OSCR

A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation.

Code ↔ Paper

9 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 9 matches
  1. [1] § Results › Hybrid μECoG for functionally targeted multi-area laminar recordings in the marmoset ↔ Figure_Simulation_MarmoMT_targeting.m, lines 1–36 · score 0.96 · Monte Carlo simulation, Random MT penetrations, RF distance expected, V1 MT RF, RF alignment, retinotopic alignment
  2. [2] § STAR★Methods › Quantification and statistical analysis ↔ Figure_Simulation_MarmoMT_targeting.m, lines 1–36 · score 0.92 · Monte Carlo simulation, Random MT probe, V1 MT RF, retinotopic alignment, retinotopic map, laminar probe
  3. [3] § STAR★Methods › Quantification and statistical analysis ↔ FigureECoGwithLaminarCorrelation.m, lines 89–99 · score 0.58 · 100–200 Hz, 10–20 Hz, correlation, filtered, laminar, 100 Hz
  4. [4] § Results › Hybrid μECoG for optogenetic mapping of local and inter-areal interactions ↔ FigureStats_FeedforwardOpto32ChAtlasProbe_MarmosetMT_MUA.m, lines 115–138 · score 0.55 · Bonferroni corrected, sided signrank, Feedforward, probe, modulation, MUA
  5. [5] § STAR★Methods › Quantification and statistical analysis ↔ buildRFstruct2.m, lines 1–61 · score 0.53 · band pass filtered, EcoG, 200 Hz, 20 Hz, 100 Hz, channel
  6. [6] § Results › Hybrid μECoG for optogenetic mapping of local and inter-areal interactions ↔ FigureStats_FeedforwardOpto32ChAtlasProbe_MarmosetMT_MUA.m, lines 115–138 · score 0.52 · Bonferroni corrected, sided signrank, feedforward, modulation, MUA, MT
  7. [7] § STAR★Methods › Quantification and statistical analysis ↔ Figure_catECoG_bar_sweep_trajectory_area17.m, lines 423–470 · score 0.52 · peak envelope, trajectory, amplitude, SNR, linearly, thresholded
  8. [8] § Results › Hybrid μECoGs enable multi-scale investigation of cortical activity ↔ FigureECoGwithLaminarCorrelation.m, lines 89–99 · score 0.52 · 100–200 Hz, 10–20 Hz, correlating, laminar, 100 Hz
  9. [9] § STAR★Methods › Quantification and statistical analysis ↔ buildRFstruct2.m, lines 1–61 · score 0.51 · band pass filtered, delay, PSTH, smoothed, 60 Hz, 90 Hz

Paper

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The authors' code

MATLAB · 763 lines · 17 KB · CC-BY-4.0 · 2 matches

  1. % % This script analyzes retinotopic alignment between laminar probes in V1 and MT
  2. % using µECoG maps from Marmoset001Block37.
  3. %
  4. % The first part visualizes the retinotopic maps (µECoG and laminar RFs) to
  5. % confirm spatial correspondence between the recording modalities.
  6. %
  7. % The main analysis simulates random MT probe insertions across the MT µECoG
  8. % retinotopic map and compares the resulting RF distances to the actual
  9. % V1–MT laminar probe RF distance. This Monte-Carlo simulation estimates
  10. % how well the experimentally chosen MT penetration aligns with the V1 site
  11. % relative to chance targeting.
  12. %
  13. % Output:
  14. % - Distribution of RF distances expected from random MT penetrations
  15. % - Actual V1–MT RF distance
  16. % - Percentile of the real penetration relative to random targeting
  17. % - Bootstrap confidence interval for the V1–MT RF distance
  18. %
  19. % Used in manuscript revision to demonstrate that µECoG-guided probe
  20. % placement improves cross-area RF alignment compared to random insertion.
  22. % P. Jendritza March 2026
  24. % data from Marmoset001Block37
  26. load marmo_RFmapData_V1
  27. disp('Loaded marmo_RFmapData_V1')
  29. load marmo_RFmapData_MT
  30. disp('Loaded marmo_RFmapData_MT')
  32. load marmo_RFprobe_V1
  33. disp('Loaded marmo_RFprobe_V1')
  35. load marmo_RFprobe_MT
  36. disp('Loaded marmo_RFprobe_MT')
  39. %% extract maps
  41. % ECoG (smoothed)
  42. V1_x_ecog = marmo_RFmapData_V1.retmap_x.smoothHighReswholeRFs;
  43. V1_y_ecog = marmo_RFmapData_V1.retmap_y.smoothHighReswholeRFs;
  45. MT_x_ecog = marmo_RFmapData_MT.retmap_x.smoothHighReswholeRFs;
  46. MT_y_ecog = marmo_RFmapData_MT.retmap_y.smoothHighReswholeRFs;
  48. % Laminar (unsmoothed)
  49. V1_x_probe = marmo_RFprobe_V1.retmap_x.wholeRFs;
  50. V1_y_probe = marmo_RFprobe_V1.retmap_y.wholeRFs;
  52. MT_x_probe = marmo_RFprobe_MT.retmap_x.wholeRFs;
  53. MT_y_probe = marmo_RFprobe_MT.retmap_y.wholeRFs;
  56. % compute color limits
  58. V1_x_lim = [min([V1_x_ecog(:); V1_x_probe(:)]) max([V1_x_ecog(:); V1_x_probe(:)])];
  59. V1_y_lim = [min([V1_y_ecog(:); V1_y_probe(:)]) max([V1_y_ecog(:); V1_y_probe(:)])];
  61. MT_x_lim = [min([MT_x_ecog(:); MT_x_probe(:)]) max([MT_x_ecog(:); MT_x_probe(:)])];
  62. MT_y_lim = [min([MT_y_ecog(:); MT_y_probe(:)]) max([MT_y_ecog(:); MT_y_probe(:)])];
  65. %% plot
  67. figure('Position',[100 100 900 800])
  69. newColormap = turbo(30);
  71. % --- V1 ECoG X
  72. subplot(4,2,1)
  73. imagesc(V1_x_ecog)
  74. title('V1 ECoG X')
  75. clim(V1_x_lim)
  76. colormap(newColormap)
  77. colorbar
  78. axis equal
  79. axis off
  81. % --- V1 ECoG Y
  82. subplot(4,2,2)
  83. imagesc(V1_y_ecog)
  84. title('V1 ECoG Y')
  85. clim(V1_y_lim)
  86. colormap(newColormap)
  87. colorbar
  88. axis equal
  89. axis off
  91. % --- V1 probe X
  92. subplot(4,2,3)
  93. imagesc(V1_x_probe)
  94. title('V1 probe X')
  95. clim(V1_x_lim)
  96. colormap(newColormap)
  97. colorbar
  98. axis equal
  99. axis off
  101. % --- V1 probe Y
  102. subplot(4,2,4)
  103. imagesc(V1_y_probe)
  104. title('V1 probe Y')
  105. clim(V1_y_lim)
  106. colormap(newColormap)
  107. colorbar
  108. axis equal
  109. axis off
  112. % --- MT ECoG X
  113. subplot(4,2,5)
  114. imagesc(MT_x_ecog)
  115. title('MT ECoG X')
  116. clim(MT_x_lim)
  117. colormap(newColormap)
  118. colorbar
  119. axis equal
  120. axis off
  122. % --- MT ECoG Y
  123. subplot(4,2,6)
  124. imagesc(MT_y_ecog)
  125. title('MT ECoG Y')
  126. clim(MT_y_lim)
  127. colormap(newColormap)
  128. colorbar
  129. axis equal
  130. axis off
  132. % --- MT probe X
  133. subplot(4,2,7)
  134. imagesc(MT_x_probe)
  135. title('MT probe X')
  136. clim(MT_x_lim)
  137. colormap(newColormap)
  138. colorbar
  139. axis equal
  140. axis off
  142. % --- MT probe Y
  143. subplot(4,2,8)
  144. imagesc(MT_y_probe)
  145. title('MT probe Y')
  146. clim(MT_y_lim)
  147. colormap(newColormap)
  148. colorbar
  149. axis equal
  150. axis off
  153. sgtitle('Marmoset retinotopic maps (pixel RF space)')
  154. set(gcf,'color','w')
  157. %%
  158. % Purpose: plot MT laminar RFs with correct probe geometry
  159. % Purpose: plot MT laminar RFs with correct probe geometry
  161. RFstruct = marmo_RFprobe_MT.RFstruct;
  163. snrThresh = 0;
  164. exampleCh = 14;
  166. chXp = marmo_RFprobe_MT.chXp;
  167. chYp = marmo_RFprobe_MT.chYp;
  169. nCh = length(chXp);
  171. % logical channel masks
  173. isValidCh = ~isnan(chXp);
  175. % compute SNR
  177. vSNR = nan(1,nCh);
  179. for iCh = find(isValidCh)
  181. thisRF = RFstruct.DataAllmAll{1,iCh};
  182. thisBound = RFstruct.LabelMatrixAll{1,iCh};
  184. vIn = thisRF(thisBound==1);
  185. vOut = thisRF(thisBound==0);
  187. vSNR(iCh) = mean(vIn(:)) / mean(vOut(:));
  189. end
  191. isHighSNRCh = vSNR > snrThresh;
  194. % compute RF centers
  196. cx = nan(1,nCh);
  197. cy = nan(1,nCh);
  199. for iCh = find(isValidCh)
  201. RF = flipud(RFstruct.DataAllmAll{1,iCh});
  202. mask = flipud(RFstruct.LabelMatrixAll{1,iCh});
  204. RFmask = RF;
  205. RFmask(mask==0) = 0;
  207. [yy,xx] = ndgrid(1:size(RFmask,1),1:size(RFmask,2));
  209. cx(iCh) = sum(xx(:).*RFmask(:)) / sum(RFmask(:));
  210. cy(iCh) = sum(yy(:).*RFmask(:)) / sum(RFmask(:));
  212. end
  215. % median RF center from GOOD channels
  217. isGoodValidCh = isValidCh & isHighSNRCh;
  219. cx_med = median(cx(isGoodValidCh));
  220. cy_med = median(cy(isGoodValidCh));
  223. % best SNR RF
  225. validIdx = find(isValidCh);
  226. [~,bestRelIdx] = max(vSNR(validIdx));
  227. bestCh = validIdx(bestRelIdx);
  229. cx_best = cx(bestCh);
  230. cy_best = cy(bestCh);
  233. % store QC results in struct (no saving)
  235. marmo_RFprobe_MT.isValidCh = isValidCh;
  236. marmo_RFprobe_MT.isHighSNRCh = isHighSNRCh;
  238. marmo_RFprobe_MT.SNR = vSNR;
  240. marmo_RFprobe_MT.cx = cx;
  241. marmo_RFprobe_MT.cy = cy;
  243. marmo_RFprobe_MT.cx_med = cx_med;
  244. marmo_RFprobe_MT.cy_med = cy_med;
  246. marmo_RFprobe_MT.bestCh = bestCh;
  247. marmo_RFprobe_MT.cx_best = cx_best;
  248. marmo_RFprobe_MT.cy_best = cy_best;
  250. marmo_RFprobe_MT.exampleCh = exampleCh;
  253. % plot RFs using probe depth order
  255. validCh = find(isValidCh);
  256. depth = marmo_RFprobe_MT.chYp(validCh);
  258. [~,order] = sort(depth); % superficial → deep
  259. plotCh = validCh(order);
  261. nPlot = length(plotCh);
  263. nCols = ceil(sqrt(nPlot));
  264. nRows = ceil(nPlot/nCols);
  267. figure('Position',[100 100 900 700])
  269. for i = 1:nPlot
  271. iCh = plotCh(i);
  273. subplot(nRows,nCols,i)
  275. RF = flipud(RFstruct.DataAllmAll{1,iCh});
  277. imagesc(RF)
  278. axis equal
  279. axis off
  280. colormap jet
  281. hold on
  283. % individual RF center
  284. plot(cx(iCh),cy(iCh),'k+','LineWidth',1.5,'MarkerSize',8)
  286. % median RF center
  287. plot(cx_med,cy_med,'b+','LineWidth',2,'MarkerSize',10)
  289. % best SNR RF center
  290. plot(cx_best,cy_best,'m+','LineWidth',2,'MarkerSize',10)
  292. % SNR text
  293. text(4,12,sprintf('%.2f',vSNR(iCh)),'Color','k','FontSize',9,'FontWeight','bold')
  295. % mark bad RF
  296. if ~isHighSNRCh(iCh)
  297. text(4,4,'X','Color','r','FontSize',14,'FontWeight','bold')
  298. end
  300. % mark example RF
  301. if iCh == exampleCh
  302. rectangle('Position',[1 1 size(RF,2)-1 size(RF,1)-1],...
  303. 'EdgeColor','r','LineWidth',2)
  304. text(4,size(RF,1)-4,'example','Color','r','FontWeight','bold')
  305. end
  307. set(gca,'YDir','normal')
  309. end
  311. sgtitle('MT laminar probe RFs (probe geometry)')
  312. set(gcf,'color','w')
  313. %% Purpose: plot V1 laminar RFs with correct probe geometry
  314. % Purpose: plot V1 laminar RFs with correct probe geometry
  316. RFstruct = marmo_RFprobe_V1.RFstruct;
  318. snrThresh = 0.0;
  319. exampleCh = 11;
  321. chXp = marmo_RFprobe_V1.chXp;
  322. chYp = marmo_RFprobe_V1.chYp;
  324. nCh = length(chXp);
  326. % logical channel masks
  328. isValidCh = ~isnan(chXp);
  330. % compute SNR
  332. vSNR = nan(1,nCh);
  334. for iCh = find(isValidCh)
  336. thisRF = RFstruct.DataAllmAll{1,iCh};
  337. thisBound = RFstruct.LabelMatrixAll{1,iCh};
  339. vIn = thisRF(thisBound==1);
  340. vOut = thisRF(thisBound==0);
  342. vSNR(iCh) = mean(vIn(:)) / mean(vOut(:));
  344. end
  346. isHighSNRCh = vSNR > snrThresh;
  349. % compute RF centers
  351. cx = nan(1,nCh);
  352. cy = nan(1,nCh);
  354. for iCh = find(isValidCh)
  356. RF = flipud(RFstruct.DataAllmAll{1,iCh});
  357. mask = flipud(RFstruct.LabelMatrixAll{1,iCh});
  359. RFmask = RF;
  360. RFmask(mask==0) = 0;
  362. [yy,xx] = ndgrid(1:size(RFmask,1),1:size(RFmask,2));
  364. cx(iCh) = sum(xx(:).*RFmask(:)) / sum(RFmask(:));
  365. cy(iCh) = sum(yy(:).*RFmask(:)) / sum(RFmask(:));
  367. end
  370. % median RF center from GOOD channels
  372. isGoodValidCh = isValidCh & isHighSNRCh;
  374. cx_med = median(cx(isGoodValidCh));
  375. cy_med = median(cy(isGoodValidCh));
  378. % best SNR RF
  380. validIdx = find(isValidCh);
  381. [~,bestRelIdx] = max(vSNR(validIdx));
  382. bestCh = validIdx(bestRelIdx);
  384. cx_best = cx(bestCh);
  385. cy_best = cy(bestCh);
  388. % store QC results into struct
  390. marmo_RFprobe_V1.isValidCh = isValidCh;
  391. marmo_RFprobe_V1.isHighSNRCh = isHighSNRCh;
  393. marmo_RFprobe_V1.SNR = vSNR;
  395. marmo_RFprobe_V1.cx = cx;
  396. marmo_RFprobe_V1.cy = cy;
  398. marmo_RFprobe_V1.cx_med = cx_med;
  399. marmo_RFprobe_V1.cy_med = cy_med;
  401. marmo_RFprobe_V1.bestCh = bestCh;
  402. marmo_RFprobe_V1.cx_best = cx_best;
  403. marmo_RFprobe_V1.cy_best = cy_best;
  405. marmo_RFprobe_V1.exampleCh = exampleCh;
  408. % plot RFs using depth order
  410. validCh = find(isValidCh);
  411. depth = marmo_RFprobe_V1.chYp(validCh);
  413. [~,order] = sort(depth); % superficial → deep
  414. plotCh = validCh(order);
  416. nPlot = length(plotCh);
  418. nCols = ceil(sqrt(nPlot));
  419. nRows = ceil(nPlot/nCols);
  421. figure('Position',[100 100 900 700])
  423. for i = 1:nPlot
  425. iCh = plotCh(i);
  427. subplot(nRows,nCols,i)
  429. RF = flipud(RFstruct.DataAllmAll{1,iCh});
  431. imagesc(RF)
  432. axis equal
  433. axis off
  434. colormap jet
  435. hold on
  437. % individual RF center
  438. plot(cx(iCh),cy(iCh),'k+','LineWidth',1.5,'MarkerSize',8)
  440. % median RF center
  441. plot(cx_med,cy_med,'b+','LineWidth',2,'MarkerSize',10)
  443. % best SNR RF center
  444. plot(cx_best,cy_best,'m+','LineWidth',2,'MarkerSize',10)
  446. % SNR text
  447. text(4,12,sprintf('%.2f',vSNR(iCh)),'Color','k','FontSize',9,'FontWeight','bold')
  449. % mark bad RF
  450. if ~isHighSNRCh(iCh)
  451. text(4,4,'X','Color','r','FontSize',14,'FontWeight','bold')
  452. end
  454. % mark example RF
  455. if iCh == exampleCh
  456. rectangle('Position',[1 1 size(RF,2)-1 size(RF,1)-1],...
  457. 'EdgeColor','r','LineWidth',2)
  458. text(4,size(RF,1)-4,'example','Color','r','FontWeight','bold')
  459. end
  461. set(gca,'YDir','normal')
  463. end
  465. sgtitle('V1 laminar probe RFs (probe geometry)')
  466. set(gcf,'color','w')
  471. %%
  473. % Purpose: plot MT ECoG retinotopic maps using NON-smoothed pixel RF maps
  475. MT_x_ecog_raw = marmo_RFmapData_MT.retmap_x.wholeRFs;
  476. MT_y_ecog_raw = marmo_RFmapData_MT.retmap_y.wholeRFs;
  478. MT_x_lim = [min(MT_x_ecog_raw(:)) max(MT_x_ecog_raw(:))];
  479. MT_y_lim = [min(MT_y_ecog_raw(:)) max(MT_y_ecog_raw(:))];
  481. figure('Position',[100 100 700 500])
  483. newColormap = turbo(30);
  485. subplot(1,2,1)
  486. imagesc(MT_x_ecog_raw)
  487. title('MT ECoG X (raw)')
  488. clim(MT_x_lim)
  489. colormap(newColormap)
  490. colorbar
  491. axis equal
  492. axis off
  494. subplot(1,2,2)
  495. imagesc(MT_y_ecog_raw)
  496. title('MT ECoG Y (raw)')
  497. clim(MT_y_lim)
  498. colormap(newColormap)
  499. colorbar
  500. axis equal
  501. axis off
  503. sgtitle('MT ECoG retinotopic maps (non-smoothed)')
  504. set(gcf,'color','w')
  507. %%
  510. median(marmo_RFprobe_MT.retmap_x.wholeRFs(marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh))
  512. median(marmo_RFprobe_MT.retmap_y.wholeRFs(marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh))
  515. %%
  517. % Purpose: plot smoothed MT ECoG maps and find pixel closest to probe RF median
  519. MT_x_ecog_smooth = marmo_RFmapData_MT.retmap_x.smoothHighReswholeRFs;
  520. MT_y_ecog_smooth = marmo_RFmapData_MT.retmap_y.smoothHighReswholeRFs;
  522. % probe RF medians
  523. medX = median(marmo_RFprobe_MT.retmap_x.wholeRFs( ...
  524. marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh));
  526. medY = median(marmo_RFprobe_MT.retmap_y.wholeRFs( ...
  527. marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh));
  529. % find closest pixels
  530. [~,ix] = min(abs(MT_x_ecog_smooth(:) - medX));
  531. [~,iy] = min(abs(MT_y_ecog_smooth(:) - medY));
  533. [rowX,colX] = ind2sub(size(MT_x_ecog_smooth),ix);
  534. [rowY,colY] = ind2sub(size(MT_y_ecog_smooth),iy);
  537. figure('Position',[100 100 700 500])
  539. newColormap = turbo(30);
  541. subplot(1,2,1)
  542. imagesc(MT_x_ecog_smooth)
  543. hold on
  544. plot(colX,rowX,'k+','LineWidth',2,'MarkerSize',12)
  545. title('MT ECoG X (smoothed)')
  546. colormap(newColormap)
  547. colorbar
  548. axis equal
  549. axis off
  551. subplot(1,2,2)
  552. imagesc(MT_y_ecog_smooth)
  553. hold on
  554. plot(colY,rowY,'k+','LineWidth',2,'MarkerSize',12)
  555. title('MT ECoG Y (smoothed)')
  556. colormap(newColormap)
  557. colorbar
  558. axis equal
  559. axis off
  561. sgtitle('Closest ECoG pixels to laminar RF median')
  562. set(gcf,'color','w')
  565. %% Purpose: plot RAW MT ECoG maps and find pixel closest to probe RF median
  567. MT_x_ecog_raw = marmo_RFmapData_MT.retmap_x.wholeRFs;
  568. MT_y_ecog_raw = marmo_RFmapData_MT.retmap_y.wholeRFs;
  570. % probe RF medians
  571. medX = median(marmo_RFprobe_MT.retmap_x.wholeRFs( ...
  572. marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh));
  574. medY = median(marmo_RFprobe_MT.retmap_y.wholeRFs( ...
  575. marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh));
  577. % find closest pixels
  578. [~,ix] = min(abs(MT_x_ecog_raw(:) - medX));
  579. [~,iy] = min(abs(MT_y_ecog_raw(:) - medY));
  581. [rowX,colX] = ind2sub(size(MT_x_ecog_raw),ix);
  582. [rowY,colY] = ind2sub(size(MT_y_ecog_raw),iy);
  584. figure('Position',[100 100 700 500])
  586. newColormap = turbo(30);
  588. subplot(1,2,1)
  589. imagesc(MT_x_ecog_raw)
  590. hold on
  591. plot(colX,rowX,'k+','LineWidth',2,'MarkerSize',12)
  592. title('MT ECoG X (raw)')
  593. colormap(newColormap)
  594. colorbar
  595. axis equal
  596. axis off
  598. subplot(1,2,2)
  599. imagesc(MT_y_ecog_raw)
  600. hold on
  601. plot(colY,rowY,'k+','LineWidth',2,'MarkerSize',12)
  602. title('MT ECoG Y (raw)')
  603. colormap(newColormap)
  604. colorbar
  605. axis equal
  606. axis off
  608. sgtitle('Closest RAW ECoG pixels to laminar RF median')
  609. set(gcf,'color','w')
  611. %%
  614. % distance between V1 and MT laminar median RF locations (good + valid channels)
  616. d = hypot( ...
  617. median(marmo_RFprobe_V1.retmap_x.wholeRFs(marmo_RFprobe_V1.isHighSNRCh & marmo_RFprobe_V1.isValidCh)) - ...
  618. median(marmo_RFprobe_MT.retmap_x.wholeRFs(marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh)), ...
  619. median(marmo_RFprobe_V1.retmap_y.wholeRFs(marmo_RFprobe_V1.isHighSNRCh & marmo_RFprobe_V1.isValidCh)) - ...
  620. median(marmo_RFprobe_MT.retmap_y.wholeRFs(marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh)) );
  623. %%
  625. % Purpose: simulate random MT locations and compare to actual V1–MT laminar RF distance
  627. rng(0)
  629. % V1 laminar median RF
  630. xV1 = median(marmo_RFprobe_V1.retmap_x.wholeRFs( ...
  631. marmo_RFprobe_V1.isHighSNRCh & marmo_RFprobe_V1.isValidCh));
  633. yV1 = median(marmo_RFprobe_V1.retmap_y.wholeRFs( ...
  634. marmo_RFprobe_V1.isHighSNRCh & marmo_RFprobe_V1.isValidCh));
  636. % MT laminar median RF
  637. xMT = median(marmo_RFprobe_MT.retmap_x.wholeRFs( ...
  638. marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh));
  640. yMT = median(marmo_RFprobe_MT.retmap_y.wholeRFs( ...
  641. marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh));
  643. % actual distance
  644. d_actual = hypot(xV1-xMT , yV1-yMT);
  647. % MT ECoG map
  648. X = marmo_RFmapData_MT.retmap_x.smoothHighReswholeRFs;
  649. Y = marmo_RFmapData_MT.retmap_y.smoothHighReswholeRFs;
  651. valid = ~isnan(X) & ~isnan(Y);
  653. xvals = X(valid);
  654. yvals = Y(valid);
  656. nIter = 10000;
  657. d_rand = zeros(nIter,1);
  659. for i = 1:nIter
  661. idx = randi(length(xvals));
  663. xr = xvals(idx);
  664. yr = yvals(idx);
  666. d_rand(i) = hypot(xV1-xr , yV1-yr);
  668. end
  671. %% Purpose: plot distribution and report RF distances (deg)
  674. pxPerDeg = marmo_RFprobe_MT.pxPerDeg;
  676. d_actual_deg = d_actual/pxPerDeg;
  677. d_rand_deg = d_rand/pxPerDeg;
  679. figure('Color','w','Position',[100 100 600 450])
  680. clf
  682. h = histogram(d_rand_deg,40,'Normalization','probability');
  683. h.FaceColor = [0.6 0.6 0.6];
  684. h.EdgeColor = 'k';
  686. hold on
  688. hLine = xline(d_actual_deg,'r','LineWidth',3);
  690. xlabel('RF distance (°)','FontSize',12)
  691. ylabel('Probability','FontSize',12)
  693. set(gca,'TickDir','out','FontSize',12,'Box','off')
  695. legend([h hLine],{'Random simulated MT penetrations','Actual penetration'},...
  696. 'Box','off','Location','northeast')
  698. set(gcf,'color','w')
  701. % console output
  702. fprintf('Actual V1–MT RF distance: %.2f deg\n', d_actual_deg)
  703. fprintf('Random MT penetration distance: %.2f ± %.2f deg (mean ± SD)\n', ...
  704. mean(d_rand_deg), std(d_rand_deg))
  707. percentile = mean(d_rand > d_actual) * 100;
  708. fprintf('Actual penetration is better aligned than %.1f%% of random penetrations\n', percentile)
  712. xlim([0 16.5])
  714. % major ticks (labeled)
  715. xticks(0:2:16)
  717. % minor ticks (unlabeled)
  718. ax = gca;
  719. ax.XAxis.MinorTickValues = 1:2:15;
  720. ax.XMinorTick = 'on';
  722. % lock it for PDF export
  723. ax.XTickMode = 'manual';
  724. ax.XMinorTick = 'on';
  727. %%
  729. % Purpose: bootstrap CI for V1–MT RF distance
  731. xV1 = marmo_RFprobe_V1.retmap_x.wholeRFs(marmo_RFprobe_V1.isHighSNRCh & marmo_RFprobe_V1.isValidCh);
  732. yV1 = marmo_RFprobe_V1.retmap_y.wholeRFs(marmo_RFprobe_V1.isHighSNRCh & marmo_RFprobe_V1.isValidCh);
  734. xMT = marmo_RFprobe_MT.retmap_x.wholeRFs(marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh);
  735. yMT = marmo_RFprobe_MT.retmap_y.wholeRFs(marmo_RFprobe_MT.isHighSNRCh & marmo_RFprobe_MT.isValidCh);
  737. rng(0)
  738. nBoot = 10000;
  739. d_boot = zeros(nBoot,1);
  741. for i = 1:nBoot
  743. idxV1 = randi(length(xV1),length(xV1),1);
  744. idxMT = randi(length(xMT),length(xMT),1);
  746. xm1 = median(xV1(idxV1));
  747. ym1 = median(yV1(idxV1));
  749. xm2 = median(xMT(idxMT));
  750. ym2 = median(yMT(idxMT));
  752. d_boot(i) = hypot(xm1-xm2 , ym1-ym2);
  754. end
  756. pxPerDeg = marmo_RFprobe_MT.pxPerDeg;
  758. d_boot_deg = d_boot/pxPerDeg;
  760. d_obs = hypot(median(xV1)-median(xMT) , median(yV1)-median(yMT)) / pxPerDeg;
  761. d_ci = prctile(d_boot_deg,[2.5 97.5]);
  763. fprintf('V1–MT RF distance: %.2f deg (95%% CI %.2f–%.2f)\n', d_obs, d_ci(1), d_ci(2))

Figure_Simulation_MarmoMT_targeting.m, under CC-BY-4.0 · at the source

Overview

Authors: Patrick Jendritza1,2,3,4,5, Rickard Liljemalm6, Thomas Stieglitz7, Pascal Fries1,2,7, Christopher Murphy Lewis1,8
ORCID iDs: Christopher Murphy Lewis
  1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany
  2. International Max Planck Research School for Neural Circuits, Max-von-Laue-Straße 4, 60438 Frankfurt, Germany
  3. Else Kröner Fresenius Center for Optogenetic Therapies, University Medical Center Göttingen, Göttingen, Germany
  4. Institute for Auditory Neuroscience, University Medical Center Göttingen, Göttingen, Germany
  5. Visual Circuits & Interfaces group, German Primate Center, Göttingen, Germany
  6. Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering (IMTEK), BrainLinks-BrainTools Center and the Bernstein Center Freiburg, Albert-Ludwigs-Universität Freiburg, 79110 Freiburg, Germany
  7. Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8, 72076 Tübingen, Germany
  8. Brain Research Institute, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Institutions: Salk Institute for Biological Studies (United States); Max Planck Society (Germany); German Primate Center (Germany); Ernst Strüngmann Institute for Neuroscience (Germany); Universitätsmedizin Göttingen (Germany); Else Kröner Fresenius Center for Optogenetic Therapies (Germany); University of Göttingen (Germany); University of Freiburg (Germany); Max Planck Institute for Biological Cybernetics (Germany); University of Zurich (Switzerland)
Journal: Cell reports methods, volume 6, issue 8, article 101481
Dates: received 5 November 2025; accepted 11 May 2026; published online 11 June 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.crmeth.2026.101481 · PMID 42276070 · PMCID PMC13494546 · OpenAlex W4405762810
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.12.18.629147
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), intracranial EEG (iEEG / ECoG / SEEG) (modality), rat (organism), other (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: neuroscience, electrocorticography, topography, sensory coding, ECoG, LFP, brain, multimodal, optogenetics, primate
MeSH: Brain*, Brain Mapping*, Electrocorticography*, Animals, Callithrix, Cats, Neurons, Optogenetics, Rats (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (SPP 1665 FR2557/1-1, FOR 1847 FR2557/2-1, FR2557/5-1-CORNET, FR2557/6-1-NeuroTMR, FR2557/7-1-DualStreams); EU (HEALTH-F2-2008-200728-BrainSynch, FP7-604102-HBP, FP7-600730-Magnetrodes); NIH (1U54MH091657-WU-Minn-Consortium-HCP); LOEWE; NeFF; University of Zurich (K-41220-04)
Citations: not cited yet (Europe PMC); 110 references in the paper

Abstract

Brain function relies on coordinated activity across spatial and temporal scales. Single neurons integrate local and long-range connectivity and reflect activity across brain-wide networks. Understanding integrated brain function requires tools capable of recording from anatomically connected populations in distributed brain areas to bridge local and global dynamics. Here, we present high-density, micro-electrocorticography arrays that facilitate multi-scale studies of brain activity. The hybrid arrays integrate the desirable features of silicone elastomers and polyimide films: silicone provides optical transparency and permits repeated penetration with intracortical arrays, while polyimide enables fine photolithographic feature definition. This combination facilitates high-throughput functional mapping to identify targets and insertion of intracortical arrays for dense local sampling. We demonstrate functional mapping in rats, cats, and marmosets, showing how functional maps guide multi-area laminar recordings. Finally, we demonstrate local and feedforward optogenetic stimulation to investigate cortico-cortical interactions. These capabilities establish the hybrid μECoG as a compelling tool for systems neuroscience.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

dataverse.harvard.edu/dataverse/hybridecog

License: none: the authors keep all their rights
State: unreachable at the last attempt, verified on 29 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 5 checks, the latest on 29 September 2026: unreachable at the last attempt (HTTP 202)
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At the source: dataverse.harvard.edu/dataverse/hybridECoG/

Zenodo 20089744

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (10 files), Signal Processing Toolbox (7 files), Image Processing Toolbox (4 files), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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24 files
At the source: doi.org/10.5281/zenodo.20089744

The paper's code and data availability statement is in the Data section.

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Data

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Data and code availability

• The datasets supporting the current study are available from the corresponding authors on request. • The code supporting the current study is available on Harvard Dataverse (https://dataverse.harvard.edu/dataverse/hybridECoG/) and has been archived at https://doi.org/10.5281/zenodo.20089744. • Any additional information required to re-analyze the data reported in this paper is available from the lead contact upon request.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 10 keywords, 9 MeSH terms, 6 funders, 108 references.

Cite

This paper

Jendritza, P., Liljemalm, R., Stieglitz, T., Fries, P., & Lewis, C. M. (2026). A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation. Cell reports methods, 6(8), 101481. https://doi.org/10.1016/j.crmeth.2026.101481

BibTeX

@article{jendritza2026hybrid,
author = {Jendritza, Patrick and Liljemalm, Rickard and Stieglitz, Thomas and Fries, Pascal and Lewis, Christopher Murphy},
title = {{A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation}},
journal = {Cell reports methods},
year = {2026},
month = jun,
volume = {6},
number = {8},
pages = {101481},
publisher = {Elsevier},
issn = {2667-2375},
doi = {10.1016/j.crmeth.2026.101481},
url = {https://doi.org/10.1016/j.crmeth.2026.101481},
pmid = {42276070},
pmcid = {PMC13494546}
}

RIS

TY - JOUR
AU - Jendritza, Patrick
AU - Liljemalm, Rickard
AU - Stieglitz, Thomas
AU - Fries, Pascal
AU - Lewis, Christopher Murphy
TI - A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation
T2 - Cell reports methods
J2 - Cell Rep Methods
PY - 2026
DA - 2026/06/11
VL - 6
IS - 8
SP - 101481
SN - 2667-2375
PB - Elsevier
DO - 10.1016/j.crmeth.2026.101481
UR - https://doi.org/10.1016/j.crmeth.2026.101481
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.crmeth.2026.101481",
"type": "article-journal",
"title": "A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation",
"container-title": "Cell reports methods",
"author": [
{
"family": "Jendritza",
"given": "Patrick"
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{
"family": "Liljemalm",
"given": "Rickard"
},
{
"family": "Stieglitz",
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{
"family": "Fries",
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],
"container-title-short": "Cell Rep Methods",
"volume": "6",
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"page": "101481",
"DOI": "10.1016/j.crmeth.2026.101481",
"PMID": "42276070",
"PMCID": "PMC13494546",
"ISSN": "2667-2375",
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"URL": "https://doi.org/10.1016/j.crmeth.2026.101481",
"language": "en",
"issued": {
"date-parts": [
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2026,
6,
11
]
]
}
}

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