OSCR

Genoarchitecture and input-output organization of the mouse basal ganglia and thalamic parafascicular nucleus.

Code ↔ Paper

17 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 17 matches
  1. [1] § Methods › ScRNA-seq of the basal ganglia and thalamic PF nucleus ↔ scripts/make_fineres_hist_ai21_cpq.py, lines 76–122 · score 1.00 · LDT PCG CS, Gata3 Lhx1 Gaba, NDB SI MA, STRv Lhx8 Gaba, ZI Pax6 Gaba, STN PSTN Pitx2
  2. [2] § Results › Diverse spatial patterns of t-types in the basal ganglia and PF nucleus ↔ scripts/make_fineres_hist_ai21_cpq.py, lines 76–122 · score 1.00 · NDB SI MA, STRv Lhx8 Gaba, ZI Pax6 Gaba, STN PSTN Pitx2, GPi Tbr1 Cngb3, GPe SI Sox6
  3. [3] § Results › Delineation of subdivisions of the basal ganglia and PF nucleus ↔ scripts/Fig2.py, lines 19–49 · score 0.76 · CPiv, CPvm, CPdm, Astn2, Calb2, Fos
  4. [4] § Results › Diverse spatial patterns of t-types in the basal ganglia and PF nucleus ↔ scripts/make_fineres_hist_ai21_cpq.py, lines 174–261 · score 0.70 · D1 Sema5a Gaba, D2 Gaba, subclasses, MERFISH, CP
  5. [5] § Results › Diverse spatial patterns of t-types in the basal ganglia and PF nucleus ↔ scripts/spatial_pattern_figures.ipynb, lines 356–368 · score 0.69 · patchy lateral, anterior medial, anterior lateral, central medial, CP
  6. [6] § Methods › Quantification of anterograde projections ↔ scripts/Fig3b_projection_summary_stats_multicore.py, lines 79–132 · score 0.64 · image volumes, anterograde projections, Projection density, hemisphere, contralateral, ipsilateral
  7. [7] § Methods › Quantification of single-neuron projections ↔ scripts/swc_plot_terminals.ipynb, lines 24–102 · score 0.62 · leaf nodes, graph tree, SWC, terminals, neuron
  8. [8] § Methods › Curation of fully reconstructed single neurons ↔ scripts/_old/swc_plot_projection_bymodule.py, lines 64–68 · score 0.61 · ORBvl, ORBm, ORBl, ACAv, ILA, ACAd
  9. [9] § Results › Delineation of subdivisions of the basal ganglia and PF nucleus ↔ scripts/_old/plot_subcortical_overlap_heatmap.py, lines 97–129 · score 0.59 · Dice coefficients, CPiv, CPdm, injection site, overlap, CPp
  10. [10] § Methods › Flatmap of the isocortex ↔ scripts/Fig3d_cortex_flatmap.py, lines 1–13 · score 0.59 · anterograde injections, soma locations, flatmap, cortex, retrogradely, cortical
  11. [11] § Methods › Quantification of anterograde projections ↔ scripts/Fig3g_anterograde_overlap.py, lines 122–141 · score 0.58 · injected hemisphere, anterograde projections, quantify, masked, volumes, injections
  12. [12] § Results › Cortical cell-type-specific, single-neuron projections in the subdivisions of the basal ganglia ↔ scripts/Fig2.py, lines 19–49 · score 0.55 · CPiv, CPvm, CPdm, Aggregated, flipped, CPp
  13. [13] § Methods › MERFISH data and its registration to CCFv3 ↔ scripts/spatial_pattern_figures.ipynb, lines 356–368 · score 0.53 · anterior lateral, central medial, patchy, CP
  14. [14] § Results › Cortical and subcortical module-specific and cell-type-specific projections in the basal ganglia and PF nucleus ↔ scripts/Fig5_plot_retrograde.py, lines 198–220 · score 0.52 · CPiv, CPvm, CPdm, ipsilateral, CPp, CPl
  15. [15] § Results › Delineation of subdivisions of the basal ganglia and PF nucleus ↔ scripts/Fig4b_swc_projection_densities.py, lines 1–37 · score 0.52 · CPiv, CPvm, CPdm, density, layers, CPp
  16. [16] § Results › Whole-brain presynaptic inputs to different neuronal types and subdivisions of the CP ↔ scripts/Fig4_plot_swc_heatmap.py, lines 38–59 · score 0.52 · CPiv, CPvm, CPdm, Contralateral, ipsilateral, CPp
  17. [17] § Results › Whole-brain presynaptic inputs to different neuronal types and subdivisions of the CP ↔ scripts/Fig5_plot_retrograde.py, lines 198–220 · score 0.52 · CPiv, CPvm, CPdm, Contralateral, ipsilateral, CPp

Paper

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The authors' code

Python · 267 lines · 8.4 KB · BSD-2-Clause · 3 matches

  1. import h5py
  2. import os
  3. import numpy as np
  4. import pandas as pd
  5. import argschema as ags
  8. class MakeHistogramsForNmfParameters(ags.ArgSchema):
  9. structure = ags.fields.String()
  10. brain = ags.fields.String()
  11. normalize_option = ags.fields.String(default="max")
  12. output_file = ags.fields.OutputFile()
  15. MERFISH_CPQ = {
  16. "609882": "/path/to/mouse_609882/cirro_folder/atlas_brain_609882_AIT21.0_mouse.cpq/",
  17. "609889": "/path/to/mouse_609889/cirro_folder/atlas_brain_609889_AIT21.0_mouse.cpq/",
  18. "638850": "/path/to/mouse_638850/cirro_folder/atlas_brain_609889_AIT21.0_mouse.cpq/",
  19. }
  21. BRAIN_GRIDS = {
  22. "609882": {
  23. "x_borders": np.array([-100000, -89000, -77500, -66500, -56000, -44500, -33500, -22500, -11500, -500, 10500, 21500]),
  24. "y_borders": np.array([50000, 60000, 68000, 75500, 84000, 92000, 99750, 108000]),
  25. },
  26. "609889": {
  27. "x_borders": np.array([-100000, -89000, -77500, -66500, -56000, -44500, -33500, -22500, -11500, -500, 10500, 21500]),
  28. "y_borders": np.array([50000, 60000, 68000, 75500, 84000, 92000, 99750, 108000]),
  29. },
  30. "638850": {
  31. "x_borders": np.array([-100000, -89000, -77500, -66500, -56000, -44500, -33500, -22500, -11500, -500, 10500, 21500]) + 99000,
  32. "y_borders": np.array([50000, 60000, 68000, 75500, 84000, 92000, 99750, 108000]) - 48000,
  33. }
  34. }
  36. BIN_STEP = {
  37. "str-msn": 100,
  38. "str-int": 100,
  39. "gpe": 100,
  40. "gpi": 100,
  41. "stn": 50,
  42. "snr": 100,
  43. "pf": 50,
  44. "cp": 100,
  45. "vstr_ot": 100,
  46. }
  48. MIN_N_PER_TTYPE = {
  49. "str-msn": 1000,
  50. "str-int": 1000,
  51. "gpe": 50,
  52. "gpi": 50,
  53. "stn": 40,
  54. "snr": 40,
  55. "pf": 40,
  56. "cp": 50,
  57. "vstr_ot": 50,
  58. }
  60. STRUCT_BY_TYPES = {
  61. "cp": [
  62. "0990 STR D1 Sema5a Gaba_1",
  63. "0991 STR D1 Sema5a Gaba_1",
  65. "0950 STR D1 Gaba_3",
  66. "0951 STR D1 Gaba_3",
  67. "0952 STR D1 Gaba_3",
  68. "0953 STR D1 Gaba_4",
  69. "0954 STR D1 Gaba_4",
  70. "0955 STR D1 Gaba_5",
  71. "0956 STR D1 Gaba_5",
  72. "0957 STR D1 Gaba_5",
  73. "0960 STR D1 Gaba_7",
  74. "0961 STR D1 Gaba_8",
  76. "0966 STR D2 Gaba_1",
  77. "0968 STR D2 Gaba_1",
  78. "0970 STR D2 Gaba_1",
  79. "0971 STR D2 Gaba_1",
  80. "0972 STR D2 Gaba_2",
  81. "0979 STR D2 Gaba_4",
  82. "0980 STR D2 Gaba_4",
  83. "0981 STR D2 Gaba_4",
  84. "0982 STR D2 Gaba_4",
  85. "0983 STR D2 Gaba_5",
  86. "0987 STR D2 Gaba_6",
  87. "0988 STR D2 Gaba_6",
  88. ]
  89. }
  92. STRUCT_SUBCLASSES = {
  93. "str-msn": [
  94. 'OT D3 Folh1 Gaba',
  95. 'STR D1 Gaba',
  96. 'STR D2 Gaba',
  97. 'STR D1 Sema5a Gaba',
  98. 'STR-PAL Chst9 Gaba',
  99. ],
  100. "str-int": [
  101. ],
  102. "gpe": [
  103. 'NDB-SI-MA-STRv Lhx8 Gaba',
  104. 'GPe-SI Sox6 Cyp26b1 Gaba',
  105. 'PAL-STR Gaba-Chol'
  106. ],
  107. "gpi": [
  108. 'GPi Tbr1 Cngb3 Gaba-Glut',
  109. 'ZI Pax6 Gaba',
  110. ],
  111. "stn": [
  112. 'STN-PSTN Pitx2 Glut',
  113. ],
  114. "snr": [
  115. 'SNr-VTA Pax5 Npas1 Gaba',
  116. 'SNr Six3 Gaba',
  117. 'LDT-PCG-CS Gata3 Lhx1 Gaba',
  118. ],
  119. "pf": [
  120. 'PF Fzd5 Glut',
  121. ]
  122. }
  124. def find_tight_borders(spatial_xy, x_borders, y_borders,
  125. tightness_factor=0.2, min_slice_n=250):
  126. tight_borders = []
  127. select_x = spatial_xy[:, 0]
  128. select_y = spatial_xy[:, 1]
  130. counter = 0
  131. for xleft, xright in zip(x_borders[:-1], x_borders[1:]):
  132. for ybottom, ytop in zip(y_borders[:-1], y_borders[1:]):
  133. in_slice_mask = ((select_x >= xleft) & (select_x < xright) & (select_y >= ybottom) & (select_y < ytop))
  134. ncells = np.sum(in_slice_mask)
  135. if ncells >= min_slice_n:
  136. tight_borders.append((
  137. np.percentile(select_x[in_slice_mask], tightness_factor),
  138. np.percentile(select_x[in_slice_mask], 100 - tightness_factor),
  139. np.percentile(select_y[in_slice_mask], tightness_factor),
  140. np.percentile(select_y[in_slice_mask], 100 - tightness_factor),
  141. ))
  142. counter +=1
  143. return tight_borders
  146. def create_histograms(spatial_xy, tight_borders, bin_step, types_in_list,
  147. cluster_labels):
  148. hist_by_type = {}
  150. for ttype in types_in_list:
  151. ttype_mask = cluster_labels.value == ttype
  153. hist_list = []
  154. for tb in tight_borders:
  155. x_edges = np.arange(tb[0], tb[1] + bin_step, bin_step)
  156. y_edges = np.arange(tb[2], tb[3] + bin_step, bin_step)
  158. hist = np.histogram2d(spatial_xy[ttype_mask, 0], spatial_xy[ttype_mask, 1],
  159. bins=(x_edges, y_edges),
  160. )
  161. my_hist = hist[0]
  162. hist_list.append(my_hist)
  163. hist_concat = np.hstack([h.flatten() for h in hist_list])
  165. # # Save to a dictionary using the t-type names as keys
  166. hist_by_type[ttype] = hist_concat
  168. return hist_by_type
  170. def bin_coordinates(spatial_xy, ccf_xyz, tight_borders, bin_step):
  171. pass
  174. def main(args):
  175. struct_key = args['structure']
  176. brain_name = args['brain']
  178. print(f"Creating histograms for structure {struct_key} with brain {brain_name}")
  179. data_dir = MERFISH_CPQ[brain_name]
  180. cluster_labels = pd.read_parquet(os.path.join(data_dir, "obs/cluster_label.parquet"))
  181. subclass_labels = pd.read_parquet(os.path.join(data_dir, "obs/subclass_label.parquet"))
  183. # Find the cells in the desired subclasses
  184. if struct_key in STRUCT_BY_TYPES:
  185. types_in_list = STRUCT_BY_TYPES[struct_key]
  186. mask = cluster_labels.value.isin(types_in_list)
  187. elif struct_key in STRUCT_SUBCLASSES:
  188. subclass_list = STRUCT_SUBCLASSES[struct_key]
  189. mask = subclass_labels.value.isin(subclass_list)
  190. types_in_list = np.unique(cluster_labels.loc[mask, "value"])
  191. elif struct_key == "vstr_ot":
  192. subclass_list = [
  193. 'OT D3 Folh1 Gaba',
  194. 'STR D1 Gaba',
  195. 'STR D2 Gaba',
  196. 'STR D1 Sema5a Gaba',
  197. ]
  198. mask = subclass_labels.value.isin(subclass_list)
  199. types_in_list = np.unique(cluster_labels.loc[mask, "value"])
  200. types_in_list = types_in_list[~np.in1d(types_in_list, STRUCT_BY_TYPES["cp"])]
  201. mask = cluster_labels.value.isin(types_in_list)
  202. print("Number of selected t-types:", len(types_in_list))
  203. print("Number of selected cells in this brain:", mask.sum())
  205. print("Finding ROIs")
  206. grid_info = BRAIN_GRIDS[brain_name]
  207. x_borders = grid_info['x_borders']
  208. y_borders = grid_info['y_borders']
  210. spatial_xy = pd.read_parquet(os.path.join(data_dir, "obsm/spatial_cirro.parquet"))
  211. tight_borders = find_tight_borders(spatial_xy.loc[mask, :].values, x_borders, y_borders)
  213. print(f"Identified {len(tight_borders)} ROIs")
  215. print("Creating histograms")
  216. bin_step = BIN_STEP[struct_key]
  217. hist_by_type = create_histograms(spatial_xy.values, tight_borders, bin_step,
  218. types_in_list, cluster_labels)
  221. print("Normalizing histograms")
  222. ttype_counts = np.array([h.sum() for h in hist_by_type.values()])
  223. ttype_order = list(hist_by_type.keys())
  225. min_n = MIN_N_PER_TTYPE[struct_key]
  226. n_with_min_cells = (ttype_counts >= min_n).sum()
  227. print(f"{n_with_min_cells} types have at least {min_n} cells")
  229. hists_as_1d_arr = np.zeros((
  230. n_with_min_cells,
  231. hist_by_type[list(hist_by_type.keys())[0]].size))
  232. ttype_order_min_n = np.array(ttype_order)[ttype_counts >= min_n]
  234. normalize_option = args['normalize_option']
  236. for i, t in enumerate(ttype_order_min_n):
  237. print(t)
  238. if normalize_option == 'sum':
  239. hists_as_1d_arr[i, :] = hist_by_type[t] / hist_by_type[t].sum()
  240. elif normalize_option == 'max':
  241. hists_as_1d_arr[i, :] = hist_by_type[t] / hist_by_type[t].max()
  242. elif normalize_option == 'none':
  243. hists_as_1d_arr[i, :] = hist_by_type[t]
  245. print("Saving results")
  246. X_input = np.asfortranarray(hists_as_1d_arr.T)
  248. data_f = h5py.File(args['output_file'], "w")
  249. dset = data_f.create_dataset("data", data=X_input)
  250. dset.attrs['min_n'] = min_n
  252. tight_borders_arr = np.array(tight_borders)
  254. tb_dset = data_f.create_dataset("tight_borders", data=tight_borders_arr)
  255. tb_dset.attrs['bin_step'] = bin_step
  257. dt = h5py.special_dtype(vlen=str)
  258. ttype_names = np.array(ttype_order_min_n, dtype=dt)
  259. ttypes_dset = data_f.create_dataset("ttypes", data=ttype_names)
  261. data_f.close()
  264. if __name__ == "__main__":
  265. module = ags.ArgSchemaParser(schema_type=MakeHistogramsForNmfParameters)
  266. main(module.args)

make_fineres_hist_ai21_cpq.py at commit ebc2d9b, under BSD-2-Clause · at the source

Overview

Authors: Quanxin Wang1, Ashwin Bhandiwad1, Nathan W. Gouwens1, Shenqin Yao1, Yun Wang1, Xiuli Kuang2, Anan Li3, Xiangning Li4, Rachel Dalley1, Hsien-Chi Kuo1, Phil Lesnar1, Wenjie Xu1, Matt Mallory1, Yaoyao Li2, Laila El-Hifnawi1, Leila Ahmadinia1, Ben Ouellette1, Lauren Kruse1, Lydia Ng1, Hui Gong3
and 6 other authorsQingming Luo4, Michael Kunst1, Cindy T. J. van Velthoven1, Zizhen Yao1, Staci A. Sorensen1, Hongkui Zeng1
ORCID iDs: Quanxin Wang, Ashwin Bhandiwad, Nathan W. Gouwens, Shenqin Yao, Xiangning Li, Rachel Dalley, Ben Ouellette, Lauren Kruse, Hui Gong, Qingming Luo, Michael Kunst, Cindy T. J. van Velthoven, Zizhen Yao, Staci A. Sorensen, Hongkui Zeng
  1. Allen Institute for Brain Science,Seattle, WA USA
  2. School of Optometry and Ophthalmology, Wenzhou Medical University,Wenzhou, China
  3. HUST-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute,Suzhou, China
  4. State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University,Haikou, China
Institutions: Allen Institute for Brain Science (United States); Wenzhou Medical University (China); Jiangsu Industry Technology Research Institute (China); Hainan University (China)
Journal: Nature neuroscience, volume 29, issue 5, pages 1248-1264
Dates: received 16 August 2025; accepted 2 March 2026; published online 28 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02253-9 · PMID 42049879 · PMCID PMC13156036 · OpenAlex W7156799368
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Smoothing, state filtering, decompositions, Machine learning
Keywords: Neural circuits, Cellular neuroscience
MeSH: Basal Ganglia*, Intralaminar Thalamic Nuclei*, Animals, Male, Mice, Mice, Inbred C57BL, Neural Pathways, Neurons (* major topic)
Journal subjects: Resource
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (U01MH105982, U19MH114830); NINDS (U01NS132267)
Citations: cited by 1 paper (Europe PMC); 61 references in the paper
Research resources: 000) and parvalbumin RRID:AB_10000344, nuclear protein Fox3 RRID:AB_10048713, 000) and neurofilament-M RRID:AB_306956, 000) or neurofilament-H RRID:AB_509998

Abstract

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AllenInstitute/CP_molecular_organization

License: BSD-2-Clause
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ebc2d9bfbdba4204f053112ea3671f7e11610437, 26 February 2024
Languages: Python (43), Jupyter (5)
Size: 78 files, 48 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 4 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (46 files), pandas (36 files), SimpleITK (22 files), Matplotlib (19 files), seaborn (12 files), AllenSDK (5 files), SciPy (4 files), h5py (3 files), Plotly (2 files), scikit-image (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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50 files
At the source: github.com/AllenInstitute/CP_molecular_organization

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Recorded: type, language, journal, volume, issue, pages, dates, 26 authors, 2 keywords, 8 MeSH terms, 2 funders, 60 references, 4 RRIDs.

Cite

This paper

Wang, Q., Bhandiwad, A., Gouwens, N. W., Yao, S., Wang, Y., Kuang, X., Li, A., Li, X., Dalley, R., Kuo, H.-C., Lesnar, P., Xu, W., Mallory, M., Li, Y., El-Hifnawi, L., Ahmadinia, L., Ouellette, B., Kruse, L., Ng, L., . . . Zeng, H. (2026). Genoarchitecture and input-output organization of the mouse basal ganglia and thalamic parafascicular nucleus. Nature neuroscience, 29(5), 1248-1264. https://doi.org/10.1038/s41593-026-02253-9

BibTeX

@article{wang2026genoarchitecture,
author = {Wang, Quanxin and Bhandiwad, Ashwin and Gouwens, Nathan W. and Yao, Shenqin and Wang, Yun and Kuang, Xiuli and Li, Anan and Li, Xiangning and Dalley, Rachel and Kuo, Hsien-Chi and Lesnar, Phil and Xu, Wenjie and Mallory, Matt and Li, Yaoyao and El-Hifnawi, Laila and Ahmadinia, Leila and Ouellette, Ben and Kruse, Lauren and Ng, Lydia and Gong, Hui and Luo, Qingming and Kunst, Michael and van Velthoven, Cindy T. J. and Yao, Zizhen and Sorensen, Staci A. and Zeng, Hongkui},
title = {{Genoarchitecture and input-output organization of the mouse basal ganglia and thalamic parafascicular nucleus}},
journal = {Nature neuroscience},
year = {2026},
month = apr,
volume = {29},
number = {5},
pages = {1248--1264},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02253-9},
url = {https://doi.org/10.1038/s41593-026-02253-9},
pmid = {42049879},
pmcid = {PMC13156036}
}

RIS

TY - JOUR
AU - Wang, Quanxin
AU - Bhandiwad, Ashwin
AU - Gouwens, Nathan W.
AU - Yao, Shenqin
AU - Wang, Yun
AU - Kuang, Xiuli
AU - Li, Anan
AU - Li, Xiangning
AU - Dalley, Rachel
AU - Kuo, Hsien-Chi
AU - Lesnar, Phil
AU - Xu, Wenjie
AU - Mallory, Matt
AU - Li, Yaoyao
AU - El-Hifnawi, Laila
AU - Ahmadinia, Leila
AU - Ouellette, Ben
AU - Kruse, Lauren
AU - Ng, Lydia
AU - Gong, Hui
AU - Luo, Qingming
AU - Kunst, Michael
AU - van Velthoven, Cindy T. J.
AU - Yao, Zizhen
AU - Sorensen, Staci A.
AU - Zeng, Hongkui
TI - Genoarchitecture and input-output organization of the mouse basal ganglia and thalamic parafascicular nucleus
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/04/28
VL - 29
IS - 5
SP - 1248
EP - 1264
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02253-9
UR - https://doi.org/10.1038/s41593-026-02253-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02253-9",
"type": "article-journal",
"title": "Genoarchitecture and input-output organization of the mouse basal ganglia and thalamic parafascicular nucleus",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Wang",
"given": "Quanxin"
},
{
"family": "Bhandiwad",
"given": "Ashwin"
},
{
"family": "Gouwens",
"given": "Nathan W."
},
{
"family": "Yao",
"given": "Shenqin"
},
{
"family": "Wang",
"given": "Yun"
},
{
"family": "Kuang",
"given": "Xiuli"
},
{
"family": "Li",
"given": "Anan"
},
{
"family": "Li",
"given": "Xiangning"
},
{
"family": "Dalley",
"given": "Rachel"
},
{
"family": "Kuo",
"given": "Hsien-Chi"
},
{
"family": "Lesnar",
"given": "Phil"
},
{
"family": "Xu",
"given": "Wenjie"
},
{
"family": "Mallory",
"given": "Matt"
},
{
"family": "Li",
"given": "Yaoyao"
},
{
"family": "El-Hifnawi",
"given": "Laila"
},
{
"family": "Ahmadinia",
"given": "Leila"
},
{
"family": "Ouellette",
"given": "Ben"
},
{
"family": "Kruse",
"given": "Lauren"
},
{
"family": "Ng",
"given": "Lydia"
},
{
"family": "Gong",
"given": "Hui"
},
{
"family": "Luo",
"given": "Qingming"
},
{
"family": "Kunst",
"given": "Michael"
},
{
"family": "van Velthoven",
"given": "Cindy T. J."
},
{
"family": "Yao",
"given": "Zizhen"
},
{
"family": "Sorensen",
"given": "Staci A."
},
{
"family": "Zeng",
"given": "Hongkui"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "5",
"page": "1248-1264",
"DOI": "10.1038/s41593-026-02253-9",
"PMID": "42049879",
"PMCID": "PMC13156036",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02253-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
28
]
]
}
}

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