OSCR

Multimodal reference brain atlas of adult <i>Danionella cerebrum</i>

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 › Sexual dimorphism analysis ↔ Fig5.ipynb, lines 289–333 · score 0.68 · female biased, male female, volume change, dimorphic, Figure 5
  2. [2] § Methods › Brain area overrepresentation analysis ↔ Fig4.ipynb, lines 890–929 · score 0.52 · Phipson Smyth, enrichment, permutation, Figure 4

Paper

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The authors' code

Jupyter notebook · 581 lines · 18 KB · CC-BY-NC-SA-4.0 · 1 match

  1. # %% [markdown]
  2. # ## Notebook to generate the panels for the Fig. 5 of Kadobianskyi et al., 2026
  4. # %% [markdown]
  5. # ### Registration and analysis of the morphological differences in male and female Danionella cerebrum
  7. # %% [markdown]
  8. # Load libraries, ants numpy matplotlib. Additional requirements: pandas, seaborn
  10. # %%
  11. %env ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=16
  12. import ants
  13. import numpy as np
  14. from matplotlib import pyplot as plt
  15. import sys, os
  16. from ants.core import ants_image_io as iio
  19. # %% [markdown]
  20. # Load the template
  22. # %%
  23. template_mixed = ants.image_read('./template_2p_mixed.nii')
  24. template_mixed
  26. # %%
  27. plt.figure(figsize=(12, 12), dpi=100)
  28. plt.imshow(template_mixed.max(2), cmap='gray')
  29. plt.show()
  31. # %% [markdown]
  32. # Load the jacobian determinants of the warp maps from each individual male and female fish to the mixed template. These were generated from the warp maps in the following way:
  33. #
  34. # ```
  35. # male_warped = ants.image_read('.../fish01.regd.nii.gz') # and 10 other warp maps for male fish
  36. # female_warped = ants.image_read('.../fish11.regd.nii.gz') # and 9 other warp maps for female fish
  37. #
  38. # logj_female = ants.create_jacobian_determinant_image(domain_image=template_mixed, do_log=True, tx='.../deformations/fish01.warptotemp.nii.gz')
  39. # logj_male = ants.create_jacobian_determinant_image(domain_image=template_mixed, do_log=True, tx='.../deformations/fish11.warptotemp.nii.gz')
  40. # ```
  41. #
  42. # and saved into the lists of warp maps onto the disk
  43. #
  44. # ```
  45. # logjs_male_np = [logj.numpy() for logj in logjs_male]
  46. # logjs_female_np = [logj.numpy() for logj in logjs_female]
  47. # np.savez_compressed('./logjs.npz', female=logjs_female_np, male=logjs_male_np)
  49. # %%
  50. logjs = np.load('sdm//logjs.npz')
  52. # %%
  53. logjs_female_np = logjs['female']
  54. logjs_male_np = logjs['male']
  56. # %% [markdown]
  57. # Load the segmentation with masks and remove the eyes
  59. # %%
  60. masks = ants.image_read('./segmentation.nii.gz')
  61. masks_np = masks.numpy()
  62. masks_np[masks_np == 204] = 0
  64. # %%
  65. z = 95
  66. plt.figure(figsize=(12, 12), dpi=100)
  67. plt.imshow(masks_np[:,:,z], cmap='Reds', alpha=.5)
  68. plt.imshow(logjs_male_np[1][:,:,z], cmap='Greys', alpha=.5)
  69. plt.show()
  71. # %% [markdown]
  72. # Import the label definitions
  74. # %%
  75. from collections import defaultdict
  77. label_names = {}
  78. with open('./dc_label_descriptions.txt', 'r') as file:
  79. lines = file.readlines()
  80. for line in lines:
  81. if line.startswith('#') or line.strip() == '':
  82. continue
  83. parts = line.strip().split()
  84. index = int(parts[0])
  85. name = ' '.join(parts[7:]).replace('"', '')
  86. label_names[index] = name
  88. # %% [markdown]
  89. # Prepare the test code
  91. # %%
  92. import numpy as np
  93. import pandas as pd
  94. from scipy.stats import t as tdist
  95. from statsmodels.stats.multitest import multipletests
  98. def prepare_label_index(masks_np, include_ids=None, exclude_ids=None):
  99. lab = np.asarray(masks_np, dtype=np.int64).ravel()
  101. if include_ids is None:
  102. label_ids = np.unique(lab)
  103. label_ids = label_ids[label_ids != 0]
  104. else:
  105. label_ids = np.array(sorted(i for i in include_ids if i != 0), dtype=np.int64)
  107. if exclude_ids is not None:
  108. exclude_ids = set(int(x) for x in exclude_ids)
  109. label_ids = np.array([i for i in label_ids if i not in exclude_ids], dtype=np.int64)
  111. if label_ids.size == 0:
  112. raise ValueError("No labels left after filtering.")
  114. lut = np.full(int(lab.max()) + 1, -1, dtype=np.int64)
  115. lut[label_ids] = np.arange(label_ids.size, dtype=np.int64)
  117. roi_index = lut[lab]
  118. keep_mask = roi_index >= 0
  119. roi_index = roi_index[keep_mask]
  121. voxel_counts = np.bincount(roi_index, minlength=label_ids.size)
  123. return roi_index, keep_mask, label_ids, voxel_counts
  126. def compute_roi_means(volumes, keep_mask, roi_index, n_rois):
  127. X = np.zeros((len(volumes), n_rois), dtype=np.float64)
  129. for i, vol in enumerate(volumes):
  130. values = np.asarray(vol).ravel()[keep_mask]
  131. sums = np.bincount(roi_index, weights=values, minlength=n_rois)
  132. counts = np.bincount(roi_index, minlength=n_rois)
  133. X[i] = sums / np.maximum(counts, 1)
  135. return X
  137. # convert log differences to linear differences
  138. def compute_mean_roi_volumes(volumes, keep_mask, roi_index, n_rois):
  139. out = np.zeros(n_rois, dtype=np.float64)
  141. for vol in volumes:
  142. jac = np.exp(np.asarray(vol).ravel()[keep_mask])
  143. sums = np.bincount(roi_index, weights=jac, minlength=n_rois)
  144. out += sums
  146. return out / max(len(volumes), 1)
  149. # Main test function for the Welch t-test
  151. def roi_stats_logJ(
  152. logjs_male_np,
  153. logjs_female_np,
  154. masks_np,
  155. label_info=None,
  156. include_ids=None,
  157. exclude_ids=None,
  158. min_voxels=50, # min voxel number per mask to consider for the test
  159. ):
  160. roi_index_all, keep_mask, label_ids_all, voxel_counts_all = prepare_label_index(
  161. masks_np,
  162. include_ids=include_ids,
  163. exclude_ids=exclude_ids,
  164. )
  166. n_rois_all = label_ids_all.size
  168. Xm_all = compute_roi_means(logjs_male_np, keep_mask, roi_index_all, n_rois_all)
  169. Xf_all = compute_roi_means(logjs_female_np, keep_mask, roi_index_all, n_rois_all)
  171. keep_roi = voxel_counts_all >= int(min_voxels)
  173. label_ids = label_ids_all[keep_roi]
  174. voxel_counts = voxel_counts_all[keep_roi]
  176. Xm = Xm_all[:, keep_roi]
  177. Xf = Xf_all[:, keep_roi]
  179. nm = Xm.shape[0]
  180. nf = Xf.shape[0]
  182. mean_m = Xm.mean(axis=0)
  183. mean_f = Xf.mean(axis=0)
  185. var_m = Xm.var(axis=0, ddof=1)
  186. var_f = Xf.var(axis=0, ddof=1)
  188. delta_logJ = mean_m - mean_f
  190. pooled_var = ((nm - 1) * var_m + (nf - 1) * var_f) / max(nm + nf - 2, 1)
  191. pooled_sd = np.sqrt(np.maximum(pooled_var, 1e-12))
  192. # Effect sizes: Cohen's d / Hedge's g
  193. cohens_d = delta_logJ / pooled_sd
  194. J = 1.0 - 3.0 / (4 * (nm + nf) - 9)
  195. hedges_g = J * cohens_d
  197. vol_m_all = compute_mean_roi_volumes(logjs_male_np, keep_mask, roi_index_all, n_rois_all)
  198. vol_f_all = compute_mean_roi_volumes(logjs_female_np, keep_mask, roi_index_all, n_rois_all)
  200. vol_m = vol_m_all[keep_roi]
  201. vol_f = vol_f_all[keep_roi]
  202. # linear volume difference
  203. delta_pct = 100.0 * (vol_m - vol_f) / (vol_f + 1e-12)
  205. se = np.sqrt(np.maximum(var_m / nm + var_f / nf, 1e-18))
  206. t_stat = delta_logJ / se
  208. df_num = (var_m / nm + var_f / nf) ** 2
  209. df_den = (
  210. (var_m ** 2) / (nm ** 2 * max(nm - 1, 1))
  211. + (var_f ** 2) / (nf ** 2 * max(nf - 1, 1))
  212. )
  213. dof = np.maximum(df_num / np.maximum(df_den, 1e-18), 1.0)
  214. # p-value
  215. p_value = 2.0 * tdist.sf(np.abs(t_stat), dof)
  216. # correct for multiple tests
  217. reject_null, p_value_adj, _, _ = multipletests(p_value, method="fdr_bh")
  219. df = pd.DataFrame({
  220. "label_id": label_ids.astype(int),
  221. "n_vox": voxel_counts.astype(int),
  222. "mean_male": mean_m,
  223. "mean_female": mean_f,
  224. "delta_logJ": delta_logJ,
  225. "vol_m": vol_m,
  226. "vol_f": vol_f,
  227. "delta_pct": delta_pct,
  228. "sd_male": np.sqrt(var_m),
  229. "sd_female": np.sqrt(var_f),
  230. "cohens_d": cohens_d,
  231. "hedges_g": hedges_g,
  232. "p_value": p_value,
  233. "p_value_adj": p_value_adj,
  234. "reject_null": reject_null,
  235. })
  237. if label_info is not None:
  238. df["label_name"] = pd.Series(label_info).reindex(df["label_id"]).values
  240. return df.sort_values("p_value_adj").reset_index(drop=True)
  242. def largest_volume_changes(df, top_n=20, only_sig=False, q=0.05):
  243. d = add_delta_pct_if_missing(df)
  245. if only_sig and "p_value_adj" in d.columns:
  246. d = d[d["p_value_adj"] < q]
  248. d = d.sort_values("delta_pct", key=lambda s: np.abs(s), ascending=False)
  250. cols = [
  251. c for c in [
  252. "label_id", "label_name", "n_vox",
  253. "vol_m", "vol_f",
  254. "mean_male", "mean_female", "delta_logJ", "delta_pct",
  255. "cohens_d", "hedges_g",
  256. "p_value", "p_value_adj", "reject_null", "direction"
  257. ]
  258. if c in d.columns
  259. ]
  261. return d[cols].head(top_n)
  263. # %% [markdown]
  264. # Now run the test
  266. # %%
  267. df_jac = roi_stats_logJ(
  268. logjs_male_np=logjs_male_np,
  269. logjs_female_np=logjs_female_np,
  270. masks_np=masks_np, # segmentation mask
  271. label_info=label_names, # label names / IDs
  272. exclude_ids=None, # no labels excluded
  273. min_voxels=0 # no small labels excluded
  274. )
  275. # Can be loaded from sdm/welch_SDM_table.csv
  277. # %%
  278. df_jac.head()
  280. # %% [markdown]
  281. # Build a plot of top 10 male- and top 10 female-biased brain regions by relative volume change (linear)
  283. # %%
  284. import numpy as np
  285. import pandas as pd
  286. import seaborn as sns
  287. import matplotlib.pyplot as plt
  289. def plot_regions_by_percent_change(
  290. df, top_n=20, title="Dimorphic regions by volume change",
  291. out_pdf="regions_percent_change.pdf",
  292. p_col="p_value_adj",
  293. size_cap_log10=6.0, size_range=(50, 300),
  294. palette={"female > male": "#5E81AC", "male > female": "#D08770"},
  295. show_size_legend=True,
  296. # squish the plot around the middle -20/+20% to preserve space
  297. squish=True, gap=20.0, center_scale=0.35,
  298. ):
  299. """
  300. Plot dimorphic regions along a single vertical axis.
  301. - Selects top_n/2 regions with largest negative delta_pct (female > male)
  302. and top_n/2 with largest positive delta_pct (male > female),
  303. then plots them together on one axis.
  304. - x-axis is delta_pct (male − female), squished around 0.
  305. - Dot size indicates significance (-log10 p).
  306. """
  307. d = df.copy()
  309. # Split into male-biased (delta>0) and female-biased (delta<0)
  310. d_pos = d[d["delta_pct"] > 0].copy() # male > female
  311. d_neg = d[d["delta_pct"] < 0].copy() # female > male
  313. # Top N labels per side (male/female)
  314. n_each = max(1, top_n // 2)
  316. pos_top = d_pos.sort_values("delta_pct", ascending=False).head(n_each)
  317. neg_top = d_neg.sort_values("delta_pct", ascending=True).head(n_each)
  319. # Combine and sort by signed delta so female-biased appear at top
  320. d_plot = pd.concat([neg_top, pos_top], axis=0)
  321. d_plot = d_plot.sort_values("delta_pct", ascending=True).copy()
  323. # Direction labels for coloring
  324. d_plot["Direction"] = np.where(
  325. d_plot["delta_pct"] > 0, "male > female", "female > male"
  326. )
  328. # Region names for y-axis
  329. d_plot["Region"] = d_plot.get("label_name", d_plot.get("label_id"))
  330. d_plot["Region"] = d_plot["Region"].fillna(d_plot.get("label_id")).astype(str)
  331. d_plot["Region"] = pd.Categorical(
  332. d_plot["Region"], categories=d_plot["Region"], ordered=True
  333. )
  335. # Significance → size mapping
  336. q = d_plot.get(p_col, d_plot.get("p_value", pd.Series(np.ones(len(d_plot))))).astype(float)
  337. q = q.fillna(1.0).clip(lower=1e-300, upper=1.0)
  338. sig = np.minimum(-np.log10(q), size_cap_log10) / size_cap_log10
  339. s_min, s_max = size_range
  340. sizes = s_min + sig * (s_max - s_min)
  342. # transform x axis (squish)
  343. x = d_plot["delta_pct"].values.astype(float)
  345. def ticks_from_data(x_vals, gap=15.0):
  346. lo, hi = np.min(x_vals), np.max(x_vals)
  347. step = 20.0
  348. base = np.arange(np.floor(lo/step)*step, np.ceil(hi/step)*step + 0.1, step)
  349. for v in (-gap, 0.0, +gap):
  350. if v < lo or v > hi:
  351. continue
  352. if not np.any(np.isclose(base, v)):
  353. base = np.sort(np.r_[base, v])
  354. return base
  356. if squish and np.any(x < -gap) and np.any(x > gap) and center_scale < 1.0:
  357. b = center_scale * gap # new half-width for center
  358. shift = gap - b
  360. def f(v):
  361. v = np.asarray(v, float).copy()
  362. mid = (np.abs(v) <= gap)
  363. left = (v < -gap)
  364. right = (v > +gap)
  365. v[mid] = (b/gap) * v[mid] # compress center region
  366. v[left] = v[left] + shift # pull left block rightward
  367. v[right] = v[right] - shift # pull right block leftward
  368. return v
  370. x_plot = f(x)
  371. tick_vals = ticks_from_data(x, gap=gap)
  372. tick_pos = f(tick_vals)
  373. zero_x = f(np.array([0.0]))[0]
  374. else:
  375. def f(v): return np.asarray(v, float)
  376. x_plot = x
  377. tick_vals = ticks_from_data(x, gap=gap)
  378. tick_pos = f(tick_vals)
  379. zero_x = 0.0
  381. d_plot["_x_plot"] = x_plot
  383. # plot
  384. sns.set(style="white", rc={"axes.spines.right": False, "axes.spines.top": False})
  385. fig, ax = plt.subplots(figsize=(4, 7))
  387. h = sns.scatterplot(
  388. data=d_plot,
  389. x="_x_plot",
  390. y="Region",
  391. hue="Direction",
  392. palette=palette,
  393. edgecolor="white",
  394. linewidth=0.5,
  395. alpha=0.9,
  396. ax=ax,
  397. legend=False,
  398. )
  399. h.collections[-1].set_sizes(sizes)
  401. ax.axvline(zero_x, color="lightgray", linestyle="-", linewidth=1)
  403. # ticks show true values; positions are transformed
  404. ax.set_xticks(tick_pos)
  405. ax.set_xticklabels([f"{v:.0f}" for v in tick_vals])
  407. # small padding
  408. lo = x_plot.min()
  409. hi = x_plot.max()
  410. ax.set_xlim(lo - 0.05 * (hi - lo + 1e-9),
  411. hi + 0.05 * (hi - lo + 1e-9))
  413. ax.set_xlabel("Relative volume change (%), male − female", fontsize=14)
  414. ax.set_ylabel("Region")
  415. ax.set_title(title, fontsize=16)
  417. # size legend for p values
  418. if show_size_legend:
  419. example_p = np.array([0.05, 0.01, 0.001])
  420. ex_sig = np.minimum(-np.log10(example_p), size_cap_log10) / size_cap_log10
  421. ex_sizes = s_min + ex_sig * (s_max - s_min)
  422. handles = [
  423. plt.scatter([], [], s=s, c="gray", alpha=0.9, edgecolors="none")
  424. for s in ex_sizes
  425. ]
  426. labels = [f"p = {pv:g}" for pv in example_p]
  427. leg = ax.legend(
  428. handles, labels,
  429. title="significance",
  430. loc="upper right",
  431. frameon=False,
  432. labelspacing=0.6,
  433. )
  434. ax.add_artist(leg)
  436. for sp in ax.spines.values():
  437. sp.set_visible(False)
  439. plt.savefig(out_pdf, dpi=300, bbox_inches="tight")
  440. plt.show()
  442. # %%
  443. plot_regions_by_percent_change(
  444. df_jac,
  445. top_n=20, # 10 female>male + 10 male>female
  446. title="Dimorphic regions by volume change",
  447. out_pdf='sdm_ttest.pdf'
  448. )
  450. # %% [markdown]
  451. # Now calculate voxel-wise Cohen's d for the last panel
  453. # %%
  454. import numpy as np
  455. import matplotlib.pyplot as plt
  457. # 1) voxelwise Cohen's d from lists of logJ volumes in the template space
  458. def voxelwise_cohens_d_from_logJ(logjs_male_np, logjs_female_np, eps=1e-12):
  459. M = np.stack(logjs_male_np, axis=0).astype(np.float32) # (nm, X, Y, Z)
  460. F = np.stack(logjs_female_np, axis=0).astype(np.float32) # (nf, X, Y, Z)
  461. nm, nf = M.shape[0], F.shape[0]
  462. mean_m, mean_f = M.mean(0), F.mean(0)
  463. var_m = M.var(0, ddof=1)
  464. var_f = F.var(0, ddof=1)
  465. sp2 = ((nm-1)*var_m + (nf-1)*var_f) / max(1, (nm + nf - 2))
  466. sp = np.sqrt(np.maximum(sp2, eps))
  467. d = (mean_m - mean_f) / sp
  468. return d
  470. # %%
  471. d_vox = voxelwise_cohens_d_from_logJ(logjs_male_np, logjs_female_np)
  472. template_np = template_mixed.numpy()
  474. # %% [markdown]
  475. # Prepare the plot function
  477. # %%
  478. import numpy as np
  479. import matplotlib.pyplot as plt
  480. from matplotlib.colors import LinearSegmentedColormap, Normalize
  481. from mpl_toolkits.axes_grid1 import make_axes_locatable
  484. def hex2rgb(h):
  485. h = h.lstrip("#")
  486. return np.array([int(h[i:i+2], 16) for i in (0, 2, 4)], dtype=float) / 255.0
  489. def project_mip(vol3d, axis=2):
  490. return np.max(np.asarray(vol3d, float), axis=axis)
  493. def show_axial_split_male_female_vertical(
  494. d_vox,
  495. brain_mask=None,
  496. pos_color="#D08770",
  497. neg_color="#5E81AC",
  498. clip_pct=99.0,
  499. crop_to_mask=True,
  500. pad=2,
  501. figsize=(8.0, 8.0),
  502. title_pos="Male > female (|Cohen's d|)",
  503. title_neg="Female > male (|Cohen's d|)",
  504. out_pdf=None,
  505. ):
  506. d = np.asarray(d_vox, float)
  508. if brain_mask is not None:
  509. d = np.where(brain_mask, d, 0.0)
  511. dpos = np.maximum(d, 0.0)
  512. dneg = np.maximum(-d, 0.0)
  514. P = project_mip(dpos, axis=2)
  515. N = project_mip(dneg, axis=2)
  517. if crop_to_mask and brain_mask is not None:
  518. m2d = np.any(brain_mask, axis=2)
  519. ys, xs = np.where(m2d)
  520. if xs.size and ys.size:
  521. x0 = max(xs.min() - pad, 0)
  522. x1 = min(xs.max() + pad, P.shape[1] - 1)
  523. y0 = max(ys.min() - pad, 0)
  524. y1 = min(ys.max() + pad, P.shape[0] - 1)
  525. P = P[y0:y1+1, x0:x1+1]
  526. N = N[y0:y1+1, x0:x1+1]
  528. eps = 1e-12
  529. pmax = np.percentile(P[P > 0], clip_pct) if np.any(P > 0) else 1.0
  530. nmax = np.percentile(N[N > 0], clip_pct) if np.any(N > 0) else 1.0
  532. Pn = np.clip(P / (pmax + eps), 0, 1)
  533. Nn = np.clip(N / (nmax + eps), 0, 1)
  535. pos_cmap = LinearSegmentedColormap.from_list("pos", [(1, 1, 1), hex2rgb(pos_color)])
  536. neg_cmap = LinearSegmentedColormap.from_list("neg", [(1, 1, 1), hex2rgb(neg_color)])
  538. fig, axes = plt.subplots(2, 1, figsize=figsize, dpi=120, constrained_layout=True)
  540. ax_neg = axes[0]
  541. ax_neg.imshow(Nn, origin="lower", cmap=neg_cmap)
  542. ax_neg.set_title(title_neg, fontsize=12)
  543. ax_neg.axis("off")
  545. ax_pos = axes[1]
  546. ax_pos.imshow(Pn, origin="lower", cmap=pos_cmap)
  547. ax_pos.set_title(title_pos, fontsize=12)
  548. ax_pos.axis("off")
  550. for ax, vmax, label, cmap in [
  551. (ax_neg, nmax, r"Max |Cohen's d| along Z (female > male)", neg_cmap),
  552. (ax_pos, pmax, r"Max |Cohen's d| along Z (male > female)", pos_cmap),
  553. ]:
  554. divider = make_axes_locatable(ax)
  555. cax = divider.append_axes("right", size="3%", pad=0.05)
  556. mappable = plt.cm.ScalarMappable(norm=Normalize(0, vmax), cmap=cmap)
  557. cb = fig.colorbar(mappable, cax=cax, orientation="vertical")
  558. cb.set_label(label, rotation=90, labelpad=6)
  559. cb.outline.set_visible(False)
  560. cb.ax.tick_params(length=3, width=0)
  561. cb.ax.set_facecolor("none")
  563. if out_pdf is not None:
  564. plt.savefig(out_pdf, dpi=300, bbox_inches="tight")
  566. plt.show()
  568. # %%
  569. show_axial_split_male_female_vertical(
  570. d_vox,
  571. brain_mask=masks_np != 0,
  572. pos_color="#D08770",
  573. neg_color="#5E81AC",
  574. clip_pct=99.0,
  575. crop_to_mask=True,
  576. pad=2,
  577. out_pdf="cohensd_volume.pdf",
  578. )
  580. # %%

Fig5.ipynb at commit c855337, under CC-BY-NC-SA-4.0 · at the source

Overview

Authors: Mykola Kadobianskyi1, Jörg Henninger1, Daniil Markov1, Antonia Groneberg1, Johannes Veith1, Marc Renz1, Kutay Deniz Atabay2, Peter W. Reddien2, Leonard Maler3, Benjamin Judkewitz1
ORCID iDs: Mykola Kadobianskyi, Benjamin Judkewitz
  1. Charité Universitätsmedizin, Berlin, Germany
  2. Massachusetts Institute of Technology, Cambridge, MA, USA
  3. University of Ottawa, Ottawa, Canada
Institutions: Charité - Universitätsmedizin Berlin (Germany); Massachusetts Institute of Technology (United States); University of Ottawa (Canada)
Journal: bioRxiv (preprint)
Dates: published online 11 March 2026
Type: Preprint
License: CC BY-NC-ND
Identifiers: DOI 10.64898/2026.03.09.710483 · OpenAlex W7135014889
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), methods / tools (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics, Machine learning
Keywords: Brain atlas, Danionella, calcium imaging, in situ hybridization
Topic: Retinal Development and Disorders (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Research Council (101043615)
Citations: cited by 1 paper (Europe PMC); 94 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.

gin.g-node.org/danionella/kadobianskyi_et_al_2026

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: c855337bd33084103fa3410d6f3c1a313fec388b, 16 July 2026
Languages: Jupyter (2)
Size: 27 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ANTs (2 files), Matplotlib (2 files), NumPy (2 files), pandas (2 files), SciPy (2 files), statsmodels (2 files), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: gin.g-node.org/danionella/Kadobianskyi_et_al_2026

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

Tracing map

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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;
  • 2 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

Datasets cited

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/10.64898/2026.03.09.710483.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, journal, dates, 10 authors, 4 keywords, 1 funder, 94 references.

Cite

This paper

Kadobianskyi, M., Henninger, J., Markov, D., Groneberg, A., Veith, J., Renz, M., Atabay, K. D., Reddien, P. W., Maler, L., & Judkewitz, B. (2026). Multimodal reference brain atlas of adult <i>Danionella cerebrum</i>. bioRxiv (preprint). https://doi.org/10.64898/2026.03.09.710483

BibTeX

@article{kadobianskyi2026multimodal,
author = {Kadobianskyi, Mykola and Henninger, Jörg and Markov, Daniil and Groneberg, Antonia and Veith, Johannes and Renz, Marc and Atabay, Kutay Deniz and Reddien, Peter W. and Maler, Leonard and Judkewitz, Benjamin},
title = {{Multimodal reference brain atlas of adult <i>Danionella cerebrum</i>}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.09.710483},
url = {https://doi.org/10.64898/2026.03.09.710483}
}

RIS

TY - JOUR
AU - Kadobianskyi, Mykola
AU - Henninger, Jörg
AU - Markov, Daniil
AU - Groneberg, Antonia
AU - Veith, Johannes
AU - Renz, Marc
AU - Atabay, Kutay Deniz
AU - Reddien, Peter W.
AU - Maler, Leonard
AU - Judkewitz, Benjamin
TI - Multimodal reference brain atlas of adult <i>Danionella cerebrum</i>
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/11
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.09.710483
UR - https://doi.org/10.64898/2026.03.09.710483
ER -

CSL-JSON

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"type": "article",
"title": "Multimodal reference brain atlas of adult <i>Danionella cerebrum</i>",
"container-title": "bioRxiv (preprint)",
"author": [
{
"family": "Kadobianskyi",
"given": "Mykola"
},
{
"family": "Henninger",
"given": "Jörg"
},
{
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"given": "Daniil"
},
{
"family": "Groneberg",
"given": "Antonia"
},
{
"family": "Veith",
"given": "Johannes"
},
{
"family": "Renz",
"given": "Marc"
},
{
"family": "Atabay",
"given": "Kutay Deniz"
},
{
"family": "Reddien",
"given": "Peter W."
},
{
"family": "Maler",
"given": "Leonard"
},
{
"family": "Judkewitz",
"given": "Benjamin"
}
],
"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.09.710483",
"ISSN": "2692-8205",
"publisher": "bioRxiv",
"URL": "https://doi.org/10.64898/2026.03.09.710483",
"issued": {
"date-parts": [
[
2026,
3,
11
]
]
}
}

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