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Pericyte K<sub>ATP</sub> channel hyperactivity redistributes cortical blood flow in a CADASIL mouse model.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Methods › Computational modeling ↔ Network Code/main.m, lines 8–45 · score 0.89 · blood viscosity, Hemodynamic simulations, microvascular network, flow rate, vessel diameter, Blood flow
  2. [2] § Methods › Computational modeling ↔ Cell Level Code/SVD_pressure_diameter_analysis.m, lines 315–344 · score 0.79 · SVD model, mm Hg, pressure diameter, high KATP, KATP activity, Myogenic tone
  3. [3] § Methods › Computational modeling ↔ Network Code/main.m, lines 8–45 · score 0.74 · network hemodynamics, Model parameters, vessel diameter, mm Hg, electrophysiology, simulated
  4. [4] § Results › KATP hyperactivity and loss of capillary tone can affect cortical blood flow ↔ Cell Level Code/SVD_pressure_diameter_analysis.m, lines 315–344 · score 0.59 · SVD pressure diameter, mm Hg, myogenic tone, passive, curves, active
  5. [5] § Results › KATP hyperactivity and loss of capillary tone can affect cortical blood flow ↔ Cell Level Code/SVD_pressure_diameter_analysis.m, lines 380–455 · score 0.58 · simulations predicted, SVD simulations, 45 %, deep, diameter, Pressure

Paper

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The authors' code

MATLAB · 485 lines · 19 KB · other · 3 matches

  1. % SVD Story: Pressure-Diameter Analysis with K_ATP Conductance Change
  2. % Control: g_KATP = 0.03 (active tone)
  3. % SVD: g_KATP = 0.4 (active tone)
  4. % Passive: A_tone = 0 (no active tone, only passive mechanics)
  5. % Author: Niloufar Khakpour
  6. % Date: 02-09-2026
  8. clear, clc, close all
  10. %% Simulation Settings
  11. % Pressure levels to test (mmHg)
  12. pressure_levels = [5, 10, 20, 40, 60, 80];
  13. n_pressures = length(pressure_levels);
  15. % K_ATP conductance values
  16. g_KATP_control = 0.03; % Control condition
  17. g_KATP_SVD = 0.4; % SVD condition
  19. % Simulation time for each pressure level
  20. TMAX = 1750; % [s] simulation time for steady state
  21. dt = 4; % [s] time step
  22. tspan = (0:dt:TMAX)*1e3; % convert to ms
  24. nMC = 1;
  25. MC_id = '1111111000000000000001';
  27. %% Stimulation protocol (all off for baseline)
  28. NEstim = false;
  29. NOstim = false;
  30. current_stim = false;
  31. potassium_stim = false;
  33. %% Storage for results
  34. % Control condition
  35. control_voltage_ss = zeros(n_pressures, 1);
  36. control_diameter_ss = zeros(n_pressures, 1);
  37. control_calcium_ss = zeros(n_pressures, 1);
  39. % SVD condition
  40. SVD_voltage_ss = zeros(n_pressures, 1);
  41. SVD_diameter_ss = zeros(n_pressures, 1);
  42. SVD_calcium_ss = zeros(n_pressures, 1);
  44. % Passive condition (A_tone = 0)
  45. passive_voltage_ss = zeros(n_pressures, 1);
  46. passive_diameter_ss = zeros(n_pressures, 1);
  47. passive_calcium_ss = zeros(n_pressures, 1);
  49. %% ========== CALCULATE BACKGROUND CONDUCTANCES (ONCE, WITH CONTROL g_KATP) ==========
  50. fprintf('\n========== Calculating Background Conductances ==========\n');
  51. fprintf('Using Control g_KATP = %.3f nS\n', g_KATP_control);
  53. % Set up for baseline calculation (at lowest pressure)
  54. P.Pvals = pressure_levels(1) * ones(1, 5);
  55. P.Kvals = [3, 3, 3, 3, 3, 3];
  56. dTs = [4, 4, 6, 7.4, 6.3]/10*600;
  57. P.TP = [0, cumsum(dTs)]*1e3;
  58. P.TK = [0, cumsum(dTs)]*1e3;
  59. P.nMC = nMC;
  61. % Load parameters with CONTROL g_KATP
  62. parameters
  63. g_KATP = g_KATP_control;
  64. P.g_KATP = g_KATP;
  66. % Initial conditions
  67. P.Vm_clamp = false;
  68. P.ICs = true;
  69. initial_conditions
  70. P.ICs = false;
  72. % Calculate background currents ONCE
  73. P.Gbg_K = 0; P.Gbg_Na = 0; P.Gbg_Ca = 0; P.Gbg_Cl = 0; P.Pbg_Ca = 0;
  74. P.scaling_factor = 1;
  76. [~, nonstates] = equations_MC(0, Xinit_S, P, MC_id);
  78. I_Catotm1 = nonstates.I_Catotm;
  79. I_Natotm1 = nonstates.I_Natotm;
  80. I_Ktotm1 = nonstates.I_Ktotm;
  81. I_Cltotm1 = nonstates.I_Cltotm;
  82. I_SERCA = nonstates.I_SERCA;
  83. I_RyR = nonstates.I_RyR;
  84. I_IP3R = nonstates.I_IP3R;
  85. I_leak = nonstates.I_leak;
  86. E_Ca = nonstates.E_Ca; E_Na = nonstates.E_Na; E_K = nonstates.E_K; E_Cl = nonstates.E_Cl;
  88. % Calculate and STORE background conductances
  89. Gbg_K_fixed = -I_Ktotm1/(V_m - E_K);
  90. Gbg_Na_fixed = -I_Natotm1/(V_m - E_Na);
  91. Gbg_Ca_fixed = -I_Catotm1/(V_m - E_Ca);
  92. Gbg_Cl_fixed = -I_Cltotm1/(V_m - E_Cl);
  93. Pbg_Ca_fixed = -I_Catotm1/(V_m*((z_Ca*F)^2)/(R*temp)*(Ca_o - Ca_i*exp(V_m*z_Ca/RT_F))/(1 - exp(V_m*z_Ca/RT_F)));
  94. R_leak_fixed = R_leak * (I_SERCA - I_RyR - I_IP3R)/I_leak;
  95. Ca_u_fixed = ((( P.I_IP3bar.*((IP3./(IP3+P.K_IP3).*Ca_i./(Ca_i+P.K_actIP3).*h_IP3).^3).*Ca_i) + P.I_SERCAmax .* Ca_i ./ (Ca_i + P.K_mup)).*((P.R_leak + P.I_RyRbar .*R_10.^2)./((2.*P.F.*vol_u) ./ P.tau_tr))...
  96. + (P.R_leak .*Ca_i + (P.I_RyRbar .*R_10.^2 .* Ca_i) + (P.I_IP3bar.*((IP3./(IP3+P.K_IP3).*Ca_i./(Ca_i+P.K_actIP3).*h_IP3).^3).*Ca_i) + (P.I_SERCAmax .* Ca_i ./ (Ca_i + P.K_mup))))...
  97. ./ ((((2.*P.F.*vol_u) ./ P.tau_tr + P.I_IP3bar.*((IP3./(IP3+P.K_IP3).*Ca_i./(Ca_i+P.K_actIP3).*h_IP3).^3)).*(P.R_leak + P.I_RyRbar .*R_10.^2)./((2.*P.F.*vol_u) ./ P.tau_tr)) + ...
  98. ( P.I_IP3bar.*((IP3./(IP3+P.K_IP3).*Ca_i./(Ca_i+P.K_actIP3).*h_IP3).^3)));
  100. fprintf('Background conductances calculated:\n');
  101. fprintf(' Gbg_K = %.4f nS\n', Gbg_K_fixed);
  102. fprintf(' Gbg_Na = %.4f nS\n', Gbg_Na_fixed);
  103. fprintf(' Gbg_Ca = %.4f nS\n', Gbg_Ca_fixed);
  104. fprintf(' Gbg_Cl = %.4f nS\n', Gbg_Cl_fixed);
  105. fprintf('These will be used for BOTH Control and SVD conditions.\n');
  107. %% ========== CONTROL CONDITION ==========
  108. fprintf('\n========== CONTROL CONDITION (g_KATP = %.3f) ==========\n', g_KATP_control);
  110. for p_idx = 1:n_pressures
  111. fprintf('Running Control: Pressure = %d mmHg...\n', pressure_levels(p_idx));
  113. % Set up parameters for this pressure level
  114. P.Pvals = pressure_levels(p_idx) * ones(1, 5);
  115. P.Kvals = [3, 3, 3, 3, 3, 3];
  116. dTs = [4, 4, 6, 7.4, 6.3]/10*600;
  117. P.TP = [0, cumsum(dTs)]*1e3;
  118. P.TK = [0, cumsum(dTs)]*1e3;
  119. P.nMC = nMC;
  121. % Load parameters
  122. parameters
  124. % Set g_KATP to control value
  125. g_KATP = g_KATP_control;
  126. P.g_KATP = g_KATP;
  128. % Initial conditions
  129. P.Vm_clamp = false;
  130. P.ICs = true;
  131. initial_conditions
  132. P.ICs = false;
  134. % Use FIXED background conductances (calculated once above)
  135. P.Gbg_K = Gbg_K_fixed;
  136. P.Gbg_Na = Gbg_Na_fixed;
  137. P.Gbg_Ca = Gbg_Ca_fixed;
  138. P.Gbg_Cl = Gbg_Cl_fixed;
  139. P.Pbg_Ca = Pbg_Ca_fixed;
  140. P.scaling_factor = 1;
  141. P.R_leak = R_leak_fixed;
  142. P.Ca_u = Ca_u_fixed;
  145. % Solve ODEs
  146. [t, X] = ode15s(@(t,x)equations_MC(t, x, P, MC_id), tspan, Xinit_S);
  148. % Extract state variables
  149. T = t/1000; % convert to seconds
  150. ii = 0;
  151. if str2num(MC_id(1)), V_m = X(:, ii+1:ii+nMC); ii = ii + 1; end
  152. if str2num(MC_id(2)), Ca_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  153. if str2num(MC_id(3)), Na_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  154. if str2num(MC_id(4)), K_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  155. if str2num(MC_id(5)), Cl_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  156. if str2num(MC_id(6)), Ca_u = X(:, ii+1:ii+nMC); ii = ii + 1; end
  157. if str2num(MC_id(7)), Ca_r = X(:, ii+1:ii+nMC); ii = ii + 1; end
  158. if str2num(MC_id(22)), D_star = X(:, end); end
  160. % Store steady-state values (last 10% of simulation)
  161. ss_idx = round(0.9*length(T)):length(T);
  162. control_voltage_ss(p_idx) = mean(V_m(ss_idx));
  163. control_diameter_ss(p_idx) = mean(D_star(ss_idx));
  164. control_calcium_ss(p_idx) = mean(Ca_i(ss_idx));
  166. fprintf(' Steady-state: Vm = %.2f mV, D* = %.4f, Ca_i = %.2f nM\n', ...
  167. control_voltage_ss(p_idx), control_diameter_ss(p_idx), control_calcium_ss(p_idx)*1e6);
  168. end
  170. %% ========== SVD CONDITION ==========
  171. fprintf('\n========== SVD CONDITION (g_KATP = %.3f) ==========\n', g_KATP_SVD);
  173. for p_idx = 1:n_pressures
  174. fprintf('Running SVD: Pressure = %d mmHg...\n', pressure_levels(p_idx));
  176. % Set up parameters for this pressure level
  177. P.Pvals = pressure_levels(p_idx) * ones(1, 5);
  178. P.Kvals = [3, 3, 3, 3, 3, 3];
  179. dTs = [4, 4, 6, 7.4, 6.3]/10*600;
  180. P.TP = [0, cumsum(dTs)]*1e3;
  181. P.TK = [0, cumsum(dTs)]*1e3;
  182. P.nMC = nMC;
  184. % Load parameters
  185. parameters
  187. % Set g_KATP to SVD value (THIS IS THE KEY CHANGE!)
  188. g_KATP = g_KATP_SVD;
  189. P.g_KATP = g_KATP;
  191. % Initial conditions
  192. P.Vm_clamp = false;
  193. P.ICs = true;
  194. initial_conditions
  195. P.ICs = false;
  197. % Use SAME FIXED background conductances as Control (NO RECALCULATION!)
  198. P.Gbg_K = Gbg_K_fixed;
  199. P.Gbg_Na = Gbg_Na_fixed;
  200. P.Gbg_Ca = Gbg_Ca_fixed;
  201. P.Gbg_Cl = Gbg_Cl_fixed;
  202. P.Pbg_Ca = Pbg_Ca_fixed;
  203. P.scaling_factor = 1;
  204. P.R_leak = R_leak_fixed;
  205. P.Ca_u = Ca_u_fixed;
  206. % P.A_tone_scale = 1; % Active tone enabled
  208. % Solve ODEs
  209. [t, X] = ode15s(@(t,x)equations_MC(t, x, P, MC_id), tspan, Xinit_S);
  211. % Extract state variables
  212. T = t/1000; % convert to seconds
  213. ii = 0;
  214. if str2num(MC_id(1)), V_m = X(:, ii+1:ii+nMC); ii = ii + 1; end
  215. if str2num(MC_id(2)), Ca_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  216. if str2num(MC_id(3)), Na_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  217. if str2num(MC_id(4)), K_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  218. if str2num(MC_id(5)), Cl_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  219. if str2num(MC_id(6)), Ca_u = X(:, ii+1:ii+nMC); ii = ii + 1; end
  220. if str2num(MC_id(7)), Ca_r = X(:, ii+1:ii+nMC); ii = ii + 1; end
  221. if str2num(MC_id(22)), D_star = X(:, end); end
  223. % Store steady-state values (last 10% of simulation)
  224. ss_idx = round(0.9*length(T)):length(T);
  225. SVD_voltage_ss(p_idx) = mean(V_m(ss_idx));
  226. SVD_diameter_ss(p_idx) = mean(D_star(ss_idx));
  227. SVD_calcium_ss(p_idx) = mean(Ca_i(ss_idx));
  229. fprintf(' Steady-state: Vm = %.2f mV, D* = %.4f, Ca_i = %.2f nM\n', ...
  230. SVD_voltage_ss(p_idx), SVD_diameter_ss(p_idx), SVD_calcium_ss(p_idx)*1e6);
  231. end
  233. %% ========== PASSIVE CONDITION (A_tone = 0) ==========
  235. for p_idx = 1:n_pressures
  236. % fprintf('Running Passive: Pressure = %d mmHg...\n', pressure_levels(p_idx));
  238. % Set up parameters for this pressure level
  239. P.Pvals = pressure_levels(p_idx) * ones(1, 5);
  240. P.Kvals = [3, 3, 3, 3, 3, 3];
  241. dTs = [4, 4, 6, 7.4, 6.3]/10*600;
  242. P.TP = [0, cumsum(dTs)]*1e3;
  243. P.TK = [0, cumsum(dTs)]*1e3;
  244. P.nMC = nMC;
  246. % Load parameters
  247. parameters
  249. % Set g_KATP to control value (same baseline)
  250. g_KATP = g_KATP_control;
  251. P.g_KATP = g_KATP;
  253. % Initial conditions
  254. P.Vm_clamp = false;
  255. P.ICs = true;
  256. initial_conditions
  257. P.ICs = false;
  259. % Use SAME FIXED background conductances
  260. P.Gbg_K = Gbg_K_fixed;
  261. P.Gbg_Na = Gbg_Na_fixed;
  262. P.Gbg_Ca = Gbg_Ca_fixed;
  263. P.Gbg_Cl = Gbg_Cl_fixed;
  264. P.Pbg_Ca = Pbg_Ca_fixed;
  265. P.scaling_factor = 1;
  266. P.R_leak = R_leak_fixed;
  267. P.Ca_u = Ca_u_fixed;
  268. P.A_tone_scale = 0; % PASSIVE: Active tone DISABLED
  270. % Solve ODEs
  271. [t, X] = ode15s(@(t,x)equations_MC(t, x, P, MC_id), tspan, Xinit_S);
  273. % Extract state variables
  274. T = t/1000; % convert to seconds
  275. ii = 0;
  276. if str2num(MC_id(1)), V_m = X(:, ii+1:ii+nMC); ii = ii + 1; end
  277. if str2num(MC_id(2)), Ca_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  278. if str2num(MC_id(3)), Na_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  279. if str2num(MC_id(4)), K_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  280. if str2num(MC_id(5)), Cl_i = X(:, ii+1:ii+nMC); ii = ii + 1; end
  281. if str2num(MC_id(6)), Ca_u = X(:, ii+1:ii+nMC); ii = ii + 1; end
  282. if str2num(MC_id(7)), Ca_r = X(:, ii+1:ii+nMC); ii = ii + 1; end
  283. if str2num(MC_id(22)), D_star = X(:, end); end
  285. % Store steady-state values (last 10% of simulation)
  286. ss_idx = round(0.9*length(T)):length(T);
  287. passive_voltage_ss(p_idx) = mean(V_m(ss_idx));
  288. passive_diameter_ss(p_idx) = mean(D_star(ss_idx));
  289. passive_calcium_ss(p_idx) = mean(Ca_i(ss_idx));
  292. end
  294. %% ========== NORMALIZE DIAMETERS ==========
  295. fprintf('\nNormalizing diameters...\n');
  297. % Normalize diameters by dividing by diameter at 5 mmHg (first pressure point)
  298. % All curves will start at 1.0 at 5 mmHg
  299. control_diameter_norm = control_diameter_ss / control_diameter_ss(1);
  300. SVD_diameter_norm = SVD_diameter_ss / SVD_diameter_ss(1);
  301. passive_diameter_norm = passive_diameter_ss / passive_diameter_ss(1);
  304. %% ========== PLOTTING ==========
  305. fprintf('\nGenerating plots...\n');
  307. % Define colors
  308. color_WT = [0.2, 0.7, 0.2]; % Green for Active WT/Control
  309. color_SVD = [0.6, 0.2, 0.8]; % Purple for Active SVD
  310. color_passive = [0, 0.4, 1]; % Black for Passive
  312. % Create smooth curves using spline interpolation
  313. pressure_smooth = linspace(5, 80, 200);
  315. %% Figure 1: Absolute Diameter vs Pressure (starting from 0 mmHg with D* = 1)
  316. figure('Color', 'w', 'Position', [720, 100, 500, 400]);
  317. % Include 0 mmHg point where D* = 1 (resting/reference diameter)
  318. pressure_with_zero = [0, pressure_levels];
  319. control_D_with_zero = [1, control_diameter_ss'];
  320. SVD_D_with_zero = [1, SVD_diameter_ss'];
  321. passive_D_with_zero = [1, passive_diameter_ss'];
  323. % Create smooth curves starting from 0 mmHg
  324. pressure_smooth_from0 = linspace(0, 80, 200);
  325. control_abs_smooth = interp1(pressure_with_zero, control_D_with_zero, pressure_smooth_from0, 'pchip');
  326. SVD_abs_smooth = interp1(pressure_with_zero, SVD_D_with_zero, pressure_smooth_from0, 'pchip');
  327. passive_abs_smooth = interp1(pressure_with_zero, passive_D_with_zero, pressure_smooth_from0, 'pchip');
  329. plot(pressure_smooth_from0, passive_abs_smooth, '--', 'LineWidth', 2.5, 'Color', color_passive);
  330. hold on;
  331. plot(pressure_smooth_from0, control_abs_smooth, '-', 'LineWidth', 2.5, 'Color', color_WT);
  332. plot(pressure_smooth_from0, SVD_abs_smooth, '-', 'LineWidth', 2.5, 'Color', color_SVD);
  333. plot(pressure_with_zero, passive_D_with_zero, 's', 'MarkerSize', 8, 'MarkerFaceColor', color_passive, 'MarkerEdgeColor', color_passive);
  334. plot(pressure_with_zero, control_D_with_zero, 'o', 'MarkerSize', 8, 'MarkerFaceColor', color_WT, 'MarkerEdgeColor', color_WT);
  335. plot(pressure_with_zero, SVD_D_with_zero, 'o', 'MarkerSize', 8, 'MarkerFaceColor', color_SVD, 'MarkerEdgeColor', color_SVD);
  336. xlabel('Pressure (mmHg)', 'FontSize', 14, 'FontWeight', 'bold');
  337. ylabel('Diameter D*', 'FontSize', 14, 'FontWeight', 'bold');
  338. title('Predicted Myogenic tone at low and high KATP activity', 'FontSize', 14, 'FontWeight', 'bold');
  339. legend({'Passive model', 'Active WT model', 'Active SVD model'}, 'FontSize', 10, 'Location', 'northeast');
  340. xlim([0 80]);
  341. ylim([0 2]);
  342. yticks([0 0.5 1 1.5 2])
  343. set(gca, 'FontSize', 11, 'LineWidth', 1.5);
  344. box on;
  346. %% Figure 2: Membrane Voltage vs Pressure
  347. figure('Color', 'w', 'Position', [720, 100, 500, 400]);
  348. voltage_ctrl_smooth = interp1(pressure_levels, control_voltage_ss, pressure_smooth, 'pchip');
  349. voltage_SVD_smooth = interp1(pressure_levels, SVD_voltage_ss, pressure_smooth, 'pchip');
  350. plot(pressure_smooth, voltage_ctrl_smooth, '-', 'LineWidth', 2.5, 'Color', color_WT);
  351. hold on;
  352. plot(pressure_smooth, voltage_SVD_smooth, '-', 'LineWidth', 2.5, 'Color', color_SVD);
  353. plot(pressure_levels, control_voltage_ss, 'o', 'MarkerSize', 8, 'MarkerFaceColor', color_WT, 'MarkerEdgeColor', color_WT);
  354. plot(pressure_levels, SVD_voltage_ss, 'o', 'MarkerSize', 8, 'MarkerFaceColor', color_SVD, 'MarkerEdgeColor', color_SVD);
  355. xlabel('Pressure (mmHg)', 'FontSize', 14, 'FontWeight', 'bold');
  356. ylabel('Membrane Voltage (mV)', 'FontSize', 14, 'FontWeight', 'bold');
  357. title('Voltage vs Pressure', 'FontSize', 14, 'FontWeight', 'bold');
  358. legend({'Active WT model', 'Active SVD model'}, 'FontSize', 10, 'Location', 'best');
  359. xlim([5 80]);
  360. set(gca, 'FontSize', 11, 'LineWidth', 1.5);
  361. box on;
  363. %% Figure 3: Intracellular Calcium vs Pressure
  364. figure('Color', 'w', 'Position', [1240, 100, 500, 400]);
  365. calcium_ctrl_smooth = interp1(pressure_levels, control_calcium_ss*1e6, pressure_smooth, 'pchip');
  366. calcium_SVD_smooth = interp1(pressure_levels, SVD_calcium_ss*1e6, pressure_smooth, 'pchip');
  367. plot(pressure_smooth, calcium_ctrl_smooth, '-', 'LineWidth', 2.5, 'Color', color_WT);
  368. hold on;
  369. plot(pressure_smooth, calcium_SVD_smooth, '-', 'LineWidth', 2.5, 'Color', color_SVD);
  370. plot(pressure_levels, control_calcium_ss*1e6, 'o', 'MarkerSize', 8, 'MarkerFaceColor', color_WT, 'MarkerEdgeColor', color_WT);
  371. plot(pressure_levels, SVD_calcium_ss*1e6, 'o', 'MarkerSize', 8, 'MarkerFaceColor', color_SVD, 'MarkerEdgeColor', color_SVD);
  372. xlabel('Pressure (mmHg)', 'FontSize', 14, 'FontWeight', 'bold');
  373. ylabel('[Ca^{2+}]_i (nM)', 'FontSize', 14, 'FontWeight', 'bold');
  374. title('Intracellular Calcium vs Pressure', 'FontSize', 14, 'FontWeight', 'bold');
  375. legend({'Active WT model', 'Active SVD model'}, 'FontSize', 10, 'Location', 'best');
  376. xlim([5 80]);
  377. set(gca, 'FontSize', 11, 'LineWidth', 1.5);
  378. box on;
  380. %% ========== FIGURE 4: Vm COMPARISON WITH EXPERIMENTAL DATA ==========
  381. % Experimental Vm data
  382. WT_exp = [-23.696, -26.337, -21.709, -24.351, -28.388, -31.19, -35.064, ...
  383. -26.249, -29.977, -32.618, -35.258, -46.426];
  385. SVD_exp = [-27.414, -37.570, -43.039, -45.679, -47.257, -45.867, ...
  386. -51.929, -53.663, -56.625, -58.163, -56.796, -58.967, ...
  387. -61.83, -67.570, -69.913];
  389. % Calculate experimental statistics
  390. WT_exp_mean = mean(WT_exp);
  391. WT_exp_sd = std(WT_exp);
  392. SVD_exp_mean = mean(SVD_exp);
  393. SVD_exp_sd = std(SVD_exp);
  395. % Get simulated Vm at 40 mmHg (index 4 in pressure_levels)
  396. % pressure_levels = [5, 10, 20, 40, 60, 80], so 40 mmHg is at index 4
  397. idx_40mmHg = find(pressure_levels == 40);
  398. WT_sim_mean = control_voltage_ss(idx_40mmHg);
  399. SVD_sim_mean = SVD_voltage_ss(idx_40mmHg);
  401. % Create Vm comparison figure
  402. figure('Color', 'w', 'Position', [200 200 520 400]);
  403. hold on;
  405. x_WT = 1;
  406. x_SVD = 2;
  407. bar_width = 0.45;
  409. % Colors matching reference figure
  410. green_bar = [0.13 0.55 0.13]; % dark green for WT bar
  411. purple_bar = [0.40 0.0 0.60]; % deep purple for SVD bar
  412. % purple_scatter = [0.40 0.0 0.60]; % same purple for SVD scatter
  413. purple_scatter = [0.55 0.20 0.60]; % same purple for SVD scatter
  414. green_scatter=[0.20 0.65 0.20];
  415. % Opaque bars for simulated predictions
  416. bar_WT = bar(x_WT, WT_sim_mean, bar_width, 'FaceColor', green_bar, ...
  417. 'FaceAlpha', 1.0, 'EdgeColor', 'none');
  418. bar_SVD = bar(x_SVD, SVD_sim_mean, bar_width, 'FaceColor', purple_bar, ...
  419. 'FaceAlpha', 1.0, 'EdgeColor', 'none');
  421. % Experimental data scatter
  422. % WT: open black circles
  423. sc_WT = scatter(repmat(x_WT, 1, length(WT_exp)), WT_exp, 40, ...
  424. 'o', 'filled', 'MarkerFaceColor', green_scatter, 'MarkerEdgeColor', 'k', 'LineWidth', 0.8);
  425. % SVD: filled purple squares with black edge
  426. sc_SVD = scatter(repmat(x_SVD, 1, length(SVD_exp)), SVD_exp, 40, ...
  427. 's', 'filled', 'MarkerFaceColor', purple_scatter, 'MarkerEdgeColor', 'k', 'LineWidth', 0.8);
  429. xlim([0.3 2.7]);
  430. ylim([-75 -15]);
  432. set(gca, ...
  433. 'XTick',[x_WT x_SVD], ...
  434. 'XTickLabel',{'WT','SVD'}, ...
  435. 'FontSize',12, ...
  436. 'LineWidth',1.2, ...
  437. 'FontName','Arial', ...
  438. 'FontWeight','bold');
  440. % ylabel('Membrane potential (mV)', 'FontSize', 13, 'FontWeight', 'bold', 'FontName', 'Arial');
  441. % title({'Predicted Hyperpolarization with'; 'K_{ATP} Activity'}, 'FontSize', 13, 'FontWeight', 'bold', 'FontName', 'Arial');
  442. legend([sc_WT sc_SVD bar_WT bar_SVD], ...
  443. {'WT Experimental', 'SVD Experimental', 'WT Simulated', 'SVD Simulated'}, ...
  444. 'FontSize', 9, 'Location', 'southwest', 'FontName', 'Arial');
  445. box on; grid off;
  447. % Print Vm comparison summary
  448. fprintf('\n========== Vm COMPARISON SUMMARY ==========\n');
  449. fprintf('Experimental Data:\n');
  450. fprintf(' WT: Mean = %.2f mV, SD = %.2f mV (n=%d)\n', WT_exp_mean, WT_exp_sd, length(WT_exp));
  451. fprintf(' SVD: Mean = %.2f mV, SD = %.2f mV (n=%d)\n', SVD_exp_mean, SVD_exp_sd, length(SVD_exp));
  452. fprintf('Simulated Data (at 40 mmHg):\n');
  453. fprintf(' WT: Vm = %.2f mV\n', WT_sim_mean);
  454. fprintf(' SVD: Vm = %.2f mV\n', SVD_sim_mean);
  455. fprintf('============================================\n');
  457. %% Save results to file
  458. fprintf('\nSaving results...\n');
  459. results.pressure_levels = pressure_levels;
  460. results.control.voltage = control_voltage_ss;
  461. results.control.diameter = control_diameter_ss;
  462. results.control.calcium = control_calcium_ss;
  463. results.control.diameter_norm = control_diameter_norm;
  464. results.SVD.voltage = SVD_voltage_ss;
  465. results.SVD.diameter = SVD_diameter_ss;
  466. results.SVD.calcium = SVD_calcium_ss;
  467. results.SVD.diameter_norm = SVD_diameter_norm;
  468. results.passive.voltage = passive_voltage_ss;
  469. results.passive.diameter = passive_diameter_ss;
  470. results.passive.calcium = passive_calcium_ss;
  471. results.passive.diameter_norm = passive_diameter_norm;
  472. results.g_KATP_control = g_KATP_control;
  473. results.g_KATP_SVD = g_KATP_SVD;
  474. results.experimental.WT_Vm = WT_exp;
  475. results.experimental.SVD_Vm = SVD_exp;
  476. results.experimental.WT_Vm_mean = WT_exp_mean;
  477. results.experimental.SVD_Vm_mean = SVD_exp_mean;
  479. save('SVD_analysis_results.mat', 'results');
  481. % Print Summary
  482. fprintf('\n========== ANALYSIS COMPLETE ==========\n');
  483. fprintf('Results saved to: SVD_analysis_results.mat\n');
  484. fprintf('Figures displayed.\n\n');

SVD_pressure_diameter_analysis.m at commit 36683d0, under other · at the source

Overview

Authors: Danielle A. Jeffrey1, Eric W. Prince2, Niloufar Khakpour3, Hannah R. Ferris1, Colin H. Peters4, Gregory Seedorf1, Phinea Z. Romero1, Mayra Bueno Guerrero1, Abigail N. Russell1, Katherine Glodoski1, Gregory L. Futia5, Stephanie K. Bonney1, Michael A. Thornton6, Emily A. Gibson5, Catherine Proenza4, Anastacia M. Garcia7, Nikolaos M. Tsoukias3, Fabrice Dabertrand1,8
ORCID iDs: Danielle A. Jeffrey, Emily A. Gibson, Fabrice Dabertrand
  1. Department of Anesthesiology, University of Colorado Anschutz Medical Campus,Aurora, CO USA
  2. Department of Neurosurgery, University of Colorado Anschutz Medical Campus,Aurora, CO USA
  3. Department of Biomedical Engineering, Florida International University,Miami, FL USA
  4. Department of Physiology & Biophysics and Department of Medicine, Division of Cardiology, University of Colorado Anschutz Medical Campus,Aurora, CO USA
  5. Department of Bioengineering, University of Colorado Anschutz Medical Campus,Aurora, CO USA
  6. The Salk Institute for Biological Studies,La Jolla, CA USA
  7. Department of Pediatrics, Division of Cardiology, University of Colorado Anschutz Medical Campus,Aurora, CO USA
  8. Department of Pharmacology, University of Colorado Anschutz Medical Campus,Aurora, CO USA
Institutions: University of Colorado Anschutz (United States); Florida International University (United States); Salk Institute for Biological Studies (United States)
Journal: Nature cardiovascular research, volume 5, issue 8, pages 725-743
Dates: received 8 November 2025; accepted 26 June 2026; published online 3 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44161-026-00844-0 · PMID 42547848 · PMCID PMC13461346 · OpenAlex W7172249082
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), stroke (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Evoked potentials
Keywords: Diseases of the nervous system, Stroke
MeSH: CADASIL*, Cerebral Cortex*, Cerebrovascular Circulation*, KATP Channels*, Microcirculation*, Pericytes*, Adenosine Triphosphate, Animals, Blood Flow Velocity, Disease Models, Animal, Energy Metabolism, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Spatial Transcriptomics (* major topic)
Topic: Cerebrovascular and genetic disorders (Neurology, Medicine), according to OpenAlex
Funding: NINDS (RF1NS129022, RF1NS140137, F31HL184931, R01NS119971); HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) (R01HL136636, F31HL170645); Leducq Foundation (22CVD01 BRENDA); NIGMS (5T32GM007635)
Citations: cited by 2 papers (Europe PMC); 66 references in the paper

Abstract

Cerebral hemodynamic dysfunction is a key driver of unhealthy brain aging. Impaired microcirculatory reactivity leads to uneven perfusion, rendering deeper brain regions more vulnerable and thereby contributing to cognitive decline. Yet how capillaries contribute to these deficits remains poorly defined. Here we combined spatial transcriptomics with in vivo two-photon and three-photon imaging to measure layer-specific cerebral blood flow in control and small vessel disease model mice (CADASIL TgNotch3R169C). We found downregulation of ATP-synthesizing genes, indicating microvascular metabolic impairment that paralleled impaired pericyte bioenergetics. This energy deficit coincided with diminished tone in the arteriole–capillary transitional zone and reduced deep-layer perfusion. Complementary electrophysiology, ex vivo and in silico approaches, revealed that hyperactive KATP channels in pericytes drive a redistribution of cerebral blood flow toward superficial cortical layers. This loss of spatial perfusion equalization, despite preserved global flow, contributed to deep-layer hypoperfusion, establishing a previously underrecognized but tractable vascular function disrupted in aging pathology.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

ntsoukias/SVD-Blood-Flow-Simulation

License: other
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 36683d06e7ee497a35a885d00ab2a3079d01a890, 11 April 2026
Languages: MATLAB (4)
Size: 24 files, 4 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
6 files
At the source: github.com/ntsoukias/SVD-Blood-Flow-Simulation

Zenodo 20722612

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
6 files
At the source:

Code availability

In silico modeling code can be found at https://github.com/ntsoukias/SVD-Blood-Flow-Simulation. GitHub will remain the active code repository and Zenodo provides the fixed, citable version for the manuscript at 10.5281/zenodo.20722612 (ref. 66).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

All data including those that support the summary plots and other findings in this paper are available. Raw spatial transcriptomic sequencing data and processed count matrices are available from the corresponding author upon reasonable request while the processed spatial transcriptomics is available at the Gene Expression Omnibus, accession no. GSE335702 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE335702). Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 3, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 2 keywords, 16 MeSH terms, 4 funders, 65 references.

Cite

This paper

Jeffrey, D. A., Prince, E. W., Khakpour, N., Ferris, H. R., Peters, C. H., Seedorf, G., Romero, P. Z., Bueno Guerrero, M., Russell, A. N., Glodoski, K., Futia, G. L., Bonney, S. K., Thornton, M. A., Gibson, E. A., Proenza, C., Garcia, A. M., Tsoukias, N. M., & Dabertrand, F. (2026). Pericyte K<sub>ATP</sub> channel hyperactivity redistributes cortical blood flow in a CADASIL mouse model. Nature cardiovascular research, 5(8), 725-743. https://doi.org/10.1038/s44161-026-00844-0

BibTeX

@article{jeffrey2026pericyte,
author = {Jeffrey, Danielle A. and Prince, Eric W. and Khakpour, Niloufar and Ferris, Hannah R. and Peters, Colin H. and Seedorf, Gregory and Romero, Phinea Z. and Bueno Guerrero, Mayra and Russell, Abigail N. and Glodoski, Katherine and Futia, Gregory L. and Bonney, Stephanie K. and Thornton, Michael A. and Gibson, Emily A. and Proenza, Catherine and Garcia, Anastacia M. and Tsoukias, Nikolaos M. and Dabertrand, Fabrice},
title = {{Pericyte K\<sub\>ATP\</sub\> channel hyperactivity redistributes cortical blood flow in a CADASIL mouse model}},
journal = {Nature cardiovascular research},
year = {2026},
month = aug,
volume = {5},
number = {8},
pages = {725--743},
publisher = {Nature Portfolio},
issn = {2731-0590},
doi = {10.1038/s44161-026-00844-0},
url = {https://doi.org/10.1038/s44161-026-00844-0},
pmid = {42547848},
pmcid = {PMC13461346}
}

RIS

TY - JOUR
AU - Jeffrey, Danielle A.
AU - Prince, Eric W.
AU - Khakpour, Niloufar
AU - Ferris, Hannah R.
AU - Peters, Colin H.
AU - Seedorf, Gregory
AU - Romero, Phinea Z.
AU - Bueno Guerrero, Mayra
AU - Russell, Abigail N.
AU - Glodoski, Katherine
AU - Futia, Gregory L.
AU - Bonney, Stephanie K.
AU - Thornton, Michael A.
AU - Gibson, Emily A.
AU - Proenza, Catherine
AU - Garcia, Anastacia M.
AU - Tsoukias, Nikolaos M.
AU - Dabertrand, Fabrice
TI - Pericyte K<sub>ATP</sub> channel hyperactivity redistributes cortical blood flow in a CADASIL mouse model
T2 - Nature cardiovascular research
J2 - Nat Cardiovasc Res
PY - 2026
DA - 2026/08/03
VL - 5
IS - 8
SP - 725
EP - 743
SN - 2731-0590
PB - Nature Portfolio
DO - 10.1038/s44161-026-00844-0
UR - https://doi.org/10.1038/s44161-026-00844-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s44161-026-00844-0",
"type": "article-journal",
"title": "Pericyte K<sub>ATP</sub> channel hyperactivity redistributes cortical blood flow in a CADASIL mouse model",
"container-title": "Nature cardiovascular research",
"author": [
{
"family": "Jeffrey",
"given": "Danielle A."
},
{
"family": "Prince",
"given": "Eric W."
},
{
"family": "Khakpour",
"given": "Niloufar"
},
{
"family": "Ferris",
"given": "Hannah R."
},
{
"family": "Peters",
"given": "Colin H."
},
{
"family": "Seedorf",
"given": "Gregory"
},
{
"family": "Romero",
"given": "Phinea Z."
},
{
"family": "Bueno Guerrero",
"given": "Mayra"
},
{
"family": "Russell",
"given": "Abigail N."
},
{
"family": "Glodoski",
"given": "Katherine"
},
{
"family": "Futia",
"given": "Gregory L."
},
{
"family": "Bonney",
"given": "Stephanie K."
},
{
"family": "Thornton",
"given": "Michael A."
},
{
"family": "Gibson",
"given": "Emily A."
},
{
"family": "Proenza",
"given": "Catherine"
},
{
"family": "Garcia",
"given": "Anastacia M."
},
{
"family": "Tsoukias",
"given": "Nikolaos M."
},
{
"family": "Dabertrand",
"given": "Fabrice"
}
],
"container-title-short": "Nat Cardiovasc Res",
"volume": "5",
"issue": "8",
"page": "725-743",
"DOI": "10.1038/s44161-026-00844-0",
"PMID": "42547848",
"PMCID": "PMC13461346",
"ISSN": "2731-0590",
"publisher": "Nature Portfolio",
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"language": "en",
"issued": {
"date-parts": [
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2026,
8,
3
]
]
}
}

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