Impaired sleep resilience underlies transient neural instability in insomnia disorder.

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] § STAR★Methods › Quantification and statistical analysis › Containment: Cross frequency gating and pattern diversity ↔ ERPAC.py, lines 8 to 60 · score 0.68 · theta phase, 4–8 Hz, Event related, beta, circular, channel · Report a mismatch
[2] § STAR★Methods › Quantification and statistical analysis › Containment: Cross frequency gating and pattern diversity ↔ tensorpac/methods/meth_erpac.py, lines 42 to 82 · score 0.64 · linear correlation, Event related phase, amplitude coupling, phase amplitude, circular, ERPAC · Report a mismatch

Paper

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The authors' code

Python · 77 lines · 2.7 KB · no license · 1 match

import os
import numpy as np
import mne
from tensorpac import EventRelatedPac
from scipy.io import savemat
def compute_erpac_for_all_channel_pairs(epochs, base_dir, source_filename):
erpac_dir = os.path.join(base_dir, "ERPAC")
if not os.path.exists(erpac_dir):
os.makedirs(erpac_dir)
print(f"创建目录: {erpac_dir}")
data = 10e6 * epochs.get_data() # (n_epochs, n_channels, n_times)
sfreq = epochs.info['sfreq']
n_channels = data.shape[1]
ch_names = epochs.info['ch_names']
erpac_types = [
{"name": "theta_phase_beta_amp", "f_pha": [4, 8], "f_amp": [12, 30]}
]
for erpac_type in erpac_types:
print(f"计算ERPAC类型: {erpac_type['name']}")
erpac_matrix = np.zeros((n_channels, n_channels))
for i in range(n_channels):
for j in range(n_channels):
phase_data = data[:, i, :] # (n_epochs, n_times)
amp_data = data[:, j, :] # (n_epochs, n_times)
rp_obj = EventRelatedPac(
f_pha=erpac_type['f_pha'],
f_amp=erpac_type['f_amp']
)
erpac = rp_obj.filterfit(
sfreq,
phase_data,
amp_data,
method='circular',
n_perm=None
)
[a, b] = [xxx, xxx] ## Replace with the concerned time window e.g. [0,200]、[200,400]、[400,600](P300)、[600,1000]
t1 = int(sfreq/1000 * a)
t2 = int(sfreq/1000 * b)
mean_erpac = np.mean(erpac[:,:,t1:t2+1])
erpac_matrix[i, j] = mean_erpac
result_dict = {
'erpac_matrix': erpac_matrix,
'phase_freq': erpac_type['f_pha'],
'amp_freq': erpac_type['f_amp'],
'ch_names': ch_names,
'sfreq': sfreq
}
filename = f"{erpac_type['name']}_erpac_{source_filename}.mat"
save_path = os.path.join(erpac_dir, filename)
savemat(save_path, result_dict)
print(f"保存ERPAC结果到: {save_path}")
base_dirs = [
r'Replace your data path',
r'Replace your data path',
r'Replace your data path'
]
for base_dir in base_dirs:
set_files = [f for f in os.listdir(base_dir) if f.endswith('.set')]
for file in set_files:
file_path = os.path.join(base_dir, file)
print(f"处理文件: {file_path}")
epochs = mne.io.read_epochs_eeglab(file_path)
source_filename = os.path.splitext(file)[0]
compute_erpac_for_all_channel_pairs(epochs, base_dir, source_filename)

ERPAC.py at commit b5201e2, no license · at the source

Overview

Authors: Chao Yang¹, Hai Gu¹, He Chen², Guohua Chen³, Xinhua Song⁴, Xin Liu³, Qing Yang³, Guang Wang⁴, Cancheng Li⁵, Heng Gu⁶, Shaodi Wang⁷, Peiyang Guo¹, Jiawei Zhao¹, Lu Wang¹, Rui Wang¹, Changming Wang^8,9,10, Junhua Mei³, Xiaoli Li¹

State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing 100875, China
School of Automation Science and Engineering, South China University of Technology, Guangzhou 511442, China
Department of Neurology, Wuhan First Hospital, Wuhan 430022, China
Clinical College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
Ningbo Institute of Dalian University of Technology, Ningbo 315014, China
Department of Anesthesiology, Peking University Third Hospital, Beijing 100191, China
Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China
College of Biomedical Engineering, Capital Medical University, Beijing 100069, China
Jingtong Brain Research Laboratory, Beijing 101100, China

Institutions: Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning (China); Dalian University of Technology (China); South China University of Technology (China); Hubei University of Chinese Medicine (China); Beihang University (China); Peking University Third Hospital (China); Capital Medical University (China); Beijing Luhe Hospital Affiliated to Capital Medical University (China)

Journal: iScience, volume 29, issue 6, article 116151

Dates: received 19 November 2025; accepted 9 April 2026; published online 4 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.116151 · PMID 42305591 · PMCID PMC13266136 · OpenAlex W7163557116

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: human (organism), sleep disorders (population), clinical / translational (subfield)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Complexity, fMRI & imaging, Single-unit activity, calcium imaging, Connectivity

Keywords: Health sciences, Medicine, Neurology

Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Natural Science Foundation of Hubei Province (2025AFC109); National Natural Science Foundation of China (62271331); Department of Science and Technology of Hubei Province; Ministry of Science and Technology of the People's Republic of China (2021ZD0204300); Beijing Natural Science Foundation (22Z30074); National Science and Technology Major Project

Citations: not cited yet (Europe PMC); 46 references in the paper

Research resources: RStudio RRID:SCR_000432, MATLAB R2023a RRID:SCR_001622, R 4.5.0 RRID:SCR_001905, EEGLAB RRID:SCR_007292, PyCharm RRID:SCR_018221, NeuroKit2 RRID:SCR_021889, Lavaan RRID:SCR_027663

Abstract

This study quantified sleep resilience in insomnia disorder (ID) by examining the brain’s ability to resist, contain, and recover from auditory perturbations during nocturnal sleep. 58 ID patients and 43 healthy controls underwent real-time staged nocturnal recordings with event-locked auditory stimulation. Neural responses were evaluated using evoked complexity, P300, event-related phase-amplitude coupling (ERPAC), and coupling pattern diversity. Compared with controls, ID patients showed elevated evoked complexity and enlarged P300 responses, especially in N3 sleep, indicating reduced resistance to disturbance. ID patients also exhibited stronger fronto-parietal ERPAC during the middle post-stimulus window, suggesting impaired containment. These abnormalities weakened in the late window, consistent with relatively preserved recovery. In conclusion, ID is marked by impaired sleep resilience, particularly in deep sleep, and sleep resilience may provide useful objective markers of nocturnal instability.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Zenodo 20070128

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data and code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: MNE-Python (3 files), NumPy (3 files), SciPy (3 files), NeuroKit2 (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

3 files

Complexity.py, Python, 86 lines
ERPAC.py, Python, 77 lines
ERPAC_complexity.py, Python, 102 lines

At the source: doi.org/10.5281/zenodo.20070128

guhai123/Impaired-sleep-resilience-underlies-transient-neural-instability-in-insomnia-disorder

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: b5201e22c35350a72f4f502c929f20fe0ee14bb1, 20 April 2026

Languages: Python (3)

Size: 3 files, 3 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: MNE-Python (3 files), NumPy (3 files), SciPy (3 files), NeuroKit2 (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

3 files

Complexity.py, Python, 86 lines
ERPAC.py, Python, 77 lines, 1 match
ERPAC_complexity.py, Python, 102 lines

At the source: github.com/guhai123/Impaired-sleep-resilience-underlies-transient-neural-instability-in-insomnia-disorder

EtienneCmb/tensorpac

License: BSD-3-Clause

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: ac9058fd375d423757943810f613d63785fab85f, 8 February 2023

Languages: Python (61), Shell (1)

Size: 121 files, 62 scripts

Software Heritage: not archived

Found in: the resources table

Holds: README, license file, CITATION.cff, environment (setup.cfg, setup.py), tests, continuous integration, documentation

Tools: NumPy (39 files), Matplotlib (37 files), seaborn (14 files), SciPy (12 files), pandas (5 files), MNE-Python (4 files), statsmodels (2 files), Numba (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

64 files

build_install.sh, Shell, 3 lines
docs/source/conf.py, Python, 225 lines
examples/erpac/plot_erpac.py, Python, 76 lines
examples/erpac/plot_erpac_stats.py, Python, 67 lines
examples/misc/plot_align_tf_pha_peak.py, Python, 72 lines
examples/misc/plot_artificially_coupled_signals.py, Python, 51 lines
examples/misc/plot_binned_amplitude.py, Python, 38 lines
examples/misc/plot_fmin_fmax_optmization.py, Python, 34 lines
examples/misc/plot_frequency_vectors.py, Python, 58 lines
examples/misc/plot_itc.py, Python, 74 lines
examples/misc/plot_preferred_phase.py, Python, 85 lines
examples/misc/plot_psd.py, Python, 30 lines
examples/pac/plot_compare_filtering.py, Python, 47 lines
examples/pac/plot_compare_normalizations.py, Python, 74 lines
examples/pac/plot_compare_surrogates.py, Python, 78 lines
examples/pac/plot_long_range_coupling.py, Python, 60 lines
examples/pac/plot_pac_methods.py, Python, 65 lines
examples/pac/plot_readme.py, Python, 33 lines
examples/stats/plot_compare_cond_cluster.py, Python, 92 lines
examples/stats/plot_compare_mcp.py, Python, 73 lines
examples/stats/plot_pvalues.py, Python, 84 lines
examples/stats/plot_stats_stationary.py, Python, 74 lines
examples/tuto/plot_real_data.py, Python, 417 lines
paper/manuscript/code/fig_1_pac_process.py, Python, 127 lines
paper/manuscript/code/fig_2_rnd_signals.py, Python, 77 lines
paper/manuscript/code/fig_3_compare_pac.py, Python, 63 lines
paper/manuscript/code/fig_4_pac_corrected.py, Python, 64 lines
paper/manuscript/code/fig_5_erpac.py, Python, 52 lines
paper/manuscript/code/fig_6_pp.py, Python, 65 lines
paper/manuscript/code/fig_7_fopt.py, Python, 64 lines
paper/manuscript/code/fig_8_st_vs_tensors.py, Python, 96 lines
paper/reviews/code/r1_ndpac_threshold.py, Python, 84 lines
paper/reviews/code/r1_zscore_amp_ndpac.py, Python, 62 lines
paper/reviews/code/r2_numba_performance.py, Python, 100 lines
paper/reviews/code/r3_erpac_fn_fp.py, Python, 61 lines
paper/reviews/code/r3_functional_pac.py, Python, 63 lines
paper/reviews/code/r3_pactools_vs_tensorpac.py, Python, 133 lines
setup.py, Python, 57 lines
tensorpac/__init__.py, Python, 17 lines
tensorpac/config.py, Python, 27 lines
tensorpac/gcmi.py, Python, 105 lines
tensorpac/io.py, Python, 170 lines
tensorpac/methods/__init__.py, Python, 12 lines
tensorpac/methods/meth_erpac.py, Python, 158 lines, 1 match
tensorpac/methods/meth_pac.py, Python, 222 lines
tensorpac/methods/meth_pac_nb.py, Python, 267 lines
tensorpac/methods/meth_pp.py, Python, 42 lines
tensorpac/methods/meth_surrogates.py, Python, 168 lines
tensorpac/methods/meth_switch.py, Python, 98 lines
tensorpac/methods/tests/test_pac_methods.py, Python, 114 lines
tensorpac/pac.py, Python, 887 lines
tensorpac/signals.py, Python, 150 lines
tensorpac/spectral.py, Python, 230 lines
tensorpac/stats.py, Python, 76 lines
tensorpac/tests/test_gcmi.py, Python, 28 lines
tensorpac/tests/test_io.py, Python, 42 lines
tensorpac/tests/test_pac.py, Python, 281 lines
tensorpac/tests/test_signals.py, Python, 25 lines
tensorpac/tests/test_stats.py, Python, 19 lines
tensorpac/tests/test_utils.py, Python, 79 lines
tensorpac/utils.py, Python, 744 lines
tensorpac/visu.py, Python, 436 lines
LICENSE, License, 29 lines
README.rst, Text, 109 lines

At the source: github.com/EtienneCmb/tensorpac

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:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
68 scripts, each with its path and the digest of its content;
2 matches 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 and code availability

All data reported in this paper will be shared by the lead contact upon request.

All original code used for the analyses reported in this paper has been deposited at Zenodo: https://doi.org/10.5281/zenodo.20070128 and is publicly available as of the date of publication. The corresponding source repository is available at GitHub: https://github.com/guhai123/Impaired-sleep-resilience-underlies-transient-neural-instability-in-insomnia-disorder.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 3 keywords, 6 funders, 46 references, 7 RRIDs.

Cite

This paper

Yang, C., Gu, H., Chen, H., Chen, G., Song, X., Liu, X., Yang, Q., Wang, G., Li, C., Gu, H., Wang, S., Guo, P., Zhao, J., Wang, L., Wang, R., Wang, C., Mei, J., & Li, X. (2026). Impaired sleep resilience underlies transient neural instability in insomnia disorder. iScience, 29(6), 116151. https://doi.org/10.1016/j.isci.2026.116151

BibTeX

@article{yang2026impaired, author = {Yang, Chao and Gu, Hai and Chen, He and Chen, Guohua and Song, Xinhua and Liu, Xin and Yang, Qing and Wang, Guang and Li, Cancheng and Gu, Heng and Wang, Shaodi and Guo, Peiyang and Zhao, Jiawei and Wang, Lu and Wang, Rui and Wang, Changming and Mei, Junhua and Li, Xiaoli}, title = {{Impaired sleep resilience underlies transient neural instability in insomnia disorder}}, journal = {iScience}, year = {2026}, month = jun, volume = {29}, number = {6}, pages = {116151}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116151}, url = {https://doi.org/10.1016/j.isci.2026.116151}, pmid = {42305591}, pmcid = {PMC13266136} }

RIS

TY - JOUR AU - Yang, Chao AU - Gu, Hai AU - Chen, He AU - Chen, Guohua AU - Song, Xinhua AU - Liu, Xin AU - Yang, Qing AU - Wang, Guang AU - Li, Cancheng AU - Gu, Heng AU - Wang, Shaodi AU - Guo, Peiyang AU - Zhao, Jiawei AU - Wang, Lu AU - Wang, Rui AU - Wang, Changming AU - Mei, Junhua AU - Li, Xiaoli TI - Impaired sleep resilience underlies transient neural instability in insomnia disorder T2 - iScience J2 - iScience PY - 2026 DA - 2026/06/04 VL - 29 IS - 6 SP - 116151 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116151 UR - https://doi.org/10.1016/j.isci.2026.116151 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.isci.2026.116151", "type": "article-journal", "title": "Impaired sleep resilience underlies transient neural instability in insomnia disorder", "container-title": "iScience", "author": [ { "family": "Yang", "given": "Chao" }, { "family": "Gu", "given": "Hai" }, { "family": "Chen", "given": "He" }, { "family": "Chen", "given": "Guohua" }, { "family": "Song", "given": "Xinhua" }, { "family": "Liu", "given": "Xin" }, { "family": "Yang", "given": "Qing" }, { "family": "Wang", "given": "Guang" }, { "family": "Li", "given": "Cancheng" }, { "family": "Gu", "given": "Heng" }, { "family": "Wang", "given": "Shaodi" }, { "family": "Guo", "given": "Peiyang" }, { "family": "Zhao", "given": "Jiawei" }, { "family": "Wang", "given": "Lu" }, { "family": "Wang", "given": "Rui" }, { "family": "Wang", "given": "Changming" }, { "family": "Mei", "given": "Junhua" }, { "family": "Li", "given": "Xiaoli" } ], "container-title-short": "iScience", "volume": "29", "issue": "6", "page": "116151", "DOI": "10.1016/j.isci.2026.116151", "PMID": "42305591", "PMCID": "PMC13266136", "ISSN": "2589-0042", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.isci.2026.116151", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 4 ] ] } }

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Impaired sleep resilience underlies transient neural instability in insomnia disorder.

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Zenodo 20070128

guhai123/Impaired-sleep-resilience-underlies-transient-neural-instability-in-insomnia-disorder

EtienneCmb/tensorpac

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