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Divergent disruption of brain networks following total and chronic sleep loss: a longitudinal fMRI study.

Code ↔ Paper

10 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 10 matches
  1. [1] § Materials and Methods › Time series extraction ↔ Scripts/quality_check_FD_code.ipynb, lines 228–338 · score 0.68 · framewise displacement, Quality check, BOLD signal, Power, ROI, brain
  2. [2] § Materials and Methods › Subjective data analysis ↔ Scripts/subjective_behavior_state_traits.ipynb, lines 1–142 · score 0.67 · subjective sleepiness, global graph metrics, global efficiency, graph distance, modularity, mixed
  3. [3] § Materials and Methods › Subjective data analysis ↔ Scripts/subjective_behavior_state_traits.ipynb, lines 1–142 · score 0.63 · subjective behavioral, subjective sleepiness, objective graph, trait, baseline
  4. [4] § Materials and Methods › Statistical analyses › Global and nodal metrics comparisons ↔ Scripts/Graphs_nodal_global_metrics_HDI_CCML.ipynb, lines 714–810 · score 0.62 · unpaired permutation, global metrics, robustness, shuffled, LMMs, FDR
  5. [5] § Materials and Methods › Covariate-constraint manifold learning ↔ Scripts/Graphs_nodal_global_metrics_HDI_CCML.ipynb, lines 2931–3051 · score 0.62 · classical ISOMAP, global metrics, embeddings, manifold, CCML, covariates
  6. [6] § Results › Nodal graph metrics ↔ Scripts/Graphs_nodal_global_metrics_HDI_CCML.ipynb, lines 3399–3440 · score 0.60 · III VI, Thalamus, Vermis, VII, FPN, limbic
  7. [7] § Materials and Methods › Subjective data analysis ↔ Scripts/Graphs_nodal_global_metrics_HDI_CCML.ipynb, lines 527–586 · score 0.58 · linear mixed, global efficiency, graph distance, modularity, model, clustering
  8. [8] § Materials and Methods › Subjective data analysis ↔ Scripts/subjective_behavior_state_traits.ipynb, lines 145–255 · score 0.58 · global graph metric, HC3, scored, OLS, trait, PSQI
  9. [9] § Results › Nodal graph metrics ↔ Scripts/Graphs_nodal_global_metrics_HDI_CCML.ipynb, lines 3399–3440 · score 0.57 · III VI, Heschl, Precuneus, parietal, Angular, DMN
  10. [10] § Materials and Methods › Time series extraction ↔ Scripts/quality_check_FD_code.ipynb, lines 228–338 · score 0.56 · temporal derivatives, motion parameters, outlier, signal, zero, regression

Paper

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The authors' code

Jupyter notebook · 3,559 lines · 167 KB · no license · 5 matches

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It can be read at the source: Scripts/Graphs_nodal_global_metrics_HDI_CCML.ipynb.

Overview

Authors: Patrycja Scislewska1, Arturo Cabrera Vazquez2,3,4, Iwona Szatkowska5, Halszka Kontrymowicz-Ogińska6, Sophie Achard2, Aleksandra Domagalik7
ORCID iDs: Patrycja Scislewska, Arturo Cabrera Vazquez, Iwona Szatkowska, Sophie Achard, Aleksandra Domagalik
  1. University of Warsaw, Faculty of Biology, Institute of Experimental Zoology, Warsaw, Poland
  2. Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France
  3. Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
  4. INSERM U1214, Toulouse Neuroimaging Center, CHU Purpan, 31059 Toulouse, France
  5. Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 02-093 Warsaw, Poland
  6. Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland
  7. Centre for Brain Research, Jagiellonian University, 31-501 Kraków, Poland
Institutions: University of Warsaw (Poland); Institut polytechnique de Grenoble (France); Centre National de la Recherche Scientifique (France); Inserm (France); Grenoble Institute of Neurosciences (France); Toulouse NeuroImaging Center (France); Université Grenoble Alpes (France); Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (Poland); Polish Academy of Sciences (Poland); Jagiellonian University (Poland)
Journal: Sleep, volume 49, issue 5, article zsag030
Dates: received 20 October 2025; accepted 29 January 2026; published online 3 February 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/sleep/zsag030 · PMID 41631633 · PMCID PMC13163182 · OpenAlex W7127334101
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Statistics, Machine learning, Connectivity, Preprocessing, Spectral & time-frequency, Graphs, fMRI & imaging
Keywords: fMRI, sleep deprivation, graphs, functional connectivity, hub disruption index, machine learning, sleepiness, chronotype
MeSH: Brain*, Magnetic Resonance Imaging*, Nerve Net*, Sleep Deprivation*, Adult, Brain Mapping, Female, Humans, Longitudinal Studies, Male, Wakefulness, Young Adult (* major topic)
Topic: Sleep and Work-Related Fatigue (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Excellence Initiative-Research University (BOB-IDUB-622-412/2025, 2020-2026); Inria and including CNRS; Agence Nationale de la Recherche under the France 2030 program (ANR-23-IACL-0006); French government grant; Ministry of Science and Higher Education; Polish National Science Centre (2018/29/B/HS6/01934)
Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

Study Objectives: Sleep loss significantly disrupts cognitive and emotional functioning, yet the neural consequences of different types of sleep deprivation remain unclear.

Methods: In a within-subject resting-state functional magnetic resonance imaging study, we examined how acute total sleep deprivation (TSD) and chronic sleep restriction (CSR) alter intrinsic functional brain organization in 28 healthy adults scanned under three conditions: rested wakefulness (RW), after one night of TSD, and after five nights of CSR.

To quantify network-level disruption, we applied graph-theoretical analyses, including a novel within-subject adaptation of the Hub Disruption Index and Covariate-Constrained Manifold Learning (CCML), an unsupervised embedding technique sensitive to subject-level covariates. Moreover, we assessed subjective sleep quality, sleepiness, and circadian traits.

Results: Both TSD and CSR were associated with a consistent reorganization of graph topology relative to RW. Furthermore, direct comparisons revealed that TSD and CSR affect different brain hubs. Regional changes in degree, closeness, and clustering coefficients were most prominent in subsystems of the default mode network, frontoparietal network, and cerebellum. These differences were also captured in CCML embeddings, supporting the hypothesis that acute and chronic sleep deprivation exert divergent effects on brain connectivity. Findings were robust across graph thresholds, brain atlases, and nodal metrics. Moreover, these results were further supported by subjective measures—sleepiness was associated with reduced network integration in RW, and circadian phenotype emerged as a key determinant of individual sensitivity to sleep loss.

Conclusions: Our results show that TSD and CSR induce divergent alterations in brain functional organization, offering new insights into their neural impact.

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 10 matches between paragraphs and lines of code.

PatrycjaScislewska/sleep_deprivation_graphs

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: af790e173734b687d184ee821b8c2b63bf8947ab, 20 November 2025
Languages: Jupyter (3)
Size: 183 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), statsmodels (3 files), Matplotlib (2 files), pandas (2 files), SciPy (2 files), NetworkX (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit af790e1, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/PatrycjaScislewska/sleep_deprivation_graphs/

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;
  • 3 scripts, each with its path and the digest of its content;
  • 10 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 availability

Time series, atlases and code used in this study are available in the following GitHub repository: https://github.com/PatrycjaScislewska/sleep_deprivation_graphs/

All preprocessing steps can be fully reproduced using the following GitHub repository: https://github.com/veronicamunoz/rs_graph_processing

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 8 keywords, 12 MeSH terms, 6 funders, 80 references.

Cite

This paper

Scislewska, P., Cabrera Vazquez, A., Szatkowska, I., Kontrymowicz-Ogińska, H., Achard, S., & Domagalik, A. (2026). Divergent disruption of brain networks following total and chronic sleep loss: a longitudinal fMRI study. Sleep, 49(5), zsag030. https://doi.org/10.1093/sleep/zsag030

BibTeX

@article{scislewska2026divergent,
author = {Scislewska, Patrycja and Cabrera Vazquez, Arturo and Szatkowska, Iwona and Kontrymowicz-Ogińska, Halszka and Achard, Sophie and Domagalik, Aleksandra},
title = {{Divergent disruption of brain networks following total and chronic sleep loss: a longitudinal fMRI study}},
journal = {Sleep},
year = {2026},
month = may,
volume = {49},
number = {5},
pages = {zsag030},
publisher = {Oxford University Press},
issn = {0161-8105},
doi = {10.1093/sleep/zsag030},
url = {https://doi.org/10.1093/sleep/zsag030},
pmid = {41631633},
pmcid = {PMC13163182}
}

RIS

TY - JOUR
AU - Scislewska, Patrycja
AU - Cabrera Vazquez, Arturo
AU - Szatkowska, Iwona
AU - Kontrymowicz-Ogińska, Halszka
AU - Achard, Sophie
AU - Domagalik, Aleksandra
TI - Divergent disruption of brain networks following total and chronic sleep loss: a longitudinal fMRI study
T2 - Sleep
J2 - Sleep
PY - 2026
DA - 2026/05/01
VL - 49
IS - 5
SP - zsag030
SN - 0161-8105
PB - Oxford University Press
DO - 10.1093/sleep/zsag030
UR - https://doi.org/10.1093/sleep/zsag030
LA - en
ER -

CSL-JSON

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