Consider three scores of acute behavior

public/neglect_predict.m

@@ -1,27 +1,41 @@
-function neglect_predict(fnm, acuteCoC)
-%Predict chronic neglect using lesion map and acute center of cancellation
+function neglect_predict(fnm, acuteCoCl, acuteCoCb, acuteCop)
+%Predict chronic neglect using lesion map and acute behavior
 % Assumes lesion map is normalized to 181x217x181 using SPM
 %Examples
 % neglect_predict; %use GUI;
-% neglect_predict('M2095_lesion.nii.gz', 0.65)
+% neglect_predict('example_lesion.nii.gz', 0.87, 0.81, 2)
     if nargin < 1
         [p_file, p_path] = uigetfile('*.nii.gz;*.nii', 'Select lesion map');
         if p_file==0
             return
         end
         fnm = fullfile(p_path, p_file);
     end
-    if nargin < 2
-        prompt = {'Enter acute CoC score (-1..1):'};
+    if nargin < 3 
+        prompt = {'Enter acute CoC score [letters] (-1..1):', 'Enter acute CoC score [bells] (-1..1):', 'Enter acute Copying score (0..8):'};
         dlgtitle = 'Input';
         fieldsize = [1];
-        definput = {'0.65'};
+        definput = {'0.87', '0.81', '2'};
         answer = inputdlg(prompt,dlgtitle,fieldsize,definput);
         if length(answer) == 0
             return
         end
-        acuteCoC = str2double(answer{1});
+        acuteCoCl = str2double(answer{1});
+        acuteCoCb = str2double(answer{2});
+        acuteCop = str2double(answer{3});
+    end 
+    % calculate mean CoC score
+    if (isnan(acuteCoCl)) && (isnan(acuteCoCb))
+        error('Enter at least one acute CoC score.')
+    else
+        acuteCoC = mean([acuteCoCl, acuteCoCb], 'omitnan');
     end
+    % calculate acute z-score based on user input 
+    acuteZl = (acuteCoCl - 0.00686)/0.0179; % mean/SD of controls
+    acuteZb = (acuteCoCb - 0.0092)/0.0253; % mean/SD of controls
+    acuteZCop = (acuteCop - 0.23333)/0.43018; % mean/SD of controls
+    acuteZ = mean([acuteZl, acuteZb acuteZCop], 'omitnan');
+
     lesion = niftiread(fnm);
     mpath = fileparts(mfilename("fullpath"));
     fnmMsk = fullfile(mpath, 'mask.nii.gz');
@@ -61,7 +75,6 @@ function neglect_predict(fnm, acuteCoC)
     acuteCoC01 = norm0to1(acuteCoC, -0.024243014, 0.951938077);
     ROI_volML01 = norm0to1(ROI_volML, 0, 21.625);
     % input_vector = [PC1, PC2, PC3, PC4, PC5, CoC, ROI_vol];
-    % Exemplary patient data (with outcome = 0.8155):
     input_vector = [PC(1), PC(2), PC(3), PC(4), PC(5), acuteCoC01, ROI_volML01];
     fnmModel = fullfile(mpath, 'models_5x10_diff.mat');
     if ~exist(fnmPCA,'file')
@@ -91,14 +104,10 @@ function neglect_predict(fnm, acuteCoC)
     % Calculate mean prediction
     prediction_mean = mean(predictions_mdls);
     diffZ = prediction_mean * (38.72560594 + 1.211389735) - 1.211389735;
-    % calculate acute z-score based on user input CoC
-    acuteZ = (acuteCoC - 0.00803)/0.0216; % mean/SD of controls
     % calculate chronic z-score that can be interpreted by the user
     chronZ = acuteZ-diffZ;
-    chronCoC = chronZ * 0.0216 + 0.00803;
+    chronCoC = chronZ * 0.0216 + 0.00803; % average of letters and bells
     % output text
-    % "Acute z-score = xxx; z-score difference (predicted) = xxx; chronic z-score = xxx.
-    % The predicted score represents the direct improvement between acute and chronic stroke phase (i.e. acute z-score minus chronic z-score). Negative values and a z-score % of about 0 indicate no deficit; the larger the z-score, the more severe the neglect behavior."
     str = sprintf('Given %gml lesion (with %g in core neglect voxels), and acute CoC %g  (z= %g), predicted chronic CoC is %g (z= %g)\n', lesionVolTotalML, ROI_volML, acuteCoC, acuteZ, chronCoC, chronZ);
     disp(str);
 end