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Integrating optogenetic fMRI and spatial transcriptomics to reveal circuit-specific gene signatures in fronto- and hippo-thalamic networks.

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] § Results › Coregistration of opto-fMRI-ST data in the thalamus ↔ Region_dict.py, lines 1–65 · score 0.67 · ATN, MED, MTN, PP, SPA, VENT
  2. [2] § Methods › fMRI data processing ↔ Fig_S2-E.py, lines 215–287 · score 0.66 · head motion, BOLD signals, DVARS, FD, positions, masks

Paper

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The authors' code

Python · 109 lines · 6.5 KB · no license · 1 match

  1. import pandas as pd
  2. import numpy as np
  4. Prefrontal_dict = {'FRP': ['FRP', 'FRP1', 'FRP2/3', 'FRP5', 'FRP6a', 'FRP6b'],
  5. 'ACAd': ['ACAd', 'ACAd1', 'ACAd2/3', 'ACAd5', 'ACAd6a', 'ACAd6b'],
  6. 'ACAv': ['ACAv', 'ACAv1', 'ACAv2/3', 'ACAv5', 'ACAv6a', 'ACAv6b'],
  7. 'PL': ['PL', 'PL1', 'PL2', 'PL2/3', 'PL5', 'PL6a', 'PL6b'],
  8. 'ILA': ['ILA', 'ILA1', 'ILA2', 'ILA2/3', 'ILA5', 'ILA6a', 'ILA6b'],
  9. 'ORBl': ['ORBl', 'ORBl1', 'ORBl2/3', 'ORBl5', 'ORBl6a', 'ORBl6b'],
  10. 'ORBm': ['ORBm', 'ORBm1', 'ORBm2', 'ORBm2/3', 'ORBm5', 'ORBm6a', 'ORBm6b'],
  11. 'ORBvl': ['ORBvl', 'ORBvl1', 'ORBvl2/3', 'ORBvl5', 'ORBvl6a', 'ORBvl6b']}
  13. Lateral_dict = {'AId': ['AId', 'AId1', 'AId2/3', 'AId5', 'AId6a', 'AId6b'],
  14. 'AIv': ['AIv', 'AIv1', 'AIv2/3', 'AIv5', 'AIv6a', 'AIv6b'],
  15. 'AIp': ['AIp', 'AIp1', 'AIp2/3', 'AIp5', 'AIp6a', 'AIp6b'],
  16. 'GU': ['GU', 'GU1', 'GU2/3', 'GU4', 'GU5', 'GU6a', 'GU6b'],
  17. 'VISC': ['VISC', 'VISC1', 'VISC2/3', 'VISC4', 'VISC5', 'VISC6a', 'VISC6b']}
  19. Somatomotor_dict = {'SSs': ['SSs', 'SSs1', 'SSs2/3', 'SSs4', 'SSs5', 'SSs6a', 'SSs6b'],
  20. 'SSp-bfd': ['SSp-bfd', 'SSp-bfd1', 'SSp-bfd2/3', 'SSp-bfd4', 'SSp-bfd5', 'SSp-bfd6a', 'SSp-bfd6b'],
  21. 'SSp-tr': ['SSp-tr', 'SSp-tr1', 'SSp-tr2/3', 'SSp-tr4', 'SSp-tr5', 'SSp-tr6a', 'SSp-tr6b'],
  22. 'SSp-ll': ['SSp-ll', 'SSp-ll1', 'SSp-ll2/3', 'SSp-ll4', 'SSp-ll5', 'SSp-ll6a', 'SSp-ll6b'],
  23. 'SSp-ul': ['SSp-ul', 'SSp-ul1', 'SSp-ul2/3', 'SSp-ul4', 'SSp-ul5', 'SSp-ul6a', 'SSp-ul6b'],
  24. 'SSp-un': ['SSp-un', 'SSp-un1', 'SSp-un2/3', 'SSp-un4', 'SSp-un5', 'SSp-un6a', 'SSp-un6b'],
  25. 'SSp-n': ['SSp-n', 'SSp-n1', 'SSp-n2/3', 'SSp-n4', 'SSp-n5', 'SSp-n6a', 'SSp-n6b'],
  26. 'SSp-m': ['SSp-m', 'SSp-m1', 'SSp-m2/3', 'SSp-m4', 'SSp-m5', 'SSp-m6a', 'SSp-m6b'],
  27. 'MOp': ['MOp', 'MOp1', 'MOp2/3', 'MOp5', 'MOp6a', 'MOp6b'],
  28. 'MOs': ['MOs', 'MOs1', 'MOs2/3', 'MOs5', 'MOs6a', 'MOs6b']}
  30. Visual_dict = {'VISal': ['VISal', 'VISal1', 'VISal2/3', 'VISal4', 'VISal5', 'VISal6a', 'VISal6b'],
  31. 'VISl': ['VISl', 'VISl1', 'VISl2/3', 'VISl4', 'VISl5', 'VISl6a', 'VISl6b'],
  32. 'VISp': ['VISp', 'VISp1', 'VISp2/3', 'VISp4', 'VISp5', 'VISp6a', 'VISp6b'],
  33. 'VISli': ['VISli', 'VISli1', 'VISli2/3', 'VISli4', 'VISli5', 'VISli6a', 'VISli6b'],
  34. 'VISrl': ['VISrl', 'VISrl1', 'VISrl2/3', 'VISrl4', 'VISrl5', 'VISrl6a', 'VISrl6b']}
  37. Medial_dict = {'VISa': ['VISa', 'VISa1', 'VISa2/3', 'VISa4', 'VISa5', 'VISa6a', 'VISa6b'],
  38. 'VISam': ['VISam', 'VISam1', 'VISam2/3', 'VISam4', 'VISam5', 'VISam6a', 'VISam6b'],
  39. 'VISpm': ['VISpm', 'VISpm1', 'VISpm2/3', 'VISpm4', 'VISpm5', 'VISpm6a', 'VISpm6b'],
  40. 'RSPagl': ['RSPagl', 'RSPagl1', 'RSPagl2/3', 'RSPagl5', 'RSPagl6a', 'RSPagl6b'],
  41. 'RSPd': ['RSPd', 'RSPd1', 'RSPd2/3', 'RSPd4', 'RSPd5', 'RSPd6a', 'RSPd6b'],
  42. 'RSPv': ['RSPv', 'RSPv1', 'RSPv2', 'RSPv2/3', 'RSPv5', 'RSPv6a', 'RSPv6b']}
  44. Aud_dict = {'AUDd': ['AUDd', 'AUDd1', 'AUDd2/3', 'AUDd4', 'AUDd5', 'AUDd6a', 'AUDd6b'],
  45. 'AUDp': ['AUDp', 'AUDp1', 'AUDp2/3', 'AUDp4', 'AUDp5', 'AUDp6a', 'AUDp6b'],
  46. 'AUDpo': ['AUDpo', 'AUDpo1', 'AUDpo2/3', 'AUDpo4', 'AUDpo5', 'AUDpo6a', 'AUDpo6b'],
  47. 'AUDv': ['AUDv', 'AUDv1', 'AUDv2/3', 'AUDv4', 'AUDv5', 'AUDv6a', 'AUDv6b']}
  49. Thalamus_dict = {'VENT': ['VENT', 'PoT', 'VAL', 'VM', 'VP', 'VPL', 'VPLpc', 'VPM', 'VPMpc'],
  50. 'GENd': ['GENd', 'LGd', 'LGd-sh', 'LGd-co', 'LGd-ip', 'MG', 'MGd', 'MGv', 'MGm'],
  51. 'SPF':[ 'SPF', 'SPFm', 'SPFp'],
  52. 'SPA':['SPA'],
  53. 'PP':['PP'],
  54. 'LAT': ['LAT', 'Eth', 'LP', 'PO', 'POL', 'REth', 'SGN'],
  55. 'ATN': ['ATN', 'AD', 'AM', 'AMd', 'AMv', 'AV', 'IAD', 'IAM', 'LD'],
  56. 'MED': ['MED', 'IMD', 'MD', 'MDc', 'MDl', 'MDm', 'PR', 'SMT'],
  57. 'MTN': ['MTN', 'RE', 'PT', 'PVT', 'Xi'],
  58. 'ILM': ['ILM', 'CL', 'CM', 'PCN', 'PF', 'PIL', 'RH'],
  59. 'RT': ['RT'],
  60. 'GENv': ['GENv', 'IGL', 'IntG', 'SubG', 'LGv', 'LGvl', 'LGvm'],
  61. 'EPI': ['EPI', 'LH', 'MH', 'PIN']}
  64. Midbrain_dict = {
  65. 'MBmot': ['MBmot', 'SNr', 'VTA', 'PN', 'RR', 'MRN', 'MRNm', 'MRNmg', 'MRNp', 'SCm', 'SCdg', 'SCdw', 'SCiw', 'SCig', 'SCig-a', 'SCig-b', 'SCig-c', 'PAG', 'INC', 'ND', 'PRC', 'Su3', 'PRT', 'APN', 'MPT', 'NOT', 'NPC', 'OP', 'PPT', 'RPF', 'InCo', 'CUN', 'RN', 'III', 'MA3', 'EW', 'IV', 'Pa4', 'VTN', 'AT', 'LT', 'DT', 'MT', 'SNl']}
  68. ## Striatum_dict
  69. Striatum_dict = {'STRd': ['STRd', 'CP'],
  70. 'STRv': ['STRv', 'ACB', 'FS', 'OT', 'isl', 'islm', 'OT1', 'OT2', 'OT3'],
  71. 'LSX': ['LSX', 'LS', 'LSc', 'LSr', 'LSv', 'SF', 'SH']}
  73. ## Pallidum_dict
  74. Pallidum_dict = {'PALd': ['PALd', 'GPe', 'GPi'],
  75. 'PALv': ['PALv', 'SI', 'MA'],
  76. 'PALm': ['PALm', 'MSC', 'MS', 'NDB', 'TRS'],
  77. 'PALc': ['PALc', 'BST', 'BSTa', 'BSTal', 'BSTam', 'BSTdm', 'BSTfu', 'BSTju', 'BSTmg', 'BSTov', 'BSTrh', 'BSTv', 'BSTp', 'BSTd', 'BSTpr', 'BSTif', 'BSTtr', 'BSTse', 'BAC']}
  80. ## CTXsp
  81. CTXsp_dict={
  82. 'CLA': ['CLA'],
  83. }
  85. ## Hippocampal_dict
  86. Hippocampal_dict={
  87. 'CA1': ['CA1', 'CA1slm', 'CA1so', 'CA1sp', 'CA1sr'],
  88. 'CA2': ['CA2', 'CA2slm', 'CA2so', 'CA2sp', 'CA2sr'],
  89. 'CA3': ['CA3', 'CA3slm', 'CA3slu', 'CA3so', 'CA3sp', 'CA3sr'],
  90. 'DG': ['DG', 'DG-mo', 'DG-po', 'DG-sg', 'DG-sgz', 'DGcr', 'DGcr-mo', 'DGcr-po', 'DGcr-sg', 'DGlb', 'DGlb-mo', 'DGlb-po', 'DGlb-sg', 'DGmb', 'DGmb-mo', 'DGmb-po', 'DGmb-sg'],
  91. 'POST': ['POST', 'POST1', 'POST2', 'POST3'],
  92. 'SUBd': ['SUB', 'SUBd', 'SUBd-m', 'SUBd-sp', 'SUBd-sr'],
  93. 'SUBv': ['SUBv', 'SUBv-m', 'SUBv-sp', 'SUBv-sr'],
  94. 'PRE': ['PRE', 'PRE1', 'PRE2', 'PRE3'],
  95. }
  98. All_dict = {**Prefrontal_dict, **Lateral_dict, **Somatomotor_dict, **Visual_dict, **Medial_dict, **Aud_dict, **Thalamus_dict, **Midbrain_dict, **Striatum_dict, **Pallidum_dict, **CTXsp_dict, **Hippocampal_dict}
  102. CCF_info = pd.read_csv('/data100/dataset/mice_2021/Allen_CCF/P56_Label.csv')
  103. CCF_index = np.concatenate([CCF_info['Index'].values, CCF_info['Index'].values + 10000])
  104. CCF_index[CCF_index == 10000] = 0
  105. CCF_new = pd.DataFrame({
  106. 'index': CCF_index,
  107. 'ACR_name': np.concatenate([CCF_info['ACR_name'].values + '_lh', CCF_info['ACR_name'].values + '_rh']),
  108. 'Full_name': np.concatenate([CCF_info['Full_name'].values + '_lh', CCF_info['Full_name'].values + '_rh'])
  109. }).set_index('Full_name')

Region_dict.py at commit 5097601, no license · at the source

Overview

Authors: Changjiang Zhang1,2, Yijuan Zou2, Liangchen Zhuo1,3, Xiya Liu1,3, Suijuan Zhong4, Chonghai Yin2, Mayuqing Li4, Mengdi Wang1, Wei Wang1, Xin Zhou4, Bo Zeng2, Changsheng Dong1,3, Le Sun5, Zheng Wang6, Ang Li1,3, Qian Wu4, Xiaoqun Wang1,2,4
ORCID iDs: Changjiang Zhang, Liangchen Zhuo, Suijuan Zhong, Zheng Wang, Ang Li, Qian Wu, Xiaoqun Wang
  1. State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
  2. Changping Laboratory, Beijing, China
  3. University of Chinese Academy of Sciences, Beijing, China
  4. IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
  5. Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
  6. School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, State Key Laboratory of General Artificial Intelligence, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
Institutions: Chinese Academy of Sciences (China); Institute of Biophysics (China); Changping Laboratory (China); University of Chinese Academy of Sciences (China); Beijing Normal University (China); Capital Medical University (China); Beijing Institute for Brain Disorders (China); Peking University (China); Center for Life Sciences (China)
Journal: Nature communications, volume 17, issue 1, article 5387
Dates: received 26 March 2025; accepted 31 March 2026; published online 18 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71923-w · PMID 42000729 · PMCID PMC13276179 · OpenAlex W7154829783
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), fMRI (modality), mouse (organism), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging
Keywords: Neural circuits, Genetics of the nervous system
MeSH: Magnetic Resonance Imaging*, Nerve Net*, Optogenetics*, Thalamus*, Transcriptome*, Animals, Brain Mapping, Male, Mice, Mice, Inbred C57BL, Prefrontal Cortex, Spatial Transcriptomics (* major topic)
Topic: Photoreceptor and optogenetics research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 97 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

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wangxiaoqun-lab/opto_fMRI_ST

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 509760108dec8647559f60a1129102501a948f8f, 7 April 2025
Languages: Python (11), Jupyter (3)
Size: 15 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), Nilearn (7 files), pandas (7 files), Matplotlib (5 files), ggplot2 (3 files), SciPy (3 files), Seurat (3 files), tidyverse (3 files), pheatmap (2 files), NiBabel (1 file), Pillow (1 file), reshape2 (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
15 files
At the source: github.com/wangxiaoqun-lab/opto_fMRI_ST.git

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  • 14 scripts, each with its path and the digest of its content;
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Code and data availability statement

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 2 keywords, 12 MeSH terms, 93 references.

Cite

This paper

Zhang, C., Zou, Y., Zhuo, L., Liu, X., Zhong, S., Yin, C., Li, M., Wang, M., Wang, W., Zhou, X., Zeng, B., Dong, C., Sun, L., Wang, Z., Li, A., Wu, Q., & Wang, X. (2026). Integrating optogenetic fMRI and spatial transcriptomics to reveal circuit-specific gene signatures in fronto- and hippo-thalamic networks. Nature communications, 17(1), 5387. https://doi.org/10.1038/s41467-026-71923-w

BibTeX

@article{zhang2026integrating,
author = {Zhang, Changjiang and Zou, Yijuan and Zhuo, Liangchen and Liu, Xiya and Zhong, Suijuan and Yin, Chonghai and Li, Mayuqing and Wang, Mengdi and Wang, Wei and Zhou, Xin and Zeng, Bo and Dong, Changsheng and Sun, Le and Wang, Zheng and Li, Ang and Wu, Qian and Wang, Xiaoqun},
title = {{Integrating optogenetic fMRI and spatial transcriptomics to reveal circuit-specific gene signatures in fronto- and hippo-thalamic networks}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5387},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71923-w},
url = {https://doi.org/10.1038/s41467-026-71923-w},
pmid = {42000729},
pmcid = {PMC13276179}
}

RIS

TY - JOUR
AU - Zhang, Changjiang
AU - Zou, Yijuan
AU - Zhuo, Liangchen
AU - Liu, Xiya
AU - Zhong, Suijuan
AU - Yin, Chonghai
AU - Li, Mayuqing
AU - Wang, Mengdi
AU - Wang, Wei
AU - Zhou, Xin
AU - Zeng, Bo
AU - Dong, Changsheng
AU - Sun, Le
AU - Wang, Zheng
AU - Li, Ang
AU - Wu, Qian
AU - Wang, Xiaoqun
TI - Integrating optogenetic fMRI and spatial transcriptomics to reveal circuit-specific gene signatures in fronto- and hippo-thalamic networks
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/18
VL - 17
IS - 1
SP - 5387
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71923-w
UR - https://doi.org/10.1038/s41467-026-71923-w
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Integrating optogenetic fMRI and spatial transcriptomics to reveal circuit-specific gene signatures in fronto- and hippo-thalamic networks",
"container-title": "Nature communications",
"author": [
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{
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"given": "Mayuqing"
},
{
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"given": "Mengdi"
},
{
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"given": "Wei"
},
{
"family": "Zhou",
"given": "Xin"
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{
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"volume": "17",
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"page": "5387",
"DOI": "10.1038/s41467-026-71923-w",
"PMID": "42000729",
"PMCID": "PMC13276179",
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"issued": {
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[
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4,
18
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}
}

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