OSCR

Microstructural spine alterations increase neuronal excitability in focal cortical dysplasia Type I.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Statistics ↔ figures.py, lines 476–547 · score 0.68 · exponential fits, somatic EPSP amplitude, apical dendritic, distance, Curves, basal
  2. [2] § Methods › Statistics ↔ controller.py, lines 160–224 · score 0.66 · somatic EPSP amplitude, single spine, spine density, fits, distance, Violin
  3. [3] § Methods › Common model backbone ↔ sseEPSP.py, lines 9–82 · score 0.50 · basal dendrites, apical dendrites, distances, somatic, active, epileptogenic

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 552 lines · 22 KB · CC-BY-NC-4.0 · 1 match

  1. from matplotlib import pyplot as plt
  2. import numpy as np
  3. from scipy.signal import savgol_filter
  5. # line plot for firing probability when activate multiple spines
  6. def line_firingprobability(data, save=False, xpeak=[40, 175]) :
  7. x_ctl_nspines = data['ctl_actspines']
  8. y_ctl_prob = data['ctl_prob']
  9. y_ctl_stdev = data['ctl_stdev']
  10. x_fcd_nspines = data['fcd_actspines']
  11. y_fcd_prob = data['fcd_prob']
  12. y_fcd_stdev = data['fcd_stdev']
  14. fig, (ax_fcd, ax_ctl) = plt.subplots(1, 2, sharey=True)
  15. fig.subplots_adjust(wspace=0.05) # adjust space between Axes
  17. filter_win = 5
  18. y_ctl_prob = savgol_filter(y_ctl_prob, window_length=filter_win, polyorder=2)
  19. y_fcd_prob = savgol_filter(y_fcd_prob, window_length=filter_win, polyorder=2)
  20. y_ctl_stdev = savgol_filter(y_ctl_stdev, window_length=filter_win, polyorder=2)
  21. y_fcd_stdev = savgol_filter(y_fcd_stdev, window_length=filter_win, polyorder=2)
  23. ax_ctl.fill_between(x_ctl_nspines, np.clip(y_ctl_prob-y_ctl_stdev, 0, None), np.clip(y_ctl_prob+y_ctl_stdev, None, 1), color='cyan', alpha=.55, linewidth=1)
  24. ax_ctl.plot(x_ctl_nspines, y_ctl_prob, linewidth=2, color='darkcyan', label="Baseline")
  25. ax_fcd.plot(x_ctl_nspines, y_ctl_prob, linewidth=2, color='darkcyan', label="Baseline")
  27. ax_fcd.fill_between(x_fcd_nspines, np.clip(y_fcd_prob-y_fcd_stdev, 0, None), np.clip(y_fcd_prob+y_fcd_stdev, None, 1), color='magenta', alpha=.55, linewidth=1)
  28. ax_fcd.plot(x_fcd_nspines, y_fcd_prob, linewidth=2, color='darkmagenta', label="Altered")
  29. ax_ctl.plot(x_fcd_nspines, y_fcd_prob, linewidth=2, color='darkmagenta', label="Altered") #just for the legend
  32. # xpeak = [40,175]
  33. ax_fcd.set_xlim(xpeak[0]-10, xpeak[0]+10)
  34. ax_ctl.set_xlim(xpeak[1]-10, xpeak[1]+10)
  35. ax_fcd.set_ylim(0, 1)
  36. ax_ctl.set_ylim(0, 1)
  38. ax_fcd.spines.right.set_visible(False)
  39. ax_ctl.spines.left.set_visible(False)
  40. ax_fcd.yaxis.tick_left()
  41. ax_ctl.yaxis.tick_right()
  43. ax_fcd.set_xticks([xpeak[0]-5, xpeak[0]+5], labels=[str(xpeak[0]-5), str(xpeak[0]+5)], fontsize=12)
  44. ax_ctl.set_xticks([xpeak[1]-5, xpeak[1]+5], labels=[str(xpeak[1]-5), str(xpeak[1]+5)], fontsize=12)
  45. ax_fcd.set_yticks([0, 1.0], labels=["0.0", "1.0"], fontsize=12)
  47. d = .2 # proportion of vertical to horizontal extent of the slanted line
  48. kwargs = dict(marker=[(-d, -1), (d, 1)], markersize=10,
  49. linestyle="none", color='k', mec='k', mew=1, clip_on=False)
  50. ax_fcd.plot([1, 1], [0, 1], transform=ax_fcd.transAxes, **kwargs)
  51. ax_ctl.plot([0, 0], [0, 1], transform=ax_ctl.transAxes, **kwargs)
  53. ax_fcd.set_ylabel("Firing Probability", fontsize=14)
  54. ax_fcd.set_xlabel('Number of Activated Spines', fontsize=14)
  55. ax_fcd.xaxis.set_label_coords(0.5, 0.05, transform=fig.transFigure)
  57. if save :
  58. plt.savefig(f"syn_activation.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  60. plt.show()
  62. # line plot for io rate curve
  63. def line_io_curve(stats_df, save=False) :
  64. plt.figure()
  66. # Control
  67. plt.plot(stats_df['input_rate'], stats_df['avg_ctl'], label='Baseline', color='darkcyan')
  68. plt.fill_between(stats_df['input_rate'], stats_df['min_ctl'], stats_df['max_ctl'], color='cyan', alpha=.55, linewidth=1)
  70. # FCD
  71. plt.plot(stats_df['input_rate'], stats_df['avg_fcd'], label='Altered', color='darkmagenta')
  72. plt.fill_between(stats_df['input_rate'], stats_df['min_fcd'], stats_df['max_fcd'], color='magenta', alpha=.55, linewidth=1)
  74. plt.ylabel("Firing Rate (Hz)", fontsize=14)
  75. plt.ylim((0, 110))
  76. plt.xlim((0.2,5))
  77. plt.yticks(ticks=[0, 50, 100], labels=["0", "50", "100"], fontsize=12)
  78. plt.xlabel("Input Frequency (Hz)", fontsize=14)
  79. plt.xticks(ticks=[0.2, 1.0, 2.0, 3.0, 4.0, 5.0], labels=["0.2", "1.0", "2.0", "3.0", "4.0", "5.0"], fontsize=12)
  80. plt.tick_params(
  81. axis='x', # changes apply to the x-axis
  82. which='both', # both major and minor ticks are affected
  83. bottom=True, # ticks along the bottom edge are off
  84. top=False)#, # ticks along the top edge are off
  85. if save :
  86. plt.savefig(f"io_curve.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  88. plt.show()
  90. # line plot about deltaV(head-base) when neck diameter changed
  91. def line_neckdiam_vs_deltaV(data, ra, save=False, show=True, legend=False) :
  92. import matplotlib.cm as cm
  93. from matplotlib.colors import Normalize
  94. from mpl_toolkits.axes_grid1.inset_locator import inset_axes
  96. # neck_range_min = data['range'][0]
  97. # neck_range_max = data['range'][1]
  98. neck_diameter = data['neck_diameter']
  100. deltaV_min = data['deltaV_min']
  101. deltaV_max = data['deltaV_max']
  103. fig, ax = plt.subplots()
  105. # plt.fill_between(neck_diameter, deltaV_min, deltaV_max, color="lightgrey", alpha=0.9)
  107. cmap = cm.Reds
  108. # norm = Normalize(vmin=min(ra), vmax=max(ra))
  109. norm = Normalize(vmin=0, vmax=max(ra))
  110. for i in range(len(neck_diameter) - 1):
  111. res_val = (ra[i] + ra[i+1]) / 2
  112. color = cmap(norm(res_val))
  113. ax.fill_between(neck_diameter[i:i+2], deltaV_min[i:i+2], deltaV_max[i:i+2], color=color, edgecolor=None)
  115. plt.xlabel("Spine Neck Diameter (µm)", fontsize=14)
  116. plt.ylabel('ΔV (Head - Base, mV)', fontsize=14)
  117. plt.ylim((0, max(deltaV_max)))
  118. plt.xlim((0.1, 0.5))
  119. plt.yticks(ticks=[0, 5], labels=["0", "5"], fontsize=12)
  120. plt.xticks(ticks=[0.1, 0.2, 0.3, 0.4, 0.5], labels=["0.1", "0.2", "0.3", "0.4", "0.5"], fontsize=12)
  122. plt.axvline(x = 0.15, ymin = 0, ymax = deltaV_max[neck_diameter.index(0.15)]/max(deltaV_max), color = 'cyan', label = 'Baseline', linestyle='-')
  123. plt.axvline(x = 0.3, ymin = 0, ymax = deltaV_max[neck_diameter.index(0.3)]/max(deltaV_max), color = 'magenta', label = 'Altered', linestyle='-')
  125. plt.scatter(0.3, deltaV_max[neck_diameter.index(0.3)], s=20, c='magenta', marker='X')
  126. plt.scatter(0.15, deltaV_max[neck_diameter.index(0.15)], s=20, c='cyan', marker='X')
  127. if legend :
  128. plt.text(0.3, deltaV_max[neck_diameter.index(0.3)], 'Altered', fontdict={'size': 9, 'color': 'magenta'})
  129. plt.text(0.15, deltaV_max[neck_diameter.index(0.15)], 'Baseline', fontdict={'size': 9, 'color': 'cyan'})
  131. # Force all spines on
  132. for spine in ax.spines.values():
  133. spine.set_visible(True)
  134. spine.set_color('black')
  136. ax_ins = inset_axes(ax, width="5%", height="40%", loc='upper right', borderpad=2)
  137. sm = cm.ScalarMappable(cmap=cmap, norm=norm)
  138. sm.set_array([])
  140. cbar = fig.colorbar(sm, cax=ax_ins)
  141. cbar.set_label('Neck Resistance (MΩ)')
  142. # cbar.ax.yaxis.set_label_position('left')
  143. cbar.ax.yaxis.set_ticks_position('left')
  145. cbar.ax.tick_params(labelsize=8)
  146. cbar.set_ticks([0, 330])
  148. if save :
  149. plt.savefig(f"neckdiam_deltaV.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  151. if show :
  152. plt.show()
  154. # line plot about multipoints EPSP amplitude (head, base, soma) when head diameter(fixed neck length) changed
  155. def line_headdiam_vs_amplitude_multipoints(data, save=False) :
  156. # head_diam_range_min = data['range'][0]
  157. # head_diam_range_max = data['range'][1]
  158. #x axis
  159. head_diameters = data['head_diameter']
  161. #averaged values
  162. head_ = data['head']
  163. base_ = data['base']
  164. soma_ = data['soma']
  166. #min and max for shading
  167. head_min = data['head_min']
  168. base_min = data['base_min']
  169. soma_min = data['soma_min']
  171. head_max = data['head_max']
  172. base_max = data['base_max']
  173. soma_max = data['soma_max']
  175. #plot 그리기
  176. fig, (ax_spine, ax_soma) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [0.7, 0.3]})
  177. fig.subplots_adjust(hspace=0.05) # adjust space between Axes
  179. ax_spine.plot(head_diameters, head_, color="red",label="Head")
  180. ax_spine.plot(head_diameters, base_, color="blue", label="Base")
  181. ax_spine.plot(head_diameters, soma_, color="black", label="Soma")
  183. ax_soma.plot(head_diameters, head_, color="red",label="Head")
  184. ax_soma.plot(head_diameters, base_, color="blue", label="Base")
  185. ax_soma.plot(head_diameters, soma_, color="black", label="Soma")
  187. ax_spine.fill_between(head_diameters, head_min, head_max, color="red", alpha=0.3)
  188. ax_spine.fill_between(head_diameters, base_min, base_max, color="blue", alpha=0.3)
  189. ax_spine.fill_between(head_diameters, soma_min, soma_max, color="black", alpha=0.3)
  191. ax_soma.fill_between(head_diameters, head_min, head_max, color="red", alpha=0.3)
  192. ax_soma.fill_between(head_diameters, base_min, base_max, color="blue", alpha=0.3)
  193. ax_soma.fill_between(head_diameters, soma_min, soma_max, color="black", alpha=0.3)
  196. ax_spine.spines.bottom.set_visible(False)
  198. ax_soma.xaxis.tick_bottom()
  199. ax_soma.spines.top.set_visible(False)
  201. ax_spine.set_xticks(ticks=[])
  202. ax_spine.set_yticks(ticks=[3,5,7], labels=["3", "5", "7"])
  203. ax_spine.set_ylim([0.5,9])
  206. ax_soma.set_xticks(ticks=[0.2, 0.5, 1.0, 1.5], labels=["0.2", "0.5", "1.0", "1.5"], fontsize=12)
  207. ax_soma.set_yticks(ticks=[0, 0.4], labels=["0", "0.4"], fontsize=12)
  208. ax_soma.set_ylim([0, 0.5])
  210. ax_spine.axvline(x = 0.55, ymin = 0, ymax = 1, color = 'cyan', linestyle='-')
  211. ax_spine.axvline(x = 0.70, ymin = 0, ymax = 1, color = 'magenta',linestyle='-')
  212. ax_soma.axvline(x = 0.55, ymin = 0, ymax = 1, color = 'cyan', linestyle='-')
  213. ax_soma.axvline(x = 0.70, ymin = 0, ymax = 1, color = 'magenta', linestyle='-')
  216. d = .5 # proportion of vertical to horizontal extent of the slanted line
  217. kwargs = dict(marker=[(-d, -1), (d, 1)], markersize=10,
  218. linestyle="none", color='k', mec='k', mew=1, clip_on=False)
  219. ax_spine.plot([0, 1], [0, 0], transform=ax_spine.transAxes, **kwargs)
  220. ax_soma.plot([0, 1], [1, 1], transform=ax_soma.transAxes, **kwargs)
  222. ax_soma.set_xlabel("Spine Head Diameter (µm)", fontsize=14)
  223. ax_spine.legend(loc="upper center", ncol=3, fontsize=12)
  225. fig.text(0.04, 0.5, 'EPSP Amplitude (mV)', va='center', rotation='vertical', fontsize=14)
  227. if save :
  228. plt.savefig(f"head_diam_amplitude.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  229. plt.show()
  231. # violin plot for head, base, soma EPSP amplitude between groups
  232. def violin_singlespine_EPSPamp_multipoints_between_groups(control_data, altered_data, alter_what, N=100, save=False, show=True, legend=False) :
  233. amp_head_ctl = control_data['head']
  234. amp_base_ctl = control_data['base']
  235. amp_soma_ctl = control_data['soma']
  237. amp_head_fcd = altered_data['head']
  238. amp_base_fcd = altered_data['base']
  239. amp_soma_fcd = altered_data['soma']
  241. #plot 그리기
  242. fig, ax = plt.subplots(figsize=(4,4))
  244. # #Head, Base, Soma position base
  245. box_colors = ["cyan", "magenta", "cyan", "magenta", "cyan", "magenta"]
  246. bplot1 = ax.violinplot([amp_head_ctl, amp_head_fcd],
  247. positions=[1,2], widths=0.7, showmedians=True)
  248. bplot3 = ax.violinplot([amp_base_ctl, amp_base_fcd],
  249. positions=[3,4], widths=0.7, showmedians=True)
  251. bplot1['cmaxes'].set_color("black")
  252. bplot1['cmins'].set_color("black")
  253. bplot1['cmedians'].set_color('black')
  255. bplot3['cmaxes'].set_color("black")
  256. bplot3['cmins'].set_color("black")
  257. bplot3['cmedians'].set_color('black')
  259. ax.vlines(1, min(amp_head_ctl), max(amp_head_ctl), color="black", linestyle='-', lw=1.5)
  260. ax.vlines(2, min(amp_head_fcd), max(amp_head_fcd), color="black", linestyle='-', lw=1.5)
  261. ax.vlines(3, min(amp_base_ctl), max(amp_base_ctl), color="black", linestyle='-', lw=1.5)
  262. ax.vlines(4, min(amp_base_fcd), max(amp_base_fcd), color="black", linestyle='-', lw=1.5)
  263. for pc, color in zip(bplot1['bodies'], box_colors[0:2]):
  264. pc.set_facecolor(color)
  265. pc.set_edgecolor(color)
  266. pc.set_alpha(0.5)
  267. for pc, color in zip(bplot3['bodies'], box_colors[2:4]):
  268. pc.set_facecolor(color)
  269. pc.set_edgecolor(color)
  270. pc.set_alpha(0.5)
  272. if legend :
  273. plt.legend(["Baseline","Altered"], loc="lower left")
  275. ax.set_xticks([1.5, 3.5])
  276. ax.set_xticklabels(["Head", "Base"], fontsize=12)
  277. ax.tick_params(
  278. axis='x', # changes apply to the x-axis
  279. which='both', # both major and minor ticks are affected
  280. bottom=False, # ticks along the bottom edge are off
  281. top=False)
  283. ax.set_ylabel('EPSP Amplitude (mV)', fontsize=14)
  284. ax.set_yticks(ticks=[0, 2, 4, 6, 8, 10], labels=["0", "2", "4", "6", "8", "10"], fontsize=12)
  285. ax.set_ylim([0, 10.5])
  286. #for bigger receptor weights
  287. # ax.set_yticks(ticks=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20], labels=["0", "2", "4", "6", "8", "10", "12", "14", "16", "18", "20"], fontsize=12)
  288. # ax.set_ylim([0, 20])
  290. # if alter == "density" :
  291. # ax.set_ylim([0, 10.5])
  292. ax.set_title('Spine', fontsize=14)
  294. # Force all spines on
  295. for spine in ax.spines.values():
  296. spine.set_visible(True)
  297. spine.set_color('black')
  299. if save :
  300. plt.savefig(f"violin_{alter_what}_ampl_spine.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  301. if show :
  302. plt.show()
  304. fig, ax2 = plt.subplots(figsize=(2,4))
  305. bplot2 = ax2.violinplot([amp_soma_ctl, amp_soma_fcd], positions=[5,6], widths=0.7, showmedians=True)
  306. bplot2['cmaxes'].set_color("black")
  307. bplot2['cmins'].set_color("black")
  308. bplot2['cmedians'].set_color('black')
  309. ax2.vlines(5, min(amp_soma_ctl), max(amp_soma_ctl), color="black", linestyle='-', lw=1.5)
  310. ax2.vlines(6, min(amp_soma_fcd), max(amp_soma_fcd), color="black", linestyle='-', lw=1.5)
  311. for pc, color in zip(bplot2['bodies'], box_colors[4:6]):
  312. pc.set_facecolor(color)
  313. pc.set_edgecolor(color)
  314. pc.set_alpha(0.5)
  317. ax2.set_yticks(ticks=[0, 0.2, 0.4], labels=["0", "0.2", "0.4"], fontsize=12)
  318. ax2.set_xticks([])
  319. ax2.set_ylim([0, 0.45])
  320. #for bigger receptor weights
  321. # ax2.set_yticks(ticks=[0, 0.2, 0.4, 0.6, 0.8, 1.0], labels=["0", "0.2", "0.4", "0.6", "0.8", "1.0"], fontsize=12)
  322. # ax2.set_ylim([0, 1])
  323. ax2.tick_params(top=True, labeltop=True, bottom=False, labelbottom=False)
  324. ax2.set_title('Soma', fontsize=14)
  325. # Set the y-axis label position to the right
  326. ax2.yaxis.set_label_position("right")
  327. ax2.yaxis.tick_right()
  330. # Force all spines on
  331. for spine in ax2.spines.values():
  332. spine.set_visible(True)
  333. spine.set_color('black')
  335. if save :
  336. plt.savefig(f"violin_{alter_what}_ampl_soma.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  338. if show :
  339. plt.show()
  341. #line plot with scatter overlay for input resistance when differing spine factor
  342. # spine density vs input resistance
  343. def line_density_vs_RI(data, save=False, show=True, legend=False) :
  344. x = data['x_sf']
  345. y = data['y_ri']
  347. fig, ax = plt.subplots()
  348. plt.plot(x, y, color="black", linewidth=2.5)
  350. plt.ylim((70, 190))
  351. # Set the y-axis label position to the right
  352. ax.yaxis.set_label_position("right")
  354. # Move the y-axis ticks and tick labels to the right
  355. ax.yaxis.tick_right()
  357. ax.tick_params(
  358. axis='y', # changes apply to the y-axis
  359. which='both', # both major and minor ticks are affected
  360. left=False, # ticks along the bottom edge are off
  361. right=True)
  362. plt.yticks(ticks=[90, 130, 170], labels=["90", "130", "170"], fontsize=12)
  364. plt.xticks(ticks=[x[0], x[-1]], labels=["Sparse", "Dense"], fontsize=12)
  365. plt.tick_params(
  366. axis='x', # changes apply to the x-axis
  367. which='both', # both major and minor ticks are affected
  368. bottom=True, # ticks along the bottom edge are off
  369. top=False)#), # ticks along the top edge are off
  371. # Force all spines on
  372. for spine in ax.spines.values():
  373. spine.set_visible(True)
  374. spine.set_color('black')
  376. #flip X axis
  377. plt.gca().invert_xaxis()
  379. plt.scatter(1.2, y[x.index(1.20)], s=120, c='magenta', marker='X')
  380. plt.scatter(2.0, y[x.index(2.00)], s=120, c='cyan', marker='X')
  382. if legend :
  383. plt.text(2.0, y[x.index(2.00)], 'Baseline', fontdict={'size': 9, 'color': 'cyan'})
  384. plt.text(1.2, y[x.index(1.20)], 'Altered', fontdict={'size': 9, 'color': 'magenta'})
  386. plt.xlabel("Spine Density", fontsize=14)
  387. plt.ylabel("Input Resistance (MΩ)", fontsize=14)
  389. if save:
  390. plt.savefig("density_RI.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  392. if show :
  393. plt.show()
  395. #boxplot for input resistance when differing nbasal
  396. def box_nbasal_vs_ri(data, save=False):
  397. y_ctl = data["ri_ctl"]
  398. y_fcd = data["ri_fcd"]
  401. fig, ax = plt.subplots(figsize=(2,2))
  403. # draw boxplot
  404. bplot = ax.boxplot([y_ctl, y_fcd], widths=0.4,
  405. patch_artist=True,
  406. showfliers=False,
  407. boxprops=dict(facecolor='none', edgecolor='none'),
  408. whiskerprops=dict(color='none'),
  409. capprops=dict(color='none'),
  410. medianprops=dict(color='black', linewidth=2.5))
  411. # medianprops=dict(color="black", linewidth=2),
  412. # patch_artist=True, showfliers=False)
  413. # jitter for every data point to avoid overlap
  414. for i, data in enumerate([y_ctl]):
  415. x = np.random.normal(i + 1, 0.04, size=len(data))
  416. ax.plot(x, data, 'o', color='darkcyan', markersize=3, alpha=0.9)
  417. for i, data in enumerate([y_fcd]):
  418. x = np.random.normal(i + 2, 0.04, size=len(data))
  419. ax.plot(x, data, 'o', color='darkmagenta', markersize=3, alpha=0.9)
  421. ax.set_xticks([1, 2])
  423. # bplot = ax.boxplot([y_ctl, y_fcd], widths=0.5, medianprops=dict(color="black"), patch_artist=True) # will be used to label x-ticks
  425. # fill with colors
  426. for patch, color in zip(bplot['boxes'], ["cyan", "magenta"]):
  427. patch.set_facecolor(color)
  428. patch.set_alpha(0.7)
  430. plt.ylim((80, 170))
  431. ax.set_yticks(ticks=[100, 130, 160], labels=["100", "140", "160"], fontsize=12)
  432. plt.xticks([])
  433. if save :
  434. plt.savefig(f"ri_box.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  436. plt.show()
  439. def meansem_nbasal_vs_ri(data, save=False) :
  440. from scipy import stats
  442. y_ctl = data["ri_ctl"]
  443. y_fcd = data["ri_fcd"]
  445. data_groups = [y_ctl, y_fcd]
  446. group_colors = ['cyan', 'magenta']
  447. line_colors = ['darkcyan', 'darkmagenta']
  448. x_pos = [1, 2]
  450. fig, ax = plt.subplots(figsize=(2,2))
  452. for i, (data, color, lcolor) in enumerate(zip(data_groups, group_colors, line_colors)):
  453. mean = np.mean(data)
  454. sem = stats.sem(data)
  455. # print(f"Group {i+1} - Mean: {mean:.2f}, SEM: {sem:.2f}")
  457. x_jitter = np.random.normal(x_pos[i], 0.04, size=len(data))
  458. ax.scatter(x_jitter, data, color=color, s=5, alpha=0.9, zorder=1)
  460. ax.hlines(mean, x_pos[i]-0.2, x_pos[i]+0.2, colors=lcolor, lw=1.5, zorder=3)
  461. ax.errorbar(x_pos[i], mean, yerr=sem, fmt='none', ecolor=lcolor,
  462. elinewidth=2, capsize=10, zorder=2)
  464. ax.set_xticks(x_pos)
  465. ax.spines['top'].set_visible(False)
  466. ax.spines['right'].set_visible(False)
  468. plt.ylim((70, 160))
  469. ax.set_yticks(ticks=[90, 120, 150], labels=["90", "120", "150"], fontsize=12)
  470. plt.xticks([])
  471. if save :
  472. plt.savefig(f"ri_box.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  474. plt.show()
  476. # distance from soma vs somatic amplitude
  477. # scatter plot and fitting line
  478. # does not include dendrite information
  479. # should adjust nseg of the SF dendrite by dend_nseglevel
  480. # upper line from basal dendrite, lower line from apical dendrite
  481. def scatter_distance_from_soma_vs_somatic_amplitude(data, fit_params, save=False):
  482. import numpy as np
  483. from scipy.optimize import curve_fit
  485. #unpakcing
  486. dist_spine_ctl = data['dst_ctl']
  487. dist_spine_fcd = data['dst_fcd']
  488. amplitude_ctl = data['amp_ctl']
  489. amplitude_fcd = data['amp_fcd']
  491. a = fit_params['a']
  492. b = fit_params['b']
  493. c = fit_params['c']
  494. a_ = fit_params['a_']
  495. b_ = fit_params['b_']
  496. c_ = fit_params['c_']
  499. fig, ax = plt.subplots()
  501. plt.scatter(dist_spine_ctl, amplitude_ctl, marker="o", s=20, color="cyan", alpha=0.7, label="Baseline")
  502. plt.scatter(dist_spine_fcd, amplitude_fcd, marker="o", s=20, color="magenta", alpha=0.7, label="Altered")
  504. ## exponential fitting
  505. # Define the exponential function
  506. def exp_func(x, a, b, c):
  507. return a * np.exp(b * x) +c
  509. # Fit the curve
  510. params, covar_ = curve_fit(exp_func, dist_spine_ctl, amplitude_ctl, p0=[a, b, c])
  511. fit_a, fit_b, fit_c = params
  512. x_fit = np.linspace(min(dist_spine_ctl), max(dist_spine_ctl), 1000) # for a smooth curve
  513. y_fit = exp_func(x_fit, fit_a, fit_b, fit_c)
  514. plt.plot(x_fit, y_fit, color='darkcyan')
  515. print(f'Baseline: y={fit_a:.4f}e^({fit_b:.5f}x)+{fit_c:.4f}')
  517. # Fit the curve
  518. params_, covar_ = curve_fit(exp_func, dist_spine_fcd, amplitude_fcd, p0=[a_, b_, c_])
  519. fit_a_fcd, fit_b_fcd, fit_c_fcd = params_
  520. y_fit_ = exp_func(x_fit, fit_a_fcd, fit_b_fcd, fit_c_fcd)
  521. plt.plot(x_fit, y_fit_, color='darkmagenta')
  522. print(f'Altered: y={fit_a_fcd:.4f}e^({fit_b_fcd:.5f}x)+{fit_c_fcd:.4f}')
  524. plt.xlabel("Spine Location Relative to Soma (µm)", fontsize=14)
  525. plt.ylabel("Somatic EPSP Amplitude (mV)", fontsize=14)
  527. plt.xlim((30, 390))
  528. plt.ylim((0.08, 0.44))
  530. ax.set_xticks(ticks=[100, 200, 300], labels=["100", "200", "300"], fontsize=12)
  531. ax.set_yticks(ticks=[0.1, 0.2, 0.3, 0.4], labels=["0.1", "0.2", "0.3", "0.4"], fontsize=12)
  532. plt.tick_params(
  533. axis='y', # changes apply to the y-axis
  534. which='both', # both major and minor ticks are affected
  535. right=False)
  536. # if legend :
  537. # plt.legend(loc="upper right")
  539. # Force all spines on
  540. for spine in ax.spines.values():
  541. spine.set_visible(True)
  542. spine.set_color('black')
  544. if save :
  545. plt.savefig(f"distance_vs_soma_amp.png", dpi=600, bbox_inches='tight', pad_inches=0.01, transparent=False)
  547. plt.show()
  550. if __name__ == "__main__" :
  552. pass

figures.py at commit 0d2cd0b, under CC-BY-NC-4.0 · at the source

Overview

Authors: Jawon Gim1, Na-Young Seo1, Gyu Hyun Kim1, Kea Joo Lee1, Joon Ho Choi2
ORCID iDs: Na-Young Seo
  1. Neural Circuits Research Group, Korea Brain Research Institute (KBRI), Daegu, Republic of Korea
  2. Sensory and Motor System Research Group, Korea Brain Research Institute (KBRI), Daegu, Republic of Korea
Institutions: Korea Brain Research Institute (South Korea)
Journal: Frontiers in computational neuroscience, volume 20, article 1862383
Dates: received 22 April 2026; accepted 30 July 2026; published online 26 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fncom.2026.1862383 · PMID 42718645 · PMCID PMC13553748 · OpenAlex W7204233965
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), epilepsy (population)
Methods: Connectivity, Statistics, Preprocessing, Single-unit activity, calcium imaging
Keywords: computational modeling, dendritic spine, epilepsy, NEURON, volume electron microscopy
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Korea Brain Research Institute (26-BR-01-01, BR-01-03, 26-BR-01-03); Ministry of Science and ICT, South Korea
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Introduction: Focal cortical dysplasia (FCD) is a leading cause of drug-resistant epilepsy and has predominantly been associated with impaired inhibitory signaling. However, recent ultrastructural studies have identified morphological alterations in excitatory synapses, suggesting that structural changes in excitatory connectivity may also contribute to cortical hyperexcitability.

Methods: We used biophysically grounded computational models of human cortical pyramidal neurons to investigate how disease-associated alterations in dendritic spine architecture affect neuronal excitability. Spine density and spine geometry were independently manipulated based on quantitative volume electron microscopy measurements, allowing us to distinguish the contributions of individual structural features of excitatory synapses.

Results: Reduced spine density and altered spine neck geometry increased neuronal excitability through complementary mechanisms, whereas variations in spine head size had comparatively minor effects. These structural alterations differentially influenced synaptic signal propagation and spike initiation. Their combined effects substantially increased neuronal output, particularly under conditions of sparse synaptic input.

Discussion: These findings identify excitatory synaptic microstructure as an independent and mechanistically distinct contributor to hyperexcitability in FCD Type I. By linking ultrastructural abnormalities to altered neuronal input–output function, this study supports a potential contribution of excitatory synaptic alterations to the pathophysiology of FCD.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

jawonGim/Simulation_SpineAlterationFocalCorticalDysplasia

License: CC-BY-NC-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0d2cd0b63f0cc3e4432c56be518c7ddf7bee76ec, 16 April 2026
Languages: Python (12), NEURON (4)
Size: 20 files, 16 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (15 files), NumPy (9 files), Matplotlib (6 files), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/jawonGim/Simulation_SpineAlterationFocalCorticalDysplasia.git

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

No dataset and no data link were found in the paper.

Data availability statement

All custom codes used for simulations, data analysis, and figure generation are available here: https://github.com/jawonGim/Simulation_SpineAlterationFocalCorticalDysplasia.git.

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

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Version 2, 28 September 2026

  • Funding: added Korea Brain Research Institute: 26-BR-01-01, BR-01-03, 26-BR-01-03; Ministry of Science and ICT, South Korea

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 35 references.

Cite

This paper

Gim, J., Seo, N.-Y., Kim, G. H., Lee, K. J., & Choi, J. H. (2026). Microstructural spine alterations increase neuronal excitability in focal cortical dysplasia Type I. Frontiers in computational neuroscience, 20, 1862383. https://doi.org/10.3389/fncom.2026.1862383

BibTeX

@article{gim2026microstructural,
author = {Gim, Jawon and Seo, Na-Young and Kim, Gyu Hyun and Lee, Kea Joo and Choi, Joon Ho},
title = {{Microstructural spine alterations increase neuronal excitability in focal cortical dysplasia Type I}},
journal = {Frontiers in computational neuroscience},
year = {2026},
month = aug,
volume = {20},
pages = {1862383},
publisher = {Frontiers Media SA},
issn = {1662-5188},
doi = {10.3389/fncom.2026.1862383},
url = {https://doi.org/10.3389/fncom.2026.1862383},
pmid = {42718645},
pmcid = {PMC13553748}
}

RIS

TY - JOUR
AU - Gim, Jawon
AU - Seo, Na-Young
AU - Kim, Gyu Hyun
AU - Lee, Kea Joo
AU - Choi, Joon Ho
TI - Microstructural spine alterations increase neuronal excitability in focal cortical dysplasia Type I
T2 - Frontiers in computational neuroscience
J2 - Front Comput Neurosci
PY - 2026
DA - 2026/08/26
VL - 20
SP - 1862383
SN - 1662-5188
PB - Frontiers Media SA
DO - 10.3389/fncom.2026.1862383
UR - https://doi.org/10.3389/fncom.2026.1862383
LA - en
ER -

CSL-JSON

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"container-title": "Frontiers in computational neuroscience",
"author": [
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"family": "Gim",
"given": "Jawon"
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"given": "Na-Young"
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{
"family": "Kim",
"given": "Gyu Hyun"
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{
"family": "Lee",
"given": "Kea Joo"
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"volume": "20",
"page": "1862383",
"DOI": "10.3389/fncom.2026.1862383",
"PMID": "42718645",
"PMCID": "PMC13553748",
"ISSN": "1662-5188",
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"URL": "https://doi.org/10.3389/fncom.2026.1862383",
"language": "en",
"issued": {
"date-parts": [
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2026,
8,
26
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