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Circuit response to neuromodulation characterized with simultaneous deep brain stimulation and precision neuroimaging in humans.

Code ↔ Paper

7 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 7 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data analyses › Therapeutic effects of DBS ↔ UPDRSIII/Motor_symptom_change/S1_LME_subitem.R, the whole file · a weak match · score 0.92 · lmertest package, pairwise comparisons, Post hoc, emmeans, lme4, motor symptom
  2. [2] § Methods › Data analyses › Therapeutic effects of DBS ↔ UPDRSIII/Motor_symptom_change/S1_LME_UPDRSIII.R, lines 1–44 · score 0.88 · pairwise comparisons, Post hoc, emmeans, lme4, motor symptom, interactions
  3. [3] § Methods › Data acquisition › Neurological assessments ↔ UPDRSIII/Motor_symptom_change/S1_LME_subitem.R, the whole file · a weak match · score 0.56 · Bradykinesia, Posture, rigidity, Gait, PIGD, Tremor
  4. [4] § Methods › Data preprocessing › Resting-state fMRI preprocessing ↔ bCompCor/bcompcor.py, lines 36–46 · score 0.55 · 0.01–0.08 Hz, band, preprocessed, regressing, Brain, 0.01 Hz
  5. [5] § Results › DBS target-cortical connectivity estimated within individuals predicts clinical outcomes ↔ VTA_part/VTA_Predict/S2_predict_by_patient_idealmap.py, the whole file · a weak match · score 0.54 · cross validation, ideal map, predicted, Spearman, weighted, clinical
  6. [6] § Results › DBS target-cortical connectivity estimated within individuals predicts clinical outcomes ↔ VTA_part/VTA_Predict/S2_predict_by_normal_idealmap.py, the whole file · a weak match · score 0.54 · cross validation, ideal map, predicted, Spearman, weighted, clinical
  7. [7] § Methods › Data analyses › LOSOCV ↔ VTA_part/VTA_Predict/S2_predict_by_patient_idealmap.py, the whole file · a weak match · score 0.50 · ideal map, prediction, Spearman, fitted, VTA, model

Paper

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The authors' code

R · 80 lines · 2.5 KB · no license · 2 matches

  1. # %%
  2. # Install and load lme4 and lmerTest packages
  3. # install.packages("lmerTest")
  4. library(lme4)
  5. library(lmerTest)
  6. library(dplyr)
  7. library(tidyverse)
  8. library(emmeans)
  10. # %%
  11. # Read the CSV file
  12. df <- read.csv("./Reliability/UPDRS_bradykinesia.csv")
  13. ### Output the first few rows of the data frame
  14. head(df)
  16. # Use the separate() function to split the 'condition' column into 'time' and 'Freq' columns
  17. df <- df %>%
  18. separate(condition, into = c("time", "Freq"), sep = "(?<=m)")
  20. ### Set 'time' and 'Freq' as factors
  21. df$time <- factor(df$time, levels = c("1m", "3m", "6m", "12m"))
  22. df$Freq <- factor(df$Freq, levels = c("OFF", "CH", "CV", "CL"))
  23. df$Subject <- as.factor(df$Subject)
  25. ### Calculate 'score' as the average of 'score' and 'score_retest'
  26. df$score <- (df$score + df$score_retest) / 2
  28. ### Perform comparison between ON and OFF conditions
  29. # Create a new condition variable (Freq2: ON vs OFF)
  30. df$Freq2 <- ifelse(df$Freq %in% c("CH", "CV", "CL"), "ON", "OFF")
  31. df$Freq2 <- factor(df$Freq2, levels = c("OFF", "ON")) # Ensure OFF is the reference level
  33. # Fit a linear mixed-effects model
  34. model <- lmer(score ~ time * Freq2 + (1 | Subject), data = df)
  36. # Perform analysis of variance using the lmertest package
  37. summary(model)
  38. anova(model)
  40. ### Perform post-hoc comparisons
  41. marginal_means_freq <- emmeans(model, ~ Freq2 | time)
  43. ### Perform pairwise comparisons
  44. pairwise_comparisons_freq <- pairs(marginal_means_freq)
  46. summary(pairwise_comparisons_freq)
  48. # %%
  49. ### Perform similar analysis for other sub-scores besides bradykinesia
  50. sub_items <- c("gait", "posture", "tremor", "rigidity", "PIGD")
  52. for (sub_item in sub_items) {
  53. print(paste("Analyzing", sub_item))
  55. # Read the CSV file for the current sub-item
  56. df <- read.csv(paste0("./Reliability/UPDRS_", sub_item, ".csv"))
  58. # Separate 'condition' column
  59. df <- df %>%
  60. separate(condition, into = c("time", "Freq"), sep = "(?<=m)")
  62. # Set 'time' and 'Freq' as factors
  63. df$time <- factor(df$time, levels = c("1m", "3m", "6m", "12m"))
  64. df$Freq <- factor(df$Freq, levels = c("OFF", "CH", "CV", "CL"))
  65. df$Subject <- as.factor(df$Subject)
  67. # Calculate 'score' as the average of 'score' and 'score_retest'
  68. df$score <- (df$score + df$score_retest) / 2
  70. # Create a new condition variable (Freq2: ON vs OFF)
  71. df$Freq2 <- ifelse(df$Freq %in% c("CH", "CV", "CL"), "ON", "OFF")
  72. df$Freq2 <- factor(df$Freq2, levels = c("OFF", "ON"))
  74. # Fit a linear mixed-effects model
  75. model <- lmer(score ~ time * Freq2 + (1 | Subject), data = df)
  77. # Perform analysis of variance
  78. summary(model)
  79. anova(model)
  80. }

S1_LME_subitem.R at commit 2b42b6d, no license · at the source

Overview

Authors: Jianxun Ren1,2, Changqing Jiang1, Wei Zhang2,3, Louisa Dahmani4,5, Lunhao Shen1, Feng Zhang1, Shenshen Li2,6, Changgeng He1, Yilin Yin2, Xiaoxuan Fu2, Jianting Huang2, Yang Long2,7, Dantong Liu2,6, Yi Guo8, Yiming Liu9, Shujun Xu9, Fangang Meng10, Jianguo Zhang10, Danhong Wang2, Luming Li1,11, Hesheng Liu2,12
ORCID iDs: Changqing Jiang, Wei Zhang, Louisa Dahmani, Dantong Liu, Jianguo Zhang, Danhong Wang, Luming Li, Hesheng Liu
  1. National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
  2. Changping Laboratory, Beijing, China
  3. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
  4. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA USA
  5. Harvard Medical School, Boston, MA USA
  6. College of Future Technology, Peking University, Beijing, China
  7. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  8. Peking Union Medical College Hospital, Beijing, China
  9. Qilu Hospital of Shandong University, Jinan, China
  10. Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  11. IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, China
  12. Biomedical Pioneering Innovation Center, Peking University, Beijing, China
Institutions: Changping Laboratory (China); Tsinghua University (China); Peking University (China); Harvard University (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States); Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning; Peking Union Medical College Hospital (China); Qilu Hospital of Shandong University (China); Capital Medical University (China); Beijing Tian Tan Hospital (China)
Journal: Nature neuroscience, volume 29, issue 7, pages 1749-1761
Dates: received 18 February 2025; accepted 26 January 2026; published online 13 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02228-w · PMID 41826711 · PMCID PMC13337495 · OpenAlex W7135194605
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), Parkinson's (population), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Magnetic resonance imaging, Parkinson's disease, Cognitive neuroscience, Neural circuits
MeSH: Brain*, Deep Brain Stimulation*, Globus Pallidus*, Nerve Net*, Neuroimaging*, Parkinson Disease*, Aged, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways (* major topic)
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (81527901, 81790650, 81790652, 61761166004, 81830033, and 81771373)
Citations: cited by 5 papers (Europe PMC); 107 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

pBFSLab/open-DBS

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2b42b6d248550bd64a87b841e0afa04b17909584, 1 August 2025
Languages: Python (22), R (5), MATLAB (4)
Size: 37 files, 31 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (11 files), pandas (9 files), NiBabel (8 files), ANTs (7 files), SciPy (5 files), Matplotlib (4 files), seaborn (4 files), tidyverse (4 files), lme4 (3 files), lmerTest (3 files), Statistics and Machine Learning Toolbox (3 files), statsmodels (3 files), emmeans (2 files), FreeSurfer (2 files), ggpubr (2 files), reshape2 (2 files), rstatix (2 files), scikit-learn (2 files), OpenCV (1 file), Pingouin (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
32 files
At the source: github.com/pBFSLab/open-DBS

deepprep.readthedocs.io

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: deepprep.readthedocs.io/en/latest/

Code availability statement

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Read it in the paper: doi.org/10.1038/s41593-026-02228-w.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 31 scripts, each with its path and the digest of its content;
  • 7 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

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41593-026-02228-w.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 4 keywords, 14 MeSH terms, 1 funder, 97 references.

Cite

This paper

Ren, J., Jiang, C., Zhang, W., Dahmani, L., Shen, L., Zhang, F., Li, S., He, C., Yin, Y., Fu, X., Huang, J., Long, Y., Liu, D., Guo, Y., Liu, Y., Xu, S., Meng, F., Zhang, J., Wang, D., . . . Liu, H. (2026). Circuit response to neuromodulation characterized with simultaneous deep brain stimulation and precision neuroimaging in humans. Nature neuroscience, 29(7), 1749-1761. https://doi.org/10.1038/s41593-026-02228-w

BibTeX

@article{ren2026circuit,
author = {Ren, Jianxun and Jiang, Changqing and Zhang, Wei and Dahmani, Louisa and Shen, Lunhao and Zhang, Feng and Li, Shenshen and He, Changgeng and Yin, Yilin and Fu, Xiaoxuan and Huang, Jianting and Long, Yang and Liu, Dantong and Guo, Yi and Liu, Yiming and Xu, Shujun and Meng, Fangang and Zhang, Jianguo and Wang, Danhong and Li, Luming and Liu, Hesheng},
title = {{Circuit response to neuromodulation characterized with simultaneous deep brain stimulation and precision neuroimaging in humans}},
journal = {Nature neuroscience},
year = {2026},
month = mar,
volume = {29},
number = {7},
pages = {1749--1761},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02228-w},
url = {https://doi.org/10.1038/s41593-026-02228-w},
pmid = {41826711},
pmcid = {PMC13337495}
}

RIS

TY - JOUR
AU - Ren, Jianxun
AU - Jiang, Changqing
AU - Zhang, Wei
AU - Dahmani, Louisa
AU - Shen, Lunhao
AU - Zhang, Feng
AU - Li, Shenshen
AU - He, Changgeng
AU - Yin, Yilin
AU - Fu, Xiaoxuan
AU - Huang, Jianting
AU - Long, Yang
AU - Liu, Dantong
AU - Guo, Yi
AU - Liu, Yiming
AU - Xu, Shujun
AU - Meng, Fangang
AU - Zhang, Jianguo
AU - Wang, Danhong
AU - Li, Luming
AU - Liu, Hesheng
TI - Circuit response to neuromodulation characterized with simultaneous deep brain stimulation and precision neuroimaging in humans
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/03/13
VL - 29
IS - 7
SP - 1749
EP - 1761
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02228-w
UR - https://doi.org/10.1038/s41593-026-02228-w
LA - en
ER -

CSL-JSON

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