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Forward model of rat electroencephalogram: influence of inhomogeneous and anisotropic skull.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Sensing electrodes and modelling of neural activity of the brain ↔ plot_summary_figure.py, lines 1–45 · score 0.54 · head model, dipole orientation, inhomogeneity, anisotropy
  2. [2] § Methods › Leadfield comparisons on a volumetric grid ↔ plot_summary_figure.py, lines 1–45 · score 0.51 · head model, dipole orientations, vector, Leadfield

Paper

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The authors' code

Python · 321 lines · 13 KB · no license · 2 matches

  1. """
  2. Step 2 of 2 — Generate the leadfield contrast summary figure.
  4. Prerequisites
  5. -------------
  6. 1. Install dependencies:
  7. pip install -r requirements.txt
  8. 2. Run compute_metrics.py first (or use the pre-computed files in data/).
  10. Usage
  11. -----
  12. python plot_summary_figure.py
  14. Output
  15. ------
  16. lf_summary_figure.png (filename controlled by config.OUTPUT_FILE)
  18. What the figure shows
  19. ---------------------
  20. Each row corresponds to one leadfield contrast (L0 vs L1, L0 vs L4, L1 vs L4).
  21. Within each row there are two sub-rows:
  22. • Top sub-row — XY / XZ / YZ projection maps of the normalised Euclidean
  23. distance between the two leadfield models, overlaid with
  24. arrows indicating the dipole orientation that maximises that
  25. distance (max-contrast direction).
  26. • Bottom sub-row — scatter plots of each spatial coordinate (X, Y, Z) against
  27. the corresponding component of the unit max-contrast
  28. direction vector.
  30. Level codes
  31. -----------
  32. L0 — homogeneous head model
  33. L1 — inhomogeneous head model
  34. L4 — anisotropic head model
  35. """
  37. import sys
  38. import os
  39. from pathlib import Path
  41. import numpy as np
  42. import matplotlib
  43. import matplotlib.pyplot as plt
  44. import matplotlib.gridspec as gridspec
  45. from scipy.interpolate import griddata
  47. # ── locate config / data relative to this script ─────────────────────────────
  48. _HERE = Path(os.path.abspath(__file__)).parent
  49. sys.path.insert(0, str(_HERE))
  50. import config as cfg
  52. DATA_DIR = _HERE / cfg.OUTPUT_PATH
  54. # Contrasts to include, in display order.
  55. # Each entry is (forward_level, inverse_level) — must match the .npz filenames.
  56. CONTRASTS = cfg.LEADFIELD_CONTRASTS
  59. # ─────────────────────────────────────────────────────────────────────────────
  60. # Helper: 2-D projection with optional interpolation
  61. # ─────────────────────────────────────────────────────────────────────────────
  63. def _aggregate_projection(dip_pos, values, axis, agg_func='mean', interpolate=True):
  64. """
  65. Collapse a 3-D scatter of (dipole positions, scalar values) onto a 2-D plane
  66. by removing one axis and aggregating duplicate positions.
  68. Parameters
  69. ----------
  70. dip_pos : (N, 3) array of dipole positions [mm]
  71. values : (N,) scalar value per dipole
  72. axis : int axis to project out (0=x, 1=y, 2=z)
  73. agg_func : str 'mean', 'min', or 'max'
  74. interpolate: bool fill missing grid cells with linear interpolation
  76. Returns
  77. -------
  78. coords_0, coords_1 : 1-D arrays with unique coordinates on the two kept axes
  79. projection : (len(coords_0), len(coords_1)) aggregated grid
  80. """
  81. axes = [0, 1, 2]
  82. axes.remove(axis)
  84. coords_0 = np.unique(dip_pos[:, axes[0]])
  85. coords_1 = np.unique(dip_pos[:, axes[1]])
  87. X_grid, Y_grid = np.meshgrid(coords_0, coords_1, indexing='ij')
  88. projection = np.full(X_grid.shape, np.nan)
  90. agg = {'mean': np.mean, 'max': np.max, 'min': np.min}[agg_func]
  91. for i, c0 in enumerate(coords_0):
  92. for j, c1 in enumerate(coords_1):
  93. mask = (dip_pos[:, axes[0]] == c0) & (dip_pos[:, axes[1]] == c1)
  94. if np.any(mask):
  95. projection[i, j] = agg(values[mask])
  97. if interpolate:
  98. valid = ~np.isnan(projection)
  99. if np.any(valid) and np.any(~valid):
  100. pts = np.column_stack([X_grid[valid], Y_grid[valid]])
  101. vals_v = projection[valid]
  102. pts_all = np.column_stack([X_grid.ravel(), Y_grid.ravel()])
  103. projection = griddata(pts, vals_v, pts_all, method='linear').reshape(X_grid.shape)
  105. return coords_0, coords_1, projection
  108. # ─────────────────────────────────────────────────────────────────────────────
  109. # Main figure
  110. # ─────────────────────────────────────────────────────────────────────────────
  112. def plot_summary_figure(dip_pos, all_metrics, elec_pos, contrasts, clim_euclid, output_file):
  113. """
  114. Build and save the leadfield-contrast summary figure.
  116. Parameters
  117. ----------
  118. dip_pos : (nDip, 3) decimated dipole positions [mm]
  119. all_metrics : dict {(level_a, level_b): (corr, euclid, rel_diff, mc_vecs)}
  120. elec_pos : (nElec, 3) electrode positions [mm]
  121. contrasts : list of (level_a, level_b) pairs
  122. clim_euclid : (vmin, vmax) or None for auto colour limits
  123. output_file : Path where to write the figure
  124. """
  125. n = len(contrasts)
  126. h_comb = cfg.MEGA_FIGURE_H_COMBINED
  127. h_scat = cfg.MEGA_FIGURE_H_SCATTER
  128. h_ratios = [val for _ in range(n) for val in (h_comb, h_scat)]
  130. fig_w = cfg.FIGSIZE_PROJECTION[0]
  131. fig_h = n * (h_comb + h_scat)
  132. fig = plt.figure(figsize=(fig_w, fig_h))
  133. gs = gridspec.GridSpec(
  134. 2 * n, 3,
  135. height_ratios=h_ratios,
  136. hspace=cfg.MEGA_FIGURE_HSPACE,
  137. wspace=0.35,
  138. figure=fig,
  139. )
  141. vmin, vmax = clim_euclid if clim_euclid is not None else (None, None)
  142. contour_lvls = (
  143. np.linspace(vmin, vmax, cfg.CONTOUR_LEVELS)
  144. if vmin is not None and vmax is not None
  145. else cfg.CONTOUR_LEVELS
  146. )
  148. coord_labels = ['X [mm]', 'Y [mm]', 'Z [mm]']
  149. comp_labels = ['X', 'Y', 'Z']
  150. scatter_colors = ['tab:red', 'tab:blue', 'tab:green']
  152. # (horiz_axis, vert_axis, xlabel, ylabel)
  153. proj_specs = [
  154. (0, 1, 'X [mm]', 'Y [mm]'),
  155. (0, 2, 'X [mm]', 'Z [mm]'),
  156. (1, 2, 'Y [mm]', 'Z [mm]'),
  157. ]
  159. last_cf = None
  161. for row_idx, (level_a, level_b) in enumerate(contrasts):
  162. _, euclid, _, mc_vecs = all_metrics[(level_a, level_b)]
  164. # Scale arrows so the longest arrow = QUIVER_SCALE_LF_CONTRAST [mm]
  165. mags = np.linalg.norm(mc_vecs, axis=1)
  166. max_mag = np.nanmax(mags) if np.nanmax(mags) > 1e-12 else 1.0
  167. scaled = mc_vecs / max_mag * cfg.QUIVER_SCALE_LF_CONTRAST
  169. # 2-D projections of normalised Euclidean distance
  170. Xp, Yp, XY = _aggregate_projection(dip_pos, euclid, 2, cfg.AGG_FUNC)
  171. Xp, Zp, XZ = _aggregate_projection(dip_pos, euclid, 1, cfg.AGG_FUNC)
  172. Yp, Zp, YZ = _aggregate_projection(dip_pos, euclid, 0, cfg.AGG_FUNC)
  173. proj_grids = [
  174. (Xp, Yp, XY),
  175. (Xp, Zp, XZ),
  176. (Yp, Zp, YZ),
  177. ]
  179. combined_row = 2 * row_idx
  180. scatter_row = 2 * row_idx + 1
  181. panel_label = chr(ord('A') + row_idx)
  183. # ── Top sub-row: contourf + quiver ────────────────────────────────────
  184. for col, ((xi, yi, xlabel, ylabel), (cx, cy, cgrid)) in enumerate(
  185. zip(proj_specs, proj_grids)):
  186. ax = fig.add_subplot(gs[combined_row, col])
  188. if col == 0:
  189. ax.text(
  190. -0.18, 1.0, panel_label,
  191. transform=ax.transAxes,
  192. fontsize=cfg.FONTSIZE_TITLE + 2,
  193. fontweight='bold',
  194. va='top', ha='right', clip_on=False,
  195. )
  197. cf = ax.contourf(
  198. cx, cy, cgrid.T,
  199. levels=contour_lvls,
  200. cmap=cfg.COLORMAP_LF_EUCLIDEAN,
  201. vmin=vmin, vmax=vmax,
  202. )
  203. ax.quiver(
  204. dip_pos[:, xi], dip_pos[:, yi],
  205. scaled[:, xi], scaled[:, yi],
  206. color='black', scale=1, scale_units='xy',
  207. width=0.003, alpha=0.7,
  208. )
  209. ax.scatter(
  210. elec_pos[:, xi], elec_pos[:, yi],
  211. c='b', s=30, zorder=5,
  212. label='Electrodes' if col == 0 else None,
  213. )
  214. ax.grid(True, alpha=0.3)
  215. ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
  216. ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
  217. ax.set_aspect('equal')
  218. ax.set_xlabel(xlabel, fontsize=cfg.FONTSIZE_AXIS_LABEL)
  220. if col == 0:
  221. ax.set_ylabel(
  222. f'L{level_a} vs L{level_b}\n{ylabel}',
  223. fontsize=cfg.FONTSIZE_AXIS_LABEL,
  224. )
  225. ax.legend(fontsize=7, markerscale=1.2)
  226. else:
  227. ax.set_ylabel(ylabel, fontsize=cfg.FONTSIZE_AXIS_LABEL)
  229. ax.tick_params(labelsize=cfg.FONTSIZE_TICK_LABEL)
  230. last_cf = cf
  232. # ── Bottom sub-row: coordinate vs direction component scatter ─────────
  233. norms = np.linalg.norm(mc_vecs, axis=1)
  234. valid = norms > 1e-12
  235. directions = np.full_like(mc_vecs, np.nan)
  236. directions[valid] = mc_vecs[valid] / norms[valid, np.newaxis]
  238. for col in range(3):
  239. ax = fig.add_subplot(gs[scatter_row, col])
  240. coord_vals = dip_pos[:, col]
  241. ax.scatter(
  242. coord_vals[valid], directions[valid, col],
  243. c=scatter_colors[col], s=cfg.SCATTER_MARKER_SIZE, alpha=0.25,
  244. )
  245. ax.set_xlabel(coord_labels[col], fontsize=cfg.FONTSIZE_AXIS_LABEL)
  246. ax.set_ylabel(f'dir {comp_labels[col]}', fontsize=cfg.FONTSIZE_AXIS_LABEL)
  247. ax.set_ylim(-1.05, 1.05)
  248. ax.axhline(0, color='gray', linewidth=0.8, linestyle='--')
  249. ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
  250. ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
  251. ax.grid(True, alpha=0.3)
  252. ax.tick_params(labelsize=cfg.FONTSIZE_TICK_LABEL)
  254. # ── Shared horizontal colorbar ────────────────────────────────────────────
  255. if cfg.SHOW_COLORBAR and last_cf is not None:
  256. fig.subplots_adjust(bottom=0.06)
  257. cbar_ax = fig.add_axes([0.1, 0.02, 0.8, 0.008])
  258. cbar = fig.colorbar(last_cf, cax=cbar_ax, orientation='horizontal')
  259. cbar.set_label('LF Normalised Euclidean', fontsize=cfg.FONTSIZE_COLORBAR_LABEL)
  260. if vmin is not None and vmax is not None:
  261. cbar.set_ticks(np.arange(vmin, vmax + 0.01, 0.1))
  262. cbar.ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))
  263. cbar.ax.tick_params(labelsize=cfg.FONTSIZE_TICK_LABEL)
  265. plt.savefig(output_file, dpi=cfg.IMAGE_DPI, bbox_inches='tight')
  266. plt.close()
  267. print(f"Saved: {output_file}")
  270. # ─────────────────────────────────────────────────────────────────────────────
  271. # Entry point
  272. # ─────────────────────────────────────────────────────────────────────────────
  274. def main():
  275. matplotlib.rcParams.update({
  276. 'axes.labelsize': cfg.FONTSIZE_AXIS_LABEL,
  277. 'xtick.labelsize': cfg.FONTSIZE_TICK_LABEL,
  278. 'ytick.labelsize': cfg.FONTSIZE_TICK_LABEL,
  279. 'axes.titlesize': cfg.FONTSIZE_TITLE,
  280. 'legend.fontsize': cfg.FONTSIZE_LEGEND,
  281. })
  283. print("Loading pre-computed data from:", DATA_DIR)
  285. dip_pos = np.load(DATA_DIR / 'dipole_positions.npy')
  286. elec_pos = np.load(DATA_DIR / 'electrode_positions.npy')
  287. print(f" dipole_positions: {dip_pos.shape}")
  288. print(f" electrode_positions: {elec_pos.shape}")
  290. all_metrics = {}
  291. for (la, lb) in CONTRASTS:
  292. fname = DATA_DIR / f'metrics_L{la}vsL{lb}.npz'
  293. if not fname.exists():
  294. raise FileNotFoundError(
  295. f"Missing data file: {fname}\n"
  296. "Run compute_metrics.py first (see README.md)."
  297. )
  298. d = np.load(fname)
  299. all_metrics[(la, lb)] = (
  300. d['correlations'],
  301. d['euclidean_dists'],
  302. d['rel_diffs'],
  303. d['max_contrast_vecs'],
  304. )
  305. print(f" metrics_L{la}vsL{lb}.npz "
  306. f"(euclid mean={np.nanmean(d['euclidean_dists']):.4f})")
  308. clim = cfg.CLIM_LF_EUCLIDEAN
  309. if clim is None:
  310. all_euclid = np.concatenate([all_metrics[k][1] for k in all_metrics])
  311. clim = (float(np.nanmin(all_euclid)), float(np.nanmax(all_euclid)))
  312. print(f" Auto colour limits: {clim}")
  313. else:
  314. print(f" Colour limits (from config): {clim}")
  316. output_file = _HERE / cfg.OUTPUT_FILE
  317. plot_summary_figure(dip_pos, all_metrics, elec_pos, CONTRASTS, clim, output_file)
  320. if __name__ == '__main__':
  321. main()

plot_summary_figure.py at commit f780e22, no license · at the source

Overview

Authors: David Kuratko1, Vlastimil Koudelka2, Cestmir Vejmola2, Filip Dusek1, Frantisek Kucera3, Daniel K Wójcik4, Tomas Palenicek2,5, Jaroslav Lacik1
  1. Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, 61600 Czech Republic
  2. National Institute of Mental Health, Klecany, 250 67 Czech Republic
  3. Faculty of Chemistry, Brno University of Technology, Brno, 61200 Czech Republic
  4. Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland
  5. Third Faculty of Medicine, Charles University, Prague, 10000 Czech Republic
Institutions: Brno University of Technology (Czechia); National Institute of Mental Health (Czechia); Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (Poland); Polish Academy of Sciences (Poland); Charles University (Czechia)
Journal: Scientific reports, volume 16, issue 1, article 28626
Dates: received 22 December 2025; accepted 17 June 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-58885-1 · PMID 42331932 · PMCID PMC13575132 · OpenAlex W7165547085
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), rat (organism)
Methods: Preprocessing, Evoked potentials, Connectivity
Keywords: Engineering, Neuroscience, Physics
MeSH: Brain*, Electroencephalography*, Models, Neurological*, Skull*, Animals, Anisotropy, Phantoms, Imaging, Rats (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Grantová Agentura České Republiky (23-07578K)
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

VlastaKoudelka/skull-leadfield

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f780e223dff6faadc53f209d3b4fba61698481f9, 11 June 2026
Languages: Python (3)
Size: 17 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (2 files), SciPy (2 files), Matplotlib (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/VlastaKoudelka/skull-leadfield

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

Tracing map

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  • 3 scripts, each with its path and the digest of its content;
  • 2 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

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-58885-1.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 3 keywords, 8 MeSH terms, 1 funder, 52 references.

Cite

This paper

Kuratko, D., Koudelka, V., Vejmola, C., Dusek, F., Kucera, F., Wójcik, D. K., Palenicek, T., & Lacik, J. (2026). Forward model of rat electroencephalogram: influence of inhomogeneous and anisotropic skull. Scientific reports, 16(1), 28626. https://doi.org/10.1038/s41598-026-58885-1

BibTeX

@article{kuratko2026forward,
author = {Kuratko, David and Koudelka, Vlastimil and Vejmola, Cestmir and Dusek, Filip and Kucera, Frantisek and Wójcik, Daniel K and Palenicek, Tomas and Lacik, Jaroslav},
title = {{Forward model of rat electroencephalogram: influence of inhomogeneous and anisotropic skull}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {28626},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-58885-1},
url = {https://doi.org/10.1038/s41598-026-58885-1},
pmid = {42331932},
pmcid = {PMC13575132}
}

RIS

TY - JOUR
AU - Kuratko, David
AU - Koudelka, Vlastimil
AU - Vejmola, Cestmir
AU - Dusek, Filip
AU - Kucera, Frantisek
AU - Wójcik, Daniel K
AU - Palenicek, Tomas
AU - Lacik, Jaroslav
TI - Forward model of rat electroencephalogram: influence of inhomogeneous and anisotropic skull
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/22
VL - 16
IS - 1
SP - 28626
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-58885-1
UR - https://doi.org/10.1038/s41598-026-58885-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-58885-1",
"type": "article-journal",
"title": "Forward model of rat electroencephalogram: influence of inhomogeneous and anisotropic skull",
"container-title": "Scientific reports",
"author": [
{
"family": "Kuratko",
"given": "David"
},
{
"family": "Koudelka",
"given": "Vlastimil"
},
{
"family": "Vejmola",
"given": "Cestmir"
},
{
"family": "Dusek",
"given": "Filip"
},
{
"family": "Kucera",
"given": "Frantisek"
},
{
"family": "Wójcik",
"given": "Daniel K"
},
{
"family": "Palenicek",
"given": "Tomas"
},
{
"family": "Lacik",
"given": "Jaroslav"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "28626",
"DOI": "10.1038/s41598-026-58885-1",
"PMID": "42331932",
"PMCID": "PMC13575132",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-58885-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
22
]
]
}
}

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