-
Notifications
You must be signed in to change notification settings - Fork 0
/
plot_fig2A_SEP_D1_vs_Trained.m
103 lines (84 loc) · 3.95 KB
/
plot_fig2A_SEP_D1_vs_Trained.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Script that plots the results presented in figure 2.A.
%
% Written by Pierre Le Merre
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Load Matrices
if exist('PathDatabase','var')==0 && exist('LFP_Data','var')==0 && exist('LFP_Data_description','var')==0
[PathDatabase,LFP_Data,LFP_Data_description]=Load_LFP_Multisite_database;
end
%learned days
load([PathDatabase filesep 'Learning_Days_Mtrx.mat']);
% p_value colormap
load([PathDatabase filesep 'p_value_colormap.mat']);
%% Plot SEP Stim trials, naive vs trained mice
Mouse_list=unique(LFP_Data.Mouse_Name(strcmp(LFP_Data.Session_Type,'DT')==1));
sr=str2double(LFP_Data_description.Trial_LFP_wS1.SamplingFrequencyHz);
t=linspace(0,5,sr*5);
Stimtime=3; %in sec
Baseline_duration=100; %in samples
%% Compute p_value vector
p=[];
Amp_D1=[];
Amp_Trained=[];
start_idx=1;
end_idx=5*sr;
win_size=10;
win_step=10;
win_nb=floor((size(LFP_Data.Trial_LFP_wS1,2)-win_size)/win_step+1);
Fields={'wS1','wS2','wM1','PtA','dCA1','mPFC'};
data_mouse_D1=[];
data_mouse_Learned=[];
for j=1:6
Field=char(Fields(j));
for i=1:size(Mouse_list,1)
Mouse=char(Mouse_list(i));
eval(['data_mouse_D1.' Field '(i,:)=nanmean(LFP_Data.Trial_LFP_' Field '(strcmp(LFP_Data.Mouse_Name,Mouse)==1 & LFP_Data.Session_Counter==1 & [LFP_Data.Trial_ID==1 | LFP_Data.Trial_ID==0],:));'])
eval(['data_mouse_Learned.' Field '(i,:)=nanmean(LFP_Data.Trial_LFP_' Field '(strcmp(LFP_Data.Mouse_Name,Mouse)==1 & [LFP_Data.Session_Counter==LDM(i,1) | LFP_Data.Session_Counter==LDM(i,2) | LFP_Data.Session_Counter==LDM(i,3)] & [LFP_Data.Trial_ID==1 | LFP_Data.Trial_ID==0],:));'])
eval(['data_mouse_D1.' Field '(i,:)=data_mouse_D1.' Field '(i,:)-nanmean(data_mouse_D1.' Field '(i,Stimtime*sr-Baseline_duration:Stimtime*sr),2);'])
eval(['data_mouse_Learned.' Field '(i,:)=data_mouse_Learned.' Field '(i,:)-nanmean(data_mouse_Learned.' Field '(i,Stimtime*sr-Baseline_duration:Stimtime*sr),2);'])
end
for k=1:win_nb-1 %%Sliding Window
win_in=(k-1)*win_step+start_idx;
win_out=(k-1)*win_step+win_size+start_idx;
eval(['Amp_D1=nanmean(data_mouse_D1.' Field '(:,win_in:win_out),2);'])
eval(['Amp_Trained=nanmean(data_mouse_Learned.' Field '(:,win_in:win_out),2);'])
eval(['p.' Field '(1,k)=signrank(Amp_D1,Amp_Trained);'])
end
end
% Plot D1 vs Trained with p values colormaps
for j=1:6
fig=figure;
Field=char(Fields(j));
ax(1)=subplot(3,1,[1 2]);
eval(['data1=nanmean(data_mouse_D1.' Field ')*10^(6);'])
eval(['data3=data_mouse_Learned.' Field ';'])
eval(['sem1=nanstd((data_mouse_D1.' Field '*10^(6)),[],1)/sqrt(sum(~isnan(data_mouse_D1.' Field '(:,1))));'])
boundedline(t,data1,sem1,'b');
hold on
eval(['data2=nanmean(data_mouse_Learned.' Field ')*10^(6);'])
eval(['sem2=nanstd((data_mouse_Learned.' Field '*10^(6)),[],1)/sqrt(sum(~isnan(data_mouse_Learned.' Field '(:,1))));'])
boundedline(t,data2,sem2,'r','alpha');
set(0, 'DefaultFigureRenderer', 'painters')
title([Field ' n=' num2str(size(data3(isnan(data3(:,1))==0,1),1))],'fontweight','bold','fontsize',12,'fontname','times new roman');
xlim([2.95 3.2]);
ylim([-400 100]);
xlabel('Time(s)')
ylabel('(V)')
line([3 3],get(ax(1),'yLim'),'LineWidth',1,'Color',[0 0 0])
line([3.006 3.006],get(ax(1),'yLim'),'LineWidth',1,'Color',[0.5 0.5 0.5])
ax(2)=subplot(3,1,3);
linkaxes(ax,'x');
y=[0 1];
eval(['imagesc(t,y,1-log(p.' Field '));'])
colormap(p_value_colormap);
xlim([2.95 3.2]);
ylim([0 1])
line([3 3],get(ax(2),'yLim'),'LineWidth',1,'Color',[0 0 0])
c=colorbar('southoutside','Ticks',[0,1-log(0.05),1-log(0.01),1-log(0.001),1-log(0.0001)],...
'TickLabels',{num2str(0),num2str(0.05),num2str(0.01),num2str(0.001),num2str(0.0001)});
c.Label.String = 'p value';
caxis([1, 1-log(0.0005)])
end
%%
clear all