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Neurovascular coupling in the basolateral amygdala modulates negative emotions.

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  1. [1] § Results › Caldesmon acts as a downstream effector of GluN2D in NVC, modulating emotional reactivity ↔ Part2_Mass_spectrum_analysis/Third_KEGG/KeggEnrich.R, the whole file · a weak match · score 0.56 · vascular smooth muscle, enriched, pathway, contractile, mass

Paper

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The authors' code

R · 89 lines · 3.4 KB · no license · 1 match

  1. install.packages("BiocManager")
  2. library(BiocManager)
  3. BiocManager::install("AnnotationDbi")
  4. BiocManager::install("org.Hs.eg.db")
  5. BiocManager::install("org.Mm.eg.db")
  6. BiocManager::install("clusterProfiler")
  8. library(AnnotationDbi)
  9. library(org.Mm.eg.db)
  10. library(clusterProfiler)
  12. head(keys(org.Hs.eg.db,keytype = "PMID"))
  14. musKEGGID<-read.table("clipboard",header = F)
  15. musUniprotID<-read.table("clipboard",header = F)
  16. musNCBI<-read.table("clipboard",header = F)
  17. musNCBI<-substring(musNCBI[,1],13)
  18. musEntrezID<- bitr(musUniprotID[,1], fromType="UNIPROT",toType=c("ENTREZID","SYMBOL"), OrgDb="org.Mm.eg.db")
  21. #### ENRICH ####
  23. ### KEGG enrichment using uniprot ID
  24. kegg_IDuniprot<-enrichKEGG(musUniprotID[,1],
  25. organism = "mmu",
  26. keyType = "uniprot",
  27. pAdjustMethod = "BH",
  28. pvalueCutoff = 0.05)
  29. #"Vascular smooth muscle contraction" 136/278
  31. barplot(kegg_IDuniprot,showCategory = 150,title = "The KEGG enrichment analysis")
  33. ### KEGG enrichment using entrez ID
  34. kegg_IDEntrez<-enrichKEGG(musNCBI,
  35. organism = "mmu",
  36. keyType = "ncbi-geneid",
  37. pAdjustMethod = "BH",
  38. pvalueCutoff = 0.05)
  39. #"Vascular smooth muscle contraction" 125/289
  41. barplot(kegg_IDEntrez,showCategory = 150,title = "The KEGG enrichment analysis")
  43. ### KEGG enrichment using kegg ID
  44. kegg_IDkegg<-enrichKEGG(substring(musKEGGID[,1],5),
  45. organism = "mmu",
  46. keyType = "kegg",
  47. pAdjustMethod = "BH")
  48. #"Vascular smooth muscle contraction" 125/289
  49. dotplot(kegg_IDkegg,showCategory = 150,title = "The KEGG enrichment analysis")
  51. ### GO enrichment using entrez ID
  52. go_IDEntrez<-enrichGO(musNCBI,
  53. OrgDb = org.Mm.eg.db,
  54. keyType = "ENTREZID",
  55. pAdjustMethod = "BH",
  56. pvalueCutoff = 0.05,
  57. ont="BP")
  58. barplot(kegg_IDEntrez,showCategory = 150,title = "The KEGG enrichment analysis")
  60. kegg_result<-(as.data.frame(kegg_IDkegg@result))
  61. write.table(kegg_result,file = "E:/醉梦不醒A/Westlake/JiaLab_outsider/Jiayu_enrich/kegg_res.txt",sep = "\t",quote = F,row.names = F)
  62. save.image("E:/醉梦不醒A/Westlake/JiaLab_outsider/Jiayu_enrich/enrich_res.RData")
  65. #### Plot ####
  66. rm(list = ls())
  67. kegg_res<-read.delim("E:/醉梦不醒A/Westlake/JiaLab_outsider/Jiayu_enrich/kegg_res.txt",header = T)
  69. #kegg_plot<-kegg_res[1:150,]
  70. kegg_plot<-kegg_res[order(kegg_res$p.adjust),][1:150,] ##提取显著性前150的通路绘图
  71. top150<-data.frame("Description"=kegg_plot$Description,"count"=kegg_plot$Count,"padj"=kegg_plot$p.adjust)
  73. ##barplot
  74. p<-ggplot(data = top150,aes(x=Description,y=count,fill=padj))
  75. p1<-p+geom_bar(stat="identity")+ coord_flip()
  76. p2 <- p1 + theme(panel.background=element_rect(fill='transparent',color='gray'),
  77. axis.text.y=element_text(color="black",size=12))
  78. #ylim(0,65) 更改横坐标的范围这里坐标轴颠倒了，虽然看起来是x轴，但其实是y轴
  79. p3 <- p2 + ylim(0,65) + scale_fill_gradient(low="red",high="blue")
  80. p4 <- p3 + scale_x_discrete(limits=rev(top150[,1])) +labs(x="",y="",title="KEGG")
  81. p4
  84. ###Dotplot
  85. ggplot(top150,aes(y=Description,x=count))+geom_point()+geom_point(aes(size=count,color=padj))+
  86. scale_color_gradient(low="red",high = "blue")+
  87. labs(x="Pvalue",y="Pathways",title = "KEGG Pathway Enrichment")

KeggEnrich.R at commit 874a5f7, no license · at the source

Overview

Authors: Jiayu Ruan1,2,3,4, Xinghua Quan3, Huiqi Xie3, Yiyi Zhang3, Dongdong Zhang2, Wentao Wang3, Bingrui Zhao3, Danping Lu2, Yuxiao Niu5, Jie-Min Jia2,3,4,6
ORCID iDs: Jiayu Ruan, Jie-Min Jia
  1. College of Life Sciences, Zhejiang University,Hangzhou, Zhejiang China
  2. Westlake Laboratory of Life Sciences and Biomedicine,Hangzhou, Zhejiang China
  3. Laboratory of Neurovascular Biology, School of Life Sciences, Westlake University,Hangzhou, Zhejiang China
  4. Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study,Hangzhou, Zhejiang China
  5. Laboratory of Computational and Functional Genomics, School of Life Sciences, Westlake University,Hangzhou, Zhejiang China
  6. State Key Laboratory of Gene Expression, School of Life Sciences, Westlake University,Hangzhou, Zhejiang China
Institutions: Zhejiang University (China); Westlake University (China); Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences
Journal: Cell research, volume 36, issue 9, pages 656-679
Dates: received 14 August 2025; accepted 29 April 2026; published online 27 May 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41422-026-01256-2 · PMID 42191977 · PMCID PMC13503848 · OpenAlex W7162416200
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Spectral & time-frequency, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: Cell biology, Molecular biology
MeSH: Basolateral Nuclear Complex*, Emotions*, Neurovascular Coupling*, Animals, Male, Mice, Mice, Inbred C57BL, Stress, Psychological (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HRHI program of Westlake Laboratory of Life Sciences and Biomedicine (Grant No. 202309002) National Natural Science Foundation of China (Grant No. 32571142) State Key Laboratory of Gene Expression (Grant No. 2025ZY01117) The Westlake University Feng-Ying Career Development Chair Fund (Grant No. WK20260325001)
Citations: cited by 2 papers (Europe PMC); 105 references in the paper

Abstract

Emotion induces changes in regional cerebral blood flow, a manifestation of neurovascular coupling (NVC). However, whether NVC provides feedback to actively modulate emotion remains unexplored. Here, we demonstrate that NVC actively and bidirectionally modulates stress-induced negative emotions. We established bidirectional manipulations of NVC in freely moving mice by employing integrated pharmacological, genetic, and arteriolar optogenetic approaches. Our results showed that both systemic and region-specific NVC deficiencies in the basolateral amygdala (BLA) heightened emotional responses when mice transitioned from a safe, familiar environment to anxiogenic environments and that local restoration of NVC in the BLA normalized these responses. Mechanistically, NVC dysfunction impaired the capacity of BLA neuronal scaling during state transitions, manifesting as a characteristic biphasic pattern of c-Fos topology. NVC-deficient animals aberrantly adopted high-stress configurations under mild stress but regressed to low-stress templates during high-demand survival threats, thereby compromising defensive sustainability. Notably, the genetic NVC-enhancement model counteracted NVC impairments caused by chronic stress, thereby alleviating stress-driven emotional distress. These findings establish NVC in the BLA as an allostatic program that fine-tunes neural circuit activity during emotional responses, with implications for understanding and treating emotional disorders.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

JialabEleven/Functional_hyperemia_regulates_emotion-master

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 874a5f782942e9759a3d8766f7ab482b291ae261, 28 November 2025
Languages: MATLAB (7), R (1)
Size: 100 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (3 files), clusterProfiler (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/JialabEleven/Functional_hyperemia_regulates_emotion-master

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Detailed information on the mice, antibodies, and primers used in this study is provided in Supplementary information, Tables S3, S4, S5, respectively. Fluorescence images were processed using Zen Lite (v3.4; Zeiss) and ImageJ (v1.53 u). For fiber photometry, signal demodulation and linear regression correction were performed using the system’s built-in proprietary software (Thinkertech). Whisker stimulation data were acquired and analyzed using the LSCI software (v5.0; RWD Life Science). Trajectory tracking for the OFT, c-OFT, and EPM test was performed using the EthoVision XT system (v18; Noldus, Wageningen, the Netherlands), which was also utilized to generate locomotion heatmaps. For the Looming and VReagle assays, tracking was conducted using custom scripts (Bayonne). For the physiological and topology analysis, custom MATLAB scripts were developed to analyze CBF peak kinetics, map aggregate defecation coordinates, and generate neural activation heatmaps for the topology-based estimation framework. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and SSIM-based hierarchical clustering were executed using custom R algorithms. Statistical analyses were conducted using GraphPad Prism (v9.4.1) and MATLAB. Figures were assembled using Illustrator 2024 (v28.3; Adobe, San Jose, CA, USA). Outliers were identified and excluded using the interquartile range (IQR) method (values falling outside the range of Q1 — 1.5 × IQR to Q3 + 1.5 × IQR).105 Data are expressed as mean ± standard error of the mean (SEM). The normality of data distribution was assessed using the Shapiro-Wilk test. Specific statistical tests employed for each experiment are detailed in the corresponding figure legends. Exact P values are reported to four decimal places. A P value < 0.05 was considered indicative of statistical significance. A comprehensive data resource checklist, detailing figure attributions and sample sources, is provided in Supplementary information, Table S2 for reference. Furthermore, to ensure maximum transparency and statistical robustness, the percentage of c-Fos-positive neurons in the BLA across all experimental cohorts is quantified and presented in Supplementary Fig. S25. All custom MATLAB and R code generated for the topology-based estimation framework and hemodynamic analysis is available on GitHub: https://github.com/JialabEleven/Functional_hyperemia_regulates_emotion-master.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 8 MeSH terms, 1 funder, 105 references.

Cite

This paper

Ruan, J., Quan, X., Xie, H., Zhang, Y., Zhang, D., Wang, W., Zhao, B., Lu, D., Niu, Y., & Jia, J.-M. (2026). Neurovascular coupling in the basolateral amygdala modulates negative emotions. Cell research, 36(9), 656-679. https://doi.org/10.1038/s41422-026-01256-2

BibTeX

@article{ruan2026neurovascular,
author = {Ruan, Jiayu and Quan, Xinghua and Xie, Huiqi and Zhang, Yiyi and Zhang, Dongdong and Wang, Wentao and Zhao, Bingrui and Lu, Danping and Niu, Yuxiao and Jia, Jie-Min},
title = {{Neurovascular coupling in the basolateral amygdala modulates negative emotions}},
journal = {Cell research},
year = {2026},
month = may,
volume = {36},
number = {9},
pages = {656--679},
publisher = {Nature Publishing Group},
issn = {1001-0602},
doi = {10.1038/s41422-026-01256-2},
url = {https://doi.org/10.1038/s41422-026-01256-2},
pmid = {42191977},
pmcid = {PMC13503848}
}

RIS

TY - JOUR
AU - Ruan, Jiayu
AU - Quan, Xinghua
AU - Xie, Huiqi
AU - Zhang, Yiyi
AU - Zhang, Dongdong
AU - Wang, Wentao
AU - Zhao, Bingrui
AU - Lu, Danping
AU - Niu, Yuxiao
AU - Jia, Jie-Min
TI - Neurovascular coupling in the basolateral amygdala modulates negative emotions
T2 - Cell research
J2 - Cell Res
PY - 2026
DA - 2026/05/27
VL - 36
IS - 9
SP - 656
EP - 679
SN - 1001-0602
PB - Nature Publishing Group
DO - 10.1038/s41422-026-01256-2
UR - https://doi.org/10.1038/s41422-026-01256-2
LA - en
ER -

CSL-JSON

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"container-title": "Cell research",
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"family": "Ruan",
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"PMCID": "PMC13503848",
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