Merge pull request #69 from MaxvandenBoom/master

Multiple updates and fixes to prepare for publication
MultimodalNeuroimagingLab · Jan 28, 2023 · 3e87b97 · 3e87b97
2 parents a5c2d1e + bbe951a
commit 3e87b97
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diff --git a/README.md b/README.md
@@ -14,7 +14,8 @@ Scripts to process the data, detect N1 responses and add tract information:
 
 Scripts to make the figure panels:
 - makeFig1A_plotMNI.m
-- makeFig1BandC_plotResponsesAge.m
+- makeFig1B_subjectResponses.m
+- makeFig1C_heatmapResponsesAge.m
 - makeFig2and3_transmissionAcrossAge.m
 
 

diff --git a/external/Violinplot-Matlab/Violin.m b/external/Violinplot-Matlab/Violin.m
@@ -173,7 +173,7 @@
             hiwhisker = quartiles(3) + 1.5*IQR;
             hiwhisker = min(hiwhisker, max(data(data < hiwhisker)));
             if ~isempty(lowhisker) && ~isempty(hiwhisker)
-                obj.WhiskerPlot = plot([pos pos], [lowhisker hiwhisker]);
+                obj.WhiskerPlot = plot([pos pos], [lowhisker hiwhisker], 'LineWidth', 1.2);
             end
             obj.MedianPlot = scatter(pos, quartiles(2), [], [1 1 1], 'filled');
 

diff --git a/functions/read_tsv.m b/functions/read_tsv.m
diff --git a/scripts/ccepExtra01_plotN1.m b/scripts/ccepExtra01_plotN1.m
diff --git a/scripts/makeFig1C_heatmapResponsesAge.m → scripts/makeFig1Cand3A_heatmapResponsesAge.m b/scripts/makeFig1C_heatmapResponsesAge.m → scripts/makeFig1Cand3A_heatmapResponsesAge.m
@@ -1,6 +1,6 @@
 % 
 % 
-%  This code is used to create Figure 1C - the normalized CCEPs of all patients in order of age, 
+%  This code is used to the plot for Figure 1C and Figure 3A - the normalized CCEPs of all patients in order of age, 
 %  
 %
 
@@ -71,7 +71,7 @@
 
 
 %% 
-% Figure 1C - Plots for each stimulated and responding region with normalized CCEPs + N1 sorted by age
+% Plots for each stimulated and responding region with normalized CCEPs + N1 sorted by age
 
 ttmin = 0.010;
 ttmax = .100;
@@ -101,7 +101,7 @@
                 set(gca,'XTick',20:20:80,'YTick',[])
                 axis tight
 
-                strSign = [' (p\_fdr = ', num2str(all_p_fdr{iTr}{iSubTr}(iDir + 1)), ')'];
+                strSign = [' (p\_fdr = ', num2str(all_p_fdr{iTr}{iSubTr}(iDir + 1)), ',  n=', num2str(length(sortedCCEPs{iTr}{iSubTr}{iDir + 1}.age)), ')'];
                 if all_p_fdr{iTr}{iSubTr}(iDir + 1) < .05,  strSign = [strSign, ' *'];     end
                 title([rois(iTr).tract_name, ' - ', strSubTitle, strSign]);
 

diff --git a/scripts/makeFig2and3_transmissionAcrossAge.m b/scripts/makeFig2and3_transmissionAcrossAge.m
@@ -1,5 +1,5 @@
 %
-% This script produces Figure 3 of the article
+% This script produces the plots for Figure 2 and 3 (B/C) of the article
 %
 
 
@@ -127,6 +127,11 @@
             n1LatencyMeans(exclMeans) = [];
             n1SpeedMeans(exclMeans) = [];
 
+            % store the number of age-samples (age can be averages over more than one subject)
+            out{iTr}{iSubTr}{iDir + 1}.numAges = length(ages);
+
+
+            % initialize statistics variables
             lat_cross_linear = NaN(length(n1LatencyMeans), 4);      % <age> x <size latency (ms), prediction (ms), p1 (slope), p2 (intercept) of left out>
             spd_cross_linear = NaN(length(n1LatencyMeans), 4);      % <age> x <size latency (ms), prediction (ms), p1 (slope), p2 (intercept) of left out>
             lat_cross_second = NaN(length(n1LatencyMeans), 5);      % <age> x <size latency (ms) X prediction (ms) X p1 (age^2) X p2 (age) X p3 (intercept) of left out>
@@ -441,15 +446,15 @@
 %  Display in command window the cod and delta for each subplot
 %  this info is displayed in Figure 3 as well.
 
-long_tracts = {'Y1065_TPAT - Parietal -> Temporal','Y1065_TPAT - Temporal -> Parietal',...
-    'Y1065_AF - Frontal -> Temporal','Y1065_AF - Temporal -> Frontal',...
-    'Y1065_SLF2 - Frontal -> Parietal','Y1065_SLF2 - Parietal -> Frontal',...
-    'Y1065_SLF2 - Frontal -> Central','Y1065_SLF2 - Central -> Frontal'};
+long_tracts = { 'Y1065_TPAT - Parietal -> Temporal','Y1065_TPAT - Temporal -> Parietal',...
+                'Y1065_AF - Frontal -> Temporal','Y1065_AF - Temporal -> Frontal',...
+                'Y1065_SLF2 - Frontal -> Parietal','Y1065_SLF2 - Parietal -> Frontal',...
+                'Y1065_SLF2 - Frontal -> Central','Y1065_SLF2 - Central -> Frontal'};
 long_cod_lat = NaN(1,length(long_tracts));
 long_cod_spd = NaN(1,length(long_tracts));
 
 U_tracts = {'Y842_U - PreCentral -> PostCentral','Y842_U - PostCentral -> PreCentral',...
-    'Y842_U - Frontal -> Frontal','Y842_U - Parietal -> Parietal'};
+            'Y842_U - Frontal -> Frontal','Y842_U - Parietal -> Parietal'};
 U_cod_lat = NaN(1,length(U_tracts));
 U_cod_spd = NaN(1,length(U_tracts));
 
@@ -460,13 +465,13 @@
         for iDir = [false true]
 
             if strcmp(out{iTr}{iSubTr}{iDir + 1}.lat_fit, 'linear')
-                fprintf(' - %s: best fit is linear, with COD = %2.0f, and delta %1.2f \n', ...
-                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.lat_cod, out{iTr}{iSubTr}{iDir + 1}.lat_delta)
+                fprintf(' - %s: n=%2.0f, best fit is linear, with COD = %2.0f, and delta %1.2f \n', ...
+                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.numAges, out{iTr}{iSubTr}{iDir + 1}.lat_cod, out{iTr}{iSubTr}{iDir + 1}.lat_delta)
 
             elseif strcmp(out{iTr}{iSubTr}{iDir + 1}.lat_fit, 'second')
 
-                fprintf(' - %s: best fit is second, with COD = %2.0f, and delta: age0-10 = %1.2f, age10-20 = %1.2f, age20-30 = %1.2f, age30-40 = %1.2f, age40-50 = %1.2f \n', ...
-                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.lat_cod, out{iTr}{iSubTr}{iDir + 1}.lat_delta);
+                fprintf(' - %s: n=%2.0f, best fit is second, with COD = %2.0f, and delta: age0-10 = %1.2f, age10-20 = %1.2f, age20-30 = %1.2f, age30-40 = %1.2f, age40-50 = %1.2f \n', ...
+                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.numAges, out{iTr}{iSubTr}{iDir + 1}.lat_cod, out{iTr}{iSubTr}{iDir + 1}.lat_delta);
 
             end
 
@@ -488,13 +493,13 @@
         for iDir = [false true]
 
             if strcmp(out{iTr}{iSubTr}{iDir + 1}.spd_fit, 'linear')
-                fprintf(' - %s: best fit is linear, with COD = %2.0f, and delta %1.2f \n', ...
-                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.spd_cod, out{iTr}{iSubTr}{iDir + 1}.spd_delta)
+                fprintf(' - %s: n=%2.0f, best fit is linear, with COD = %2.0f, and delta %1.2f \n', ...
+                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.numAges, out{iTr}{iSubTr}{iDir + 1}.spd_cod, out{iTr}{iSubTr}{iDir + 1}.spd_delta)
 
             elseif strcmp(out{iTr}{iSubTr}{iDir + 1}.spd_fit, 'second')
 
-                fprintf(' - %s: best fit is second, with COD = %2.0f, and delta: age0-10 = %1.2f, age10-20 = %1.2f, age20-30 = %1.2f, age30-40 = %1.2f, age40-50 = %1.2f \n', ...
-                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.spd_cod, out{iTr}{iSubTr}{iDir + 1}.spd_delta);
+                fprintf(' - %s: n=%2.0f, best fit is second, with COD = %2.0f, and delta: age0-10 = %1.2f, age10-20 = %1.2f, age20-30 = %1.2f, age30-40 = %1.2f, age40-50 = %1.2f \n', ...
+                        out{iTr}{iSubTr}{iDir + 1}.name, out{iTr}{iSubTr}{iDir + 1}.numAges, out{iTr}{iSubTr}{iDir + 1}.spd_cod, out{iTr}{iSubTr}{iDir + 1}.spd_delta);
 
             end
             % get averages

diff --git a/scripts/makeSupFig1_only8maSubs.m b/scripts/makeSupFig1_only8maSubs.m
@@ -1,7 +1,8 @@
 %
-% This script outputs supplementary figure 1 - relation between age (years) and latency (ms) at different stimulation currents
+% This script produces supplementary figure 1 - relation between age (years) and latency (ms) at different stimulation currents
 %
-% Dorien van Blooijs, Max van den Boom, 2022
+% Max van den Boom, Dorien van Blooijs, 2023
+
 
 clear
 close all
@@ -24,7 +25,7 @@
 
 
 %% 
-%  ...
+%  Extract the different currents from the dataset
 
 all_unique_currents =[];
 all_currents = struct();
@@ -104,7 +105,7 @@
             all_currents.(['mA', num2str(subject_unique_currents(iCurrent))]) = [];
         end
 
-        % for this sbuject and current, add the age and latency
+        % for this subject and current, add the age and latency
         ind = length(all_currents.(['mA', num2str(subject_unique_currents(iCurrent))]));
         all_currents.(['mA', num2str(subject_unique_currents(iCurrent))])(ind + 1).age = ccepData(iSubj).age;
         all_currents.(['mA', num2str(subject_unique_currents(iCurrent))])(ind + 1).latency = subject_currents.(['mA', num2str(subject_unique_currents(iCurrent))]);
@@ -178,15 +179,15 @@
     % plot the points
     plot(ages, latencies * 1000, '.', 'Color', curColor, 'MarkerSize', 16, 'DisplayName', [num2str(all_unique_currents(iCurrent)) ' mA (n=', num2str(length(latencies)), ')']);
 
+    % check if there are enough points
     if length(latencies) >= minDataPoints
 
-        [P, S] = polyfit(ages(~isnan(ages) & ~isnan(latencies)), ...
-                         latencies(~isnan(ages) & ~isnan(latencies)) * 1000, 1);
+        [P, S] = polyfit(ages(~isnan(ages) & ~isnan(latencies)), latencies(~isnan(ages) & ~isnan(latencies)) * 1000, 1);
         [y_fit, ~] = polyval(P, ages, S);
 
-        % Plot polyfit throught data points
+        % Plot fit throught data points
         plot(ages, y_fit, 'Color', curColor, 'LineWidth', 2, 'DisplayName', ...
-             ['r=' num2str(all_unique_currents_r(iCurrent), 3) ' p=' num2str(all_unique_currents_p_fdr(iCurrent), 3)]);
+             ['\rho=' num2str(all_unique_currents_r(iCurrent), 3) ' p=' num2str(all_unique_currents_p_fdr(iCurrent), 3)]);
     end
 
 end