From 67c987f86537c75f6ba56f84346d1f086ee513f4 Mon Sep 17 00:00:00 2001
From: Jim Pollaro <jim.pollaro@gmail.com>
Date: Mon, 6 Jul 2026 15:39:22 -0500
Subject: [PATCH] merge conflict from cherry pick

---
 +nla/+qualityControl/checkHeadMotion.m      |  34 +++++++++++++-------
 +nla/+qualityControl/checkNormalityWithKS.m |  33 ++++++++++++-------
 +nla/NetworkAtlas.m                         |  14 ++++++--
 +nla/removeNetworks.m                       |   3 +-
 NLAQualityControl.mlapp                     | Bin 14666 -> 14561 bytes
 5 files changed, 57 insertions(+), 27 deletions(-)

diff --git a/+nla/+qualityControl/checkHeadMotion.m b/+nla/+qualityControl/checkHeadMotion.m
index a09d25c1..c937609a 100755
--- a/+nla/+qualityControl/checkHeadMotion.m
+++ b/+nla/+qualityControl/checkHeadMotion.m
@@ -1,14 +1,24 @@
-function checkHeadMotion(fig, input_struct, motion)
+function checkHeadMotion(fig, input_struct, motion, remove_index)
     
+    network_atlas = input_struct.net_atlas;
+    functional_connectivity = input_struct.func_conn;
+    if remove_index ~= 0
+        [new_netatlas, functional_connectivity] = nla.removeNetworks(input_struct.net_atlas, input_struct.net_atlas.nets(remove_index).name, strcat(input_struct.net_atlas.name, '_-', input_struct.net_atlas.nets(remove_index).name), input_struct.func_conn);
+        network_atlas = nla.NetworkAtlas(new_netatlas);
+        if ~isa(functional_connectivity, 'nla.TriMatrix')
+            functional_connectivity = nla.TriMatrix(functional_connectivity);
+        end        
+    end
+
     prog = uiprogressdlg(fig, 'Title', 'Generating figures', 'Message', 'Generating head motion figures');
     prog.Value = 0.02;
-    distances = nla.helpers.euclidianDistanceROIs(input_struct.net_atlas);
+    distances = nla.helpers.euclidianDistanceROIs(network_atlas);
     prog.Value = 0.75;
-    [r_vec, p_vec] = corr(motion, input_struct.func_conn.v', 'type', 'Pearson');
+    [r_vec, p_vec] = corr(motion, functional_connectivity.v', 'type', 'Pearson');
     
-    prob = nla.TriMatrix(input_struct.net_atlas.numROIs());
-    r = nla.TriMatrix(input_struct.net_atlas.numROIs());
-    h = nla.TriMatrix(input_struct.net_atlas.numROIs(), 'logical');
+    prob = nla.TriMatrix(network_atlas.numROIs());
+    r = nla.TriMatrix(network_atlas.numROIs());
+    h = nla.TriMatrix(network_atlas.numROIs(), 'logical');
     prob.v = p_vec';
     r.v = r_vec';
     h.v = nla.lib.fdr_bh(prob.v);
@@ -25,7 +35,7 @@ function checkHeadMotion(fig, input_struct, motion)
     ulimit = 0.3;
     
     fig = nla.gfx.createFigure(1800, 900);
-    matrix_plot = nla.gfx.plots.MatrixPlot(fig, "FC-motion correlation (Pearson's r)", r, input_struct.net_atlas.nets,...
+    matrix_plot = nla.gfx.plots.MatrixPlot(fig, "FC-motion correlation (Pearson's r)", r, network_atlas.nets,...
         nla.gfx.FigSize.LARGE, 'lower_limit', llimit, 'upper_limit', ulimit);
     matrix_plot.displayImage();
     width = matrix_plot.image_dimensions("image_width");
@@ -35,14 +45,14 @@ function checkHeadMotion(fig, input_struct, motion)
 
     ax = subplot('Position', [0.780, 0.540, 0.20, 0.40]);
     nla.gfx.setTitle(ax, sprintf("FC-motion correlation (Pearson's r) (q < 0.05)\n"));
-    nla.gfx.drawROIsOnCortex(ax, input_struct.net_atlas, ctx, mesh_alpha, ROI_radius, nla.gfx.ViewPos.DORSAL, false,...
+    nla.gfx.drawROIsOnCortex(ax, network_atlas, ctx, mesh_alpha, ROI_radius, nla.gfx.ViewPos.DORSAL, false,...
         nla.gfx.BrainColorMode.NONE);
     
-    for col = 1:input_struct.net_atlas.numROIs()
-        for row = (col + 1):input_struct.net_atlas.numROIs()
+    for col = 1:network_atlas.numROIs()
+        for row = (col + 1):network_atlas.numROIs()
             if h.get(row, col)
-                pos1 = input_struct.net_atlas.ROIs(row).pos;
-                pos2 = input_struct.net_atlas.ROIs(col).pos;
+                pos1 = network_atlas.ROIs(row).pos;
+                pos2 = network_atlas.ROIs(col).pos;
                 
                 edge_color = nla.gfx.valToColor(r.get(row, col), llimit, ulimit, color_map);
                 
diff --git a/+nla/+qualityControl/checkNormalityWithKS.m b/+nla/+qualityControl/checkNormalityWithKS.m
index 19ec0634..3bb8eacb 100644
--- a/+nla/+qualityControl/checkNormalityWithKS.m
+++ b/+nla/+qualityControl/checkNormalityWithKS.m
@@ -1,4 +1,4 @@
-function checkNormalityWithKS(fig, input_struct, test_pool)
+function checkNormalityWithKS(fig, input_struct, test_pool, remove_index)
 
     prog = uiprogressdlg(...
         fig, 'Title', 'Checking Normaility', 'Message', 'Running Kolmogorov-Smirnov Test'...
@@ -9,25 +9,31 @@ function checkNormalityWithKS(fig, input_struct, test_pool)
     edge_test_result = test_pool.runEdgeTest(input_struct);
 
     prog.Value = 0.5;
-    ks_result = runKolmogorovSmirnovTest(input_struct, edge_test_result);
+    ks_result = runKolmogorovSmirnovTest(input_struct, edge_test_result, remove_index);
 
     prog.Value = 0.75;
-    qcKSOutput(ks_result.p, input_struct)
+    qcKSOutput(ks_result.p, input_struct, remove_index)
 
 end
 
-function ks_result = runKolmogorovSmirnovTest(input_struct, edge_result)
+function ks_result = runKolmogorovSmirnovTest(input_struct, edge_result, remove_index)
     import nla.TriMatrix nla.TriMatrixDiag
 
+    network_atlas = input_struct.net_atlas;
+    if remove_index ~= 0
+        [new_netatlas] = nla.removeNetworks(input_struct.net_atlas, input_struct.net_atlas.nets(remove_index).name, strcat(input_struct.net_atlas.name, '_-', input_struct.net_atlas.nets(remove_index).name));
+        network_atlas = nla.NetworkAtlas(new_netatlas);
+    end
+
     ks_result = struct();
-    number_of_networks = input_struct.net_atlas.numNets();
+    number_of_networks = network_atlas.numNets();
     ks_result.p = TriMatrix(number_of_networks, TriMatrixDiag.KEEP_DIAGONAL);
     ks_result.ks = TriMatrix(number_of_networks, TriMatrixDiag.KEEP_DIAGONAL);
 
     for network1 = 1:number_of_networks
         for network2 = 1:network1
-            network_rho = edge_result.coeff.get(input_struct.net_atlas.nets(network1).indexes,...
-                input_struct.net_atlas.nets(network2).indexes);
+            network_rho = edge_result.coeff.get(network_atlas.nets(network1).indexes,...
+                network_atlas.nets(network2).indexes);
             [~, p, ks] = kstest(network_rho);
             ks_result.p.set(network1, network2, p);
             ks_result.ks.set(network1, network2, ks);
@@ -35,7 +41,7 @@ function checkNormalityWithKS(fig, input_struct, test_pool)
     end
 end
 
-function qcKSOutput(ks_result_p_value, edge_test_options)
+function qcKSOutput(ks_result_p_value, edge_test_options, remove_index)
     % This will open the qc figure for the KS test
     
     network_test_options = nla.net.genBaseInputs();
@@ -45,15 +51,20 @@ function qcKSOutput(ks_result_p_value, edge_test_options)
     edge_test_options.prob_max = 0.05;
     default_discrete_colors = 1000;
 
-    [~, p_value_max] = network_test_options.fdr_correction.correct(edge_test_options.net_atlas,...
-        edge_test_options, ks_result_p_value);
+    network_atlas = edge_test_options.net_atlas;
+    if remove_index ~= 0
+        [new_netatlas] = nla.removeNetworks(edge_test_options.net_atlas, edge_test_options.net_atlas.nets(remove_index).name, strcat(edge_test_options.net_atlas.name, '-', edge_test_options.net_atlas.nets(remove_index).name));
+        network_atlas = nla.NetworkAtlas(new_netatlas);
+    end
+
+    [~, p_value_max] = network_test_options.fdr_correction.correct(network_atlas, edge_test_options, ks_result_p_value);
 
     color_map = nla.net.result.NetworkResultPlotParameter.getColormap(default_discrete_colors,...
         p_value_max);
 
     fig = nla.gfx.createFigure();
     % Also remember to move this in read the docs
-    matrix_plot = nla.gfx.plots.MatrixPlot(fig, sprintf("Non-permuted Kolmogorov-Smirnov Test p-value\nSmaller values are less normal"), ks_result_p_value, edge_test_options.net_atlas.nets, nla.gfx.FigSize.LARGE,...
+    matrix_plot = nla.gfx.plots.MatrixPlot(fig, sprintf("Non-permuted Kolmogorov-Smirnov Test p-value\nSmaller values are less normal"), ks_result_p_value, network_atlas.nets, nla.gfx.FigSize.LARGE,...
         'lower_limit', 0.00, 'upper_limit', p_value_max, 'color_map', color_map);
     matrix_plot.displayImage();
     width = matrix_plot.image_dimensions('image_width');
diff --git a/+nla/NetworkAtlas.m b/+nla/NetworkAtlas.m
index d47740de..b1023c31 100755
--- a/+nla/NetworkAtlas.m
+++ b/+nla/NetworkAtlas.m
@@ -12,10 +12,13 @@
     % :param space: (Optional) The mesh that the atlas` ROI locations/parcels are in. Two options - ``Talairach (TT)`` or ``Montreal Neurological Institute (MNI)``
 
     properties (SetAccess = private)
-        nets % This is the net_names
-
+        nets 
+        net_names
         ROIs
+        ROI_key
+        ROI_pos
         ROI_order
+        net_colors
         name
         space
         anat = false;
@@ -37,7 +40,8 @@
             end
             
             net_names = net_struct.net_names;
-            
+            obj.net_names = net_names;
+
             net_count = numel(net_names);
             ROI_count = size(net_struct.ROI_key, 1);
             
@@ -45,6 +49,7 @@
             if isfield(net_struct, 'net_colors')
                 net_colors = net_struct.net_colors;
             end
+            obj.net_colors = net_colors;
             
             ROI_positions = zeros(ROI_count, 3);
             if isfield(net_struct, 'ROI_pos')
@@ -60,6 +65,9 @@
                 net_struct.ROI_order = net_struct.ROI_order(sort_idx);
                 ROI_positions = ROI_positions(sort_idx, :);
             end
+            obj.ROI_pos = ROI_positions;
+            obj.ROI_key = net_struct.ROI_key;
+            obj.ROI_order = net_struct.ROI_order;
             
             %% Network atlas name
             obj.name = net_struct.name;
diff --git a/+nla/removeNetworks.m b/+nla/removeNetworks.m
index e8de1295..f16c1c3d 100755
--- a/+nla/removeNetworks.m
+++ b/+nla/removeNetworks.m
@@ -78,7 +78,8 @@
     
     %% Functional connectivity (optional)
     if exist('fc_in', 'var')
-        fc_ordered = fc_in(atlas_in.ROI_order, atlas_in.ROI_order, :);
+        fc_in_matrix = fc_in.asMatrix();
+        fc_ordered = fc_in_matrix(atlas_in.ROI_order, atlas_in.ROI_order, :);
         fc_reduced = fc_ordered(ROI_mask, ROI_mask, :);
         ROI_order_inverse(atlas_out.ROI_order) = [1:numel(atlas_out.ROI_order)]';
         fc_out = fc_reduced(ROI_order_inverse, ROI_order_inverse, :);
diff --git a/NLAQualityControl.mlapp b/NLAQualityControl.mlapp
index 97ded08b78c03e85986dc5fa1ccfadf293310396..cfb56c2c9dde121923c8237b248cf3f51fe938aa 100644
GIT binary patch
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