How do I extract the firing threshold value from an action potential phase plot?

Hi,

I am managing to produce a phase plot and I can zoom into it to see a closely approximated value for the firing threshold (first kink with a blue arrow pointing to in the attached figure). How do I extract this value without using ginput?

My code to produce the figure and extract the value using ginput:

% Roksana's script to plot .smr average AP phase plot

% 20210127

% For troubleshooting analysis - cleans the workspace

clear

close all

%Import data

load('C:\xyz.mat')

%Define channel to plot

data = xyz;

vm = data.values;

%scale x-axis by interval

t = linspace(0,length(vm)*data.interval,length(vm));

%derive membrane potential

dA = diff(data.values);

%omit last value so vectors are of equal length

data.values(end)=[];

%plot derivative dV/dt over Vm

figure;

plot(data.values,dA);

xlabel('Membrane Potential (Vm)');

ylabel('dV/dt');

title('Phase Plot');

%find firing threshold

zoom on;

pause()

zoom off;

[x, ~] = ginput

zoom out;

[Mathieu NOE]

hello Roksana (again)

how is this post related to the other one ?

How do I set the threshold value on the action potential on the upward and downward slope to define width? - MATLAB Answers - MATLAB Central (mathworks.com)

same topic / data ?

[Roksana Khalid]

Yes, same topic and data.

The membrane potential value (x-axis) I get from here corresponds to the y-values I want to set on the plot in the other question.

Answer 1

[Mathieu NOE]

hello again....

so my second submission for you today !

attached also 2 subfunction for crossing points computation and a better derivative method

i used a bit of smoothing befor the derivation to improve the quality of the data , especially in the "corners".

it's more or less a second alternative to the technique employed in the other post (BTW you could here also compute width and area if needed)

%Import data
load('xyz.mat')

%Define channel to plot
% data = xyz;
% vm = data.values;

% S2C1_17082020_WM_apomorphine_20uM_Ch1 = 
% 
%   struct with fields:
% 
%        title: 'voltage'
%      comment: 'No comment'
%     interval: 4.0000e-05
%        scale: 0.0153
%       offset: 0
%        units: 'mV'
%        start: 0
%       length: 1122
%       values: [1122×1 double]
      
vm = S2C1_17082020_WM_apomorphine_20uM_Ch1.values;
samples = length(vm);
dt = S2C1_17082020_WM_apomorphine_20uM_Ch1.interval;
t_final = samples*dt;

%scale x-axis by interval
t = linspace(0, t_final,samples);

% smoothin a bit the data
vm_smoo = sgolayfilt(vm,1,15);

figure(1);
plot(t,vm,'b',t,vm_smoo,'r');
ylabel('V (mV)');
xlabel('Time (s)');
legend('raw','sgolay filtered');
title('Membrane potential')


% compute first derivative on raw and smoothed data
[dvm, ddvm] = firstsecondderivatives(t,vm);
[dvm_smoo, ddvm_smoo] = firstsecondderivatives(t,vm_smoo);

% 1st knee point has slope approx 1/1.5 of max  slope  (positive threshold, positive slope)
% 2nd knee point has slope approx 1/10th of max  slope  (negative threshold, positive slope)
a_pos1 = 1.5;
pos_slope_max = max(dvm_smoo(dvm_smoo>0));
pos_slope_trigger = pos_slope_max/a_pos1;

a_pos2 = 10;
neg_slope_max = min(dvm_smoo(dvm_smoo<0));
neg_slope_trigger = neg_slope_max/a_pos2;

% use crossing_V7 function to find and interpolate crossing points
[ind_pos1,t0_pos1,s0_pos1,~,~,~] = crossing_V7(dvm_smoo,t,pos_slope_trigger,'linear');
[ind_pos2,t0_pos2,s0_pos2,~,~,~] = crossing_V7(dvm_smoo,t,neg_slope_trigger,'linear');
% select first one (can be multiple if measurement noise - shoudn't be especially after filtering)
t0_pos1 = t0_pos1(1);
t0_pos2 = t0_pos2(1);
vm_knee1 = interp1(t,vm,t0_pos1,'linear');
vm_knee2 = interp1(t,vm,t0_pos2,'linear');
dvm_knee1 = interp1(t,dvm,t0_pos1,'linear');
dvm_knee2 = interp1(t,dvm,t0_pos2,'linear');

figure(2);
plot(t,vm,'b',t0_pos1,vm_knee1,'+r',t0_pos2,vm_knee2,'+g')
ylabel('V (mV)')
xlabel('Time (s)')
title('Membrane potential')

figure(3);
plot(vm,dvm,'b',vm_knee1,dvm_knee1,'+r',vm_knee2,dvm_knee2,'+g')
xlabel('Voltage (mV)')
ylabel('dV/dt (mV/s)')
title('Membrane potential')

[Roksana Khalid]

Thank you so much! The smoothing looks really nice!

The point on the derivative falls on a rising slope, which is super good, but the kink where it suddenly turns is of biological relevance in the data and what I am targeting.

I wonder if there is a way to specify the -61.15mV point?

[Mathieu NOE]

hello again

see first my answer in the other post

fine tunong my slope threshold to 7e3 mV/s led to find the exact point your showing

hope this helps

%Import data
load('xyz.mat')
% load('AP2.mat')

     
vm = S2C1_17082020_WM_apomorphine_20uM_Ch1.values;
samples = length(vm);
dt = S2C1_17082020_WM_apomorphine_20uM_Ch1.interval;
t_final = samples*dt;

%scale x-axis by interval
t = linspace(0, t_final,samples);

% compute first derivative on data
[dvm, ddvm] = firstsecondderivatives(t,vm);

figure(1);
subplot(2,1,1),plot(t,vm,'b');
ylabel('V (mV)');
subplot(2,1,2),plot(t,dvm,'b');
ylabel('dV/dt (mV/s)');
xlabel('Time (s)');
title('Membrane potential')

% 1st knee point has slope approx 1/N th of max  slope  (positive threshold, positive slope)
% 2nd knee point has slope approx 1/N th of max  slope  (negative threshold, positive slope)
% a_pos1 = 150;
% pos_slope_max = max(dvm(dvm>=0));
% pos_slope_trigger = pos_slope_max/a_pos1;   % 4e3
pos_slope_trigger =  7e3;

% a_pos2 = 25;
% neg_slope_max = min(dvm(dvm<=0));
% neg_slope_trigger = neg_slope_max/a_pos2;   % -4e3
neg_slope_trigger =  -7e3;

indpos = find(dvm>=pos_slope_trigger)
t0_pos1 = t(indpos(1));

indneg = find(dvm<=neg_slope_trigger)
t0_pos2 = t(indneg(end));

vm_knee1 = interp1(t,vm,t0_pos1,'linear');
vm_knee2 = interp1(t,vm,t0_pos2,'linear');
dvm_knee1 = interp1(t,dvm,t0_pos1,'linear');
dvm_knee2 = interp1(t,dvm,t0_pos2,'linear');

figure(2);
plot(t,vm,'b',t0_pos1,vm_knee1,'+r',t0_pos2,vm_knee2,'+g')
ylabel('V (mV)')
xlabel('Time (s)')
title('Membrane potential')

figure(3);
plot(vm,dvm,'b',vm_knee1,dvm_knee1,'+r',vm_knee2,dvm_knee2,'+g')
xlabel('Voltage (mV)')
ylabel('dV/dt (mV/s)')
title('Membrane potential')

[Roksana Khalid]

Thank you very much Mathieu! :)