster_sample_rate);
activity = single(binned_spikes(curr_depth,:)) - baseline;
% Skip if nothing in this depth
if ~any(activity(:))
continue
end
[task_kernel,activity_predicted,expl_var,activity_predicted_reduced] = ...
AP_regresskernel(regressors,activity,sample_shifts, ...
lambda,zs,cvfold,return_constant,use_constant);
mua_taskpred_k(:,curr_depth) = task_kernel;
mua_taskpred(:,:,curr_depth) = ...
interp1(time_bin_centers,activity_predicted',t_peri_event)./raster_sample_rate;
mua_taskpred_reduced(:,:,curr_depth,:) = cell2mat(arrayfun(@(x) ...
interp1(time_bin_centers,activity_predicted_reduced(:,:,x)', ...
t_peri_event)./raster_sample_rate,permute(1:length(regressors),[1,3,4,2]),'uni',false));
end
% Regression task -> MUA-ctxpred
mua_ctxpred_taskpred_k = cell(length(regressors)+return_constant,n_depths);
mua_ctxpred_taskpred = nan(size(event_aligned_mua));
mua_ctxpred_taskpred_reduced = ...
repmat(nan(size(event_aligned_mua)),1,1,1,length(regressors));
for curr_depth = 1:n_depths
baseline = nanmean(reshape(event_aligned_mua_ctxpred(:,t < 0,curr_depth),[],1)*raster_sample_rate);
activity = single(predicted_spikes(curr_depth,:))-baseline;
% Skip if nothing in this depth
if ~any(activity(:))