OSCR

Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics.

Code ↔ Paper

18 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 18 matches
  1. [1] § Results › Sensitivity analysis ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/calb1_threshold_analysis.py, lines 96–218 · score 0.74 · error bars, Dice coefficient, VL regions, optimal, Calbindin, parcellations
  2. [2] § Methods › Neuronal placement ↔ thalamic-scaffold.zip/thalamic-scaffold/placement/segment_rt.py, lines 73–112 · score 0.69 · convex hull, volumetric mask, intersection, segmented, Allen, Atlas
  3. [3] § Results › Sensitivity analysis ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/calb1_threshold_analysis.py, lines 96–218 · score 0.62 · parcellation accuracy, Dice coefficient, Calb1, threshold, sensitivity, VL
  4. [4] § Methods › Network connectivity ↔ offline-scripts.zip/update_rtrt_connections.py, lines 212–262 · score 0.61 · electrical connectivity, gap junctions, chemical connections, ellipsoid, RT
  5. [5] § Methods › Network connectivity ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/rtrt_deg_dist_analysis.py, lines 217–261 · score 0.61 · electrical connectivity, gap junctions, chemical connections, ellipsoid, RT
  6. [6] § Results › Network connectivity ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/rt_r_analysis.py, lines 89–168 · score 0.59 · dorsal ventral, lateral medial, Pearson, correlations, scaffold, cells
  7. [7] § Results › Sensitivity analysis ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/rt_beta_dist.py, lines 95–156 · score 0.57 · degree KS, KS distances, rows, optimization, baseline, beta
  8. [8] § Results › Sensitivity analysis ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/rt_r_analysis.py, lines 89–168 · score 0.56 · dorsal ventral, lateral medial, correlations, Sensitivity
  9. [9] § Results ↔ thalamic-scaffold.zip/thalamic-scaffold/connectome/rt_synthetic_axons.py, lines 593–660 · score 0.55 · synthetic axon, algorithmically, tree, Gaussian, volumetric, geometrical
  10. [10] § Methods › Neuronal placement ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/calb1_threshold_analysis.py, lines 26–48 · score 0.54 · VL subdivisions, Calb1, classified, ISH, thresholding, VA
  11. [11] § Results › Neuronal placement ↔ thalamic-scaffold.zip/thalamic-scaffold/placement/segment_rt.py, lines 73–112 · score 0.53 · volumetric mask, VM voxels, intersected, segmentation, Allen, Atlas
  12. [12] § Methods › Network simulations ↔ offline-scripts.zip/update_rtrt_connections.py, lines 136–210 · score 0.52 · post synaptic, Chemical synapses, pre, distance, cell, connection
  13. [13] § Methods › Network simulations ↔ offline-scripts.zip/offline-scripts/sensitivity_analysis/rtrt_deg_dist_analysis.py, lines 142–215 · score 0.52 · post synaptic, Chemical synapses, pre, distance, cell, connection
  14. [14] § Methods › Network connectivity ↔ thalamic-scaffold.zip/thalamic-scaffold/connectome/tc_rt.py, lines 207–296 · score 0.52 · thalamocortical cell, sphere, uniform, collaterals, cylinder, radius
  15. [15] § Results ↔ thalamic-scaffold.zip/thalamic-scaffold/connectome/rt_tc.py, lines 365–466 · score 0.51 · RT TC, arbors, grown, algorithm, space, candidates
  16. [16] § Methods › Network connectivity ↔ thalamic-scaffold.zip/thalamic-scaffold/connectome/tc_rt.py, lines 207–296 · score 0.51 · 1–44, thalamocortical cell, collaterals, beta, connections
  17. [17] § Methods › Network connectivity ↔ thalamic-scaffold.zip/thalamic-scaffold/connectome/rt_tc.py, lines 365–466 · score 0.51 · contact, iteratively, grown, algorithm, Gaussian, filled
  18. [18] § Results › Sensitivity analysis ↔ thalamic-scaffold.zip/thalamic-scaffold/connectome/tc_rt.py, lines 18–31 · score 0.50 · cylinder height, thalamo reticular, collateral

Paper

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The authors' code

Python · 432 lines · 13 KB · CC-BY-4.0 · 3 matches

  1. import nrrd
  2. import numpy as np
  3. import pickle
  4. import matplotlib.pyplot as plt
  5. import pandas as pd
  6. import seaborn as sns
  8. sns.set_theme(style="white")
  10. # Allen 25um dimensions
  11. # [528, 320, 456]
  12. M = 456
  13. N = 320
  15. # VAL volume starts from 240th voxel in x-direction in CCFv3.0
  16. MIN_X = 240
  18. with open("data/atlas/val_25_voxels.pickle", "rb") as file:
  19. VAL_VOXELS = pickle.load(file)
  20. FLIPPING = True
  22. VA_color = "#BFE052"
  23. VL_color = "#489339"
  26. def classify_voxels(ish_data, voxel_set=VAL_VOXELS, threshold=0.3):
  27. """
  28. Fuzzy classification of VAL subnuclei based on genetic information
  29. """
  30. # Define Calb1 with highest density voxels as secondary-type voxels
  31. is_secondary = ish_data > threshold * np.max(ish_data)
  32. secondary_voxels = np.transpose(np.where(is_secondary))
  33. # Remove the voxels that have been labelled as secondary
  34. secondary_voxel_set = set(map(tuple, secondary_voxels))
  35. # Classify remaining ones as primary
  36. primary_voxels = np.array(list(voxel_set - secondary_voxel_set))
  38. if FLIPPING:
  39. # Use the right hemisphere outcomes for the thalamic-scaffold (modelled as the left hemisphere).
  40. # The hemispheres appear to have different (asymmetrical) expressions, which is indeed weird.
  41. # Nonetheless, the right hemisphere is more coherent with the prediction and the expected VA-VL subdivison
  42. primary_voxels[:, 2] = 228 + (228 - primary_voxels[:, 2])
  43. secondary_voxels[:, 2] = 228 + (228 - secondary_voxels[:, 2])
  45. return (
  46. primary_voxels[primary_voxels[:, 2] < 228],
  47. secondary_voxels[secondary_voxels[:, 2] < 228],
  48. )
  51. def extract_2D_matrices(voxels, ap_indices, resolution=25, validation_map=None):
  52. """
  53. Converts a list of indices of a 3D array to a list of 2D boolean matrices
  54. """
  55. matrices = []
  56. index_type = 1
  58. if resolution == 10:
  59. index_type = 0
  61. for ap_id in ap_indices:
  62. if resolution == 10 and validation_map is not None:
  63. slice_id = validation_map[ap_id[index_type]]
  64. slice = voxels[voxels[:, 0] == slice_id][:, [1, 2]]
  65. slice = (slice * (10 / 25)).astype(int)
  66. else:
  67. slice = voxels[voxels[:, 0] == ap_id[index_type]][:, [1, 2]]
  68. matrix = np.zeros((N, M), dtype=bool)
  69. if len(slice > 0):
  70. matrix[tuple(zip(*slice))] = True
  71. matrices.append(matrix)
  73. return matrices
  76. def compute_dice_index(segmented_mask, validation_mask):
  77. """
  78. This function computes the Dice coefficient, defined ad 2|A∩B|/(|A|+|B|),
  79. between the segmented and validation masks.
  80. """
  81. # Calculate intersection and individual areas
  82. intersection = np.logical_and(segmented_mask, validation_mask).sum()
  83. a = segmented_mask.sum()
  84. b = validation_mask.sum()
  86. # Handle edge case where both masks are empty
  87. if a + b == 0:
  88. return 0.0 # Empty masks should not contribute positively
  90. # Calculate Dice coefficient
  91. dice = (2.0 * intersection) / (a + b)
  93. return dice
  96. def plot_threshold_sensitivity_summary(dice_data):
  97. # Restructure data for seaborn
  98. plot_data = []
  99. for threshold, regions in dice_data.items():
  100. # Add VA-VM data points
  101. for score in regions["va-vm"]:
  102. plot_data.append(
  103. {
  104. "threshold": threshold,
  105. "dice_coefficient": score,
  106. "region": "VA-VM",
  107. }
  108. )
  110. # Add VL data points
  111. for score in regions["vl"]:
  112. plot_data.append(
  113. {
  114. "threshold": threshold,
  115. "dice_coefficient": score,
  116. "region": "VL",
  117. }
  118. )
  120. df = pd.DataFrame(plot_data)
  122. # Calculate means and stds for error bars
  123. thresholds = list(dice_data.keys())
  124. va_vm_means = [np.mean(dice_data[t]["va-vm"]) for t in thresholds]
  125. va_vm_stds = [np.std(dice_data[t]["va-vm"]) for t in thresholds]
  126. vl_means = [np.mean(dice_data[t]["vl"]) for t in thresholds]
  127. vl_stds = [np.std(dice_data[t]["vl"]) for t in thresholds]
  129. # Create the plot
  130. fig, ax = plt.subplots(1, 2, figsize=(12, 5))
  132. # VA-VM region - bands first
  133. sns.lineplot(
  134. data=df[df["region"] == "VA-VM"],
  135. x="threshold",
  136. y="dice_coefficient",
  137. color=VA_color,
  138. err_style="band",
  139. errorbar="sd",
  140. alpha=0.7,
  141. ax=ax[0],
  142. )
  144. # Add error bars manually
  145. ax[0].errorbar(
  146. thresholds,
  147. va_vm_means,
  148. yerr=va_vm_stds,
  149. marker="o",
  150. linewidth=1,
  151. markersize=5,
  152. capsize=2,
  153. capthick=1,
  154. color=VA_color,
  155. label="VA-VM",
  156. )
  158. ax[0].set_xlabel("Calbindin Threshold")
  159. ax[0].set_ylabel("Dice Coefficient")
  160. ax[0].set_title("VA-VM Region Parcellation Accuracy")
  161. ax[0].set_ylim(0, 1)
  163. # Find and plot optimal threshold for VA-VM
  164. optimal_idx_va = np.argmax(va_vm_means)
  165. optimal_threshold_va = thresholds[optimal_idx_va]
  166. ax[0].axvline(
  167. optimal_threshold_va,
  168. color="red",
  169. linestyle="--",
  170. alpha=0.7,
  171. label=f"Optimal: {optimal_threshold_va}",
  172. )
  174. # VL region - bands first
  175. sns.lineplot(
  176. data=df[df["region"] == "VL"],
  177. x="threshold",
  178. y="dice_coefficient",
  179. color=VL_color,
  180. err_style="band",
  181. errorbar="sd",
  182. alpha=0.7,
  183. ax=ax[1],
  184. )
  186. # Add error bars manually
  187. ax[1].errorbar(
  188. thresholds,
  189. vl_means,
  190. yerr=vl_stds,
  191. marker="s",
  192. linewidth=1,
  193. markersize=5,
  194. capsize=2,
  195. capthick=1,
  196. color=VL_color,
  197. label="VL",
  198. )
  200. ax[1].set_xlabel("Calbindin Threshold")
  201. ax[1].set_ylabel("Dice Coefficient")
  202. ax[1].set_title("VL Region Parcellation Accuracy")
  203. ax[1].set_ylim(0, 1)
  205. # Find and plot optimal threshold for VL
  206. optimal_idx_vl = np.argmax(vl_means)
  207. optimal_threshold_vl = thresholds[optimal_idx_vl]
  208. ax[1].axvline(
  209. optimal_threshold_vl,
  210. color="red",
  211. linestyle="--",
  212. alpha=0.7,
  213. label=f"Optimal: {optimal_threshold_vl}",
  214. )
  216. plt.tight_layout()
  217. sns.despine(offset=5, trim=False)
  218. plt.show()
  221. def plot_threshold_heatmap(dice_data):
  222. """
  223. Heatmap showing Dice coefficients across thresholds and slices
  224. """
  226. thresholds = list(dice_data.keys())
  227. n_slices = len(dice_data[thresholds[0]]["va-vm"])
  229. # Prepare data matrices
  230. va_vm_matrix = np.array([dice_data[t]["va-vm"] for t in thresholds]).T
  231. vl_matrix = np.array([dice_data[t]["vl"] for t in thresholds]).T
  233. # Create subplots
  234. fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
  236. # VA-VM heatmap
  237. im1 = ax1.imshow(
  238. va_vm_matrix, aspect="auto", cmap="viridis", vmin=0, vmax=1
  239. )
  240. ax1.set_xlabel("Threshold")
  241. ax1.set_ylabel("Slice number")
  242. ax1.set_xticks(range(len(thresholds)))
  243. ax1.set_xticklabels([f"{t:.2f}" for t in thresholds])
  244. ax1.tick_params("x", rotation=45)
  245. ax1.set_title("VA-VM Dice Coefficients")
  246. plt.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04)
  248. # VL heatmap
  249. im2 = ax2.imshow(vl_matrix, aspect="auto", cmap="viridis", vmin=0, vmax=1)
  250. ax2.set_xlabel("Threshold")
  251. ax2.set_ylabel("Slice number")
  252. ax2.set_xticks(range(len(thresholds)))
  253. ax2.set_xticklabels([f"{t:.2f}" for t in thresholds])
  254. ax2.tick_params("x", rotation=45)
  255. ax2.set_title("VL Dice Coefficients")
  256. plt.colorbar(im2, ax=ax2, fraction=0.046, pad=0.04)
  258. plt.tight_layout()
  259. plt.show()
  262. def sensitivity_analysis(dice_data):
  263. thresholds = list(dice_data.keys())
  264. baseline_threshold = 0.3 # Your current threshold
  266. results = {}
  268. for region in ["va-vm", "vl"]:
  269. region_results = {}
  271. # Calculate statistics for each threshold
  272. means = []
  273. stds = []
  275. for threshold in thresholds:
  276. scores = dice_data[threshold][region]
  277. means.append(np.mean(scores))
  278. stds.append(np.std(scores))
  280. # Find optimal threshold
  281. optimal_idx = np.argmax(means)
  282. optimal_threshold = thresholds[optimal_idx]
  283. optimal_score = means[optimal_idx]
  285. # Calculate baseline performance
  286. baseline_idx = thresholds.index(baseline_threshold)
  287. baseline_score = means[baseline_idx]
  289. # Calculate sensitivity metrics
  290. score_range = max(means) - min(means)
  291. coefficient_of_variation = np.std(means) / np.mean(means)
  293. # Improvement over baseline
  294. improvement = ((optimal_score - baseline_score) / baseline_score) * 100
  296. region_results = {
  297. "optimal_threshold": optimal_threshold,
  298. "optimal_score": optimal_score,
  299. "baseline_score": baseline_score,
  300. "improvement_percent": improvement,
  301. "score_range": score_range,
  302. "coefficient_of_variation": coefficient_of_variation,
  303. "all_means": means,
  304. "all_stds": stds,
  305. }
  307. results[region] = region_results
  309. # Print manuscript-ready summary
  310. print("=== SENSITIVITY ANALYSIS SUMMARY ===")
  311. print(f"Baseline threshold: {baseline_threshold}")
  312. print()
  314. for region in ["va-vm", "vl"]:
  315. r = results[region]
  316. print(f"{region.upper()} Region:")
  317. print(f" Optimal threshold: {r['optimal_threshold']}")
  318. print(f" Optimal Dice: {r['optimal_score']:.3f}")
  319. print(f" Baseline Dice: {r['baseline_score']:.3f}")
  320. print(f" Improvement: {r['improvement_percent']:+.1f}%")
  321. print(f" Sensitivity (CV): {r['coefficient_of_variation']:.3f}")
  322. print(f" Score range: {r['score_range']:.3f}")
  323. print()
  325. return results
  328. # Sanity check with plots
  329. def save_2d_slice_figures(
  330. coronal_va_segmentation,
  331. coronal_va_vm_validation,
  332. coronal_vm,
  333. coronal_vl_segmentation,
  334. coronal_vl_validation,
  335. threshold,
  336. ):
  337. fig, ax = plt.subplots(nrows=2, ncols=9, figsize=(15, 6))
  338. for i in range(9):
  339. ax[0, i].matshow(
  340. np.logical_or(coronal_va_segmentation[i], coronal_vm[i])
  341. )
  342. ax[0, i].matshow(coronal_va_vm_validation[i], alpha=0.15, cmap="Blues")
  343. ax[0, i].xaxis.set_inverted(True)
  344. ax[0, i].axes.get_xaxis().set_ticks([])
  346. ax[1, i].matshow(coronal_vl_segmentation[i])
  347. ax[1, i].matshow(coronal_vl_validation[i], alpha=0.15, cmap="Blues")
  348. ax[1, i].xaxis.set_inverted(True)
  349. ax[1, i].axes.get_yaxis().set_ticks([])
  351. plt.savefig(f"offline-scripts/calb1_slices/{threshold}.png")
  352. plt.tight_layout()
  353. # plt.show()
  356. # Load halved VM voxels (VM is included in Carmen's data)
  357. vm = nrrd.read("data/atlas/halved_vm.nrrd")[0]
  358. vm_voxels = np.vstack(np.where(vm > 0)).T
  360. # Load Carmen's validation data
  361. dict_file = "data/validation/reg_output.pickle"
  362. with open(dict_file, "rb") as f:
  363. d = pickle.load(f)
  365. # Ensure the slices are in the same hemisphere
  366. va_vm = d["VA-VM"]
  367. va_vm[va_vm[:, 2] > 570, 2] = 570 + (570 - va_vm[va_vm[:, 2] > 570, 2])
  368. vl = d["VL"]
  369. vl[vl[:, 2] > 570, 2] = 570 + (570 - vl[vl[:, 2] > 570, 2])
  372. # Extract the anterior-posterior position of the slices
  373. ap_validation_ids = np.unique(va_vm[:, 0])
  374. # Get the overlapping indices between Carmen's data and VAL mesh
  375. overlapping_ids = [
  376. (slice_num, x_id)
  377. for slice_num, x_id in enumerate(
  378. (ap_validation_ids * (10 / 25)).astype(int)
  379. )
  380. if x_id > MIN_X
  381. ]
  382. # Drop the last ID (it does not contain valid Allen data)
  383. overlapping_ids = overlapping_ids[:-1]
  385. # Prepare the data for Dice coefficient computation
  386. coronal_vm = extract_2D_matrices(vm_voxels, overlapping_ids)
  388. coronal_va_vm_validation = extract_2D_matrices(
  389. va_vm, overlapping_ids, 10, ap_validation_ids
  390. )
  391. coronal_vl_validation = extract_2D_matrices(
  392. vl, overlapping_ids, 10, ap_validation_ids
  393. )
  395. # Load raw Calb1 data in VAL
  396. upsampled_calb1_data = np.load("data/ish/upsampled_calb1_val.npy")
  399. # Use different threshold values iteratively, collect Dice coeffs for each
  400. threshold_dice = {}
  401. for threshold in np.linspace(0, 1, 21)[1:]:
  402. threshold = np.around(threshold, 2)
  403. print(f"Using threshold: {threshold}")
  404. # Primary type = VL, secondary type = VA
  405. pv, sv = classify_voxels(upsampled_calb1_data, threshold=threshold)
  407. coronal_va_segmentation = extract_2D_matrices(sv, overlapping_ids)
  408. coronal_vl_segmentation = extract_2D_matrices(pv, overlapping_ids)
  410. # Collect Dice coefficient across slices
  411. dice_coeffs = {"va-vm": [], "vl": []}
  412. for i in range(len(coronal_va_segmentation)):
  413. # Note that VA and VM need to be merged from Allen
  414. va_vm_dice = compute_dice_index(
  415. np.logical_or(coronal_va_segmentation[i], coronal_vm[i]),
  416. coronal_va_vm_validation[i],
  417. )
  418. dice_coeffs["va-vm"].append(va_vm_dice)
  419. vl_dice = compute_dice_index(
  420. coronal_vl_segmentation[i], coronal_vl_validation[i]
  421. )
  422. dice_coeffs["vl"].append(vl_dice)
  423. threshold_dice[threshold] = dice_coeffs
  425. plot_threshold_sensitivity_summary(threshold_dice)
  426. plot_threshold_heatmap(threshold_dice)
  428. summary_stats = sensitivity_analysis(threshold_dice)
  431. # From DOI: 10.1016/S1076-6332(03)00671-8
  432. # Good overlap is when Dice >0.7

calb1_threshold_analysis.py, under CC-BY-4.0 · at the source

Overview

Authors: Francesco Jamal Sheiban1, Alberto Antonietti1, Muhammed Furkan Beyazyüz1, Robin De Schepper2, Carmen Alonso-Martínez3, Mario Rubio-Teves3, Francisco Clascá3, Egidio D’Angelo2, Alessandra Pedrocchi1
ORCID iDs: Francesco Jamal Sheiban, Alberto Antonietti, Muhammed Furkan Beyazyüz, Robin De Schepper, Mario Rubio-Teves, Francisco Clascá, Alessandra Pedrocchi
  1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
  2. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
  3. Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, Madrid, Spain
Institutions: Politecnico di Milano (Italy); Fondazione Politecnico di Milano (Italy); University of Pavia (Italy); Universidad Autónoma de Madrid (Spain)
Journal: Communications biology, volume 9, issue 1, article 836
Dates: received 16 April 2025; accepted 30 March 2026; published online 18 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10032-2 · PMID 42000901 · PMCID PMC13275909 · OpenAlex W7154827381
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), mouse (organism), computational (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Graphs, Single-unit activity, calcium imaging
Keywords: Network models, Computational models
MeSH: Models, Neurological*, Thalamus*, Animals, Computer Simulation, Mice (* major topic)
Topic: Neural Networks and Reservoir Computing (Artificial Intelligence, Computer Science), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 74 references in the paper

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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.

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Zenodo 15198487

License: CC-BY-4.0
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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 5 MeSH terms, 61 references.

Cite

This paper

Sheiban, F. J., Antonietti, A., Beyazyüz, M. F., De Schepper, R., Alonso-Martínez, C., Rubio-Teves, M., Clascá, F., D’Angelo, E., & Pedrocchi, A. (2026). Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics. Communications biology, 9(1), 836. https://doi.org/10.1038/s42003-026-10032-2

BibTeX

@article{sheiban2026data,
author = {Sheiban, Francesco Jamal and Antonietti, Alberto and Beyazyüz, Muhammed Furkan and De Schepper, Robin and Alonso-Martínez, Carmen and Rubio-Teves, Mario and Clascá, Francisco and D’Angelo, Egidio and Pedrocchi, Alessandra},
title = {{Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {836},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10032-2},
url = {https://doi.org/10.1038/s42003-026-10032-2},
pmid = {42000901},
pmcid = {PMC13275909}
}

RIS

TY - JOUR
AU - Sheiban, Francesco Jamal
AU - Antonietti, Alberto
AU - Beyazyüz, Muhammed Furkan
AU - De Schepper, Robin
AU - Alonso-Martínez, Carmen
AU - Rubio-Teves, Mario
AU - Clascá, Francisco
AU - D’Angelo, Egidio
AU - Pedrocchi, Alessandra
TI - Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/18
VL - 9
IS - 1
SP - 836
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10032-2
UR - https://doi.org/10.1038/s42003-026-10032-2
LA - en
ER -

CSL-JSON

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"DOI": "10.1038/s42003-026-10032-2",
"PMID": "42000901",
"PMCID": "PMC13275909",
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"language": "en",
"issued": {
"date-parts": [
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