OSCR

Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia.

Code ↔ Paper

12 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 12 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Statistical evaluation ↔ functions/func_corr_PANSS_CPZ.m, the whole file · a weak match · score 0.82 · possible confounding variables, Spearman correlation, CPZ equivalent dose, PANSS scores, residuals, Pearson
  2. [2] § Materials and methods › Surrogate data testing ↔ functions/func_surrogate_EEG.m, the whole file · a weak match · score 0.78 · phase randomization technique, Fourier transformation, Theiler, surrogate, reconstructed, nonlinear
  3. [3] § Materials and methods › Statistical evaluation ↔ functions/func_corr_RSN_CPZ.m, the whole file · a weak match · score 0.77 · possible confounding variables, Spearman correlation, CPZ equivalent dose, residuals, Pearson, regression
  4. [4] § Materials and methods › Estimating spectral power ↔ functions/func_average_RSN_results_irasa.m, lines 1–16 · score 0.69 · log transformed, oscillatory power, sliding window, fractal power, power spectrum, raw
  5. [5] § Materials and methods › Estimating spectral power ↔ functions/IRASA/amri_sig_fractal.m, lines 55–116 · score 0.69 · Irregular Resampling Auto, oscillatory components, filtering, IRASA, fractal, Spectral
  6. [6] § Materials and methods › EEG recording, quality control and pre-processing ↔ script_00_preproc_ec_mara.m, lines 71–78 · score 0.66 · notch filters, pass filtered, EEGLAB, MARA, 128 Hz
  7. [7] § Materials and methods › Band-limited power analysis ↔ functions/IRASA/Example.m, lines 56–98 · score 0.65 · power law, eyes closed, fractal power spectrum, component, windows, oscillatory
  8. [8] § Materials and methods › Confirmatory analyses ↔ functions/func_average_RSN_results_irasa.m, lines 1–16 · score 0.57 · log transformed, sliding window, spectral power, power spectrum, raw, mixed
  9. [9] § Results › Spectral features and clinical symptoms ↔ functions/func_plot_CPZ_PANSS_corr.m, the whole file · a weak match · score 0.54 · CPZ equivalent dose, PANSS NEG, PANSS scores, correlated
  10. [10] § Materials and methods › Statistical evaluation ↔ functions/func_pairwise_FDR.m, the whole file · a weak match · score 0.53 · Discovery Rate, Benjamini, Hochberg, FDR
  11. [11] § Results › Spectral features and clinical symptoms ↔ functions/func_plot_CPZ_PANSS_corr.m, the whole file · a weak match · score 0.53 · PANSS SUM, PANSS NEG, PANSS scores, Correlation
  12. [12] § Materials and methods › Statistical evaluation ↔ functions/func_corr_PANSS_CPZ.m, the whole file · a weak match · score 0.52 · CPZ equivalent dose, PANSS scores, regressed, confounders, correlation, EEG

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 103 lines · 2.9 KB · no license · 2 matches

  1. function [ output_table ] = func_corr_PANSS_CPZ( input_table, panss, conf_table, flag_conf)
  3. % Function to compute correlations between PANSS scores and CPZ
  4. % equivalent dose, after regressing out the effect of possible confounding
  5. % variables. Analysis is performed on the level of resting-state networks.
  6. %
  7. % Note that this analysis is exploratory, and therefore we did
  8. % not adjust for multiple comparisons.
  9. %
  10. % Author: F. Samuel Racz, The University of Texas at Austin
  11. % email: [email hidden]
  12. % last modified: 04/02/2025
  14. %% preallocations and constants
  16. % if no prior significant difference, return empty
  17. if isempty(input_table)
  18. output_table = [];
  19. return
  20. end
  22. y_cpz = conf_table.cpz; %........CPZ equivalent dose
  23. conf_table_red = removevars(conf_table,'cpz'); %...remove CPZ from confounders
  24. z_conf = table2array(conf_table_red); %.....table of confounding variables
  26. output_struct = struct(...
  27. 'meas', [],...
  28. 'r', [],...
  29. 'p', [],...
  30. 'h', [],...
  31. 'p_FDR', [],...
  32. 'h_FDR', [],...
  33. 'trend', [],...
  34. 'ctype', [],...
  35. 'res_panss', [],...
  36. 'res_cpz', []);
  38. %% compute correlations
  40. % EEG index
  41. v_sz = [input_table.(panss)];
  43. if flag_conf
  44. % regressing out confounders from PANSS
  45. lm_eeg = fitlm(z_conf, v_sz);
  46. res_panss = lm_eeg.Residuals.Raw;
  48. % regressing out confounders from CPZ
  49. lm_cpz = fitlm(z_conf, y_cpz);
  50. res_cpz = lm_cpz.Residuals.Raw;
  51. else
  52. res_panss = v_sz;
  53. res_cpz = y_cpz;
  54. end
  56. % test for normality
  57. if lillietest(res_panss) || lillietest(res_cpz)
  58. % non-normal: Spearman correlation
  59. [r, p] = corr(res_panss, res_cpz, 'type', 'Spearman');
  60. ctype = 'Spearman';
  61. else
  62. % normal: Pearson correlation
  63. [r, p] = corr(res_panss, res_cpz, 'type', 'Pearson');
  64. ctype = 'Pearson';
  65. end
  67. % derive significance
  68. if p<0.05
  69. output_struct.h = 1;
  70. else
  71. output_struct.h = 0;
  72. end
  74. % storing outcomes
  75. output_struct.meas = panss;
  76. output_struct.r = r;
  77. output_struct.p = p;
  78. output_struct.p_FDR = p;
  79. output_struct.h_FDR = 0;
  80. output_struct.trend = sign(r);
  81. output_struct.ctype = {ctype};
  82. output_struct.res_panss = res_panss;
  83. output_struct.res_cpz = res_cpz;
  85. % stucture results
  86. if isempty(output_struct)
  87. output_table = [];
  88. elseif length(output_struct) == 1
  89. output_table = table();
  90. vnames = fieldnames(output_struct);
  91. for i = 1:length(vnames)
  92. if ~contains(vnames{i},{'res_panss','res_cpz'})
  93. output_table.(vnames{i}) = output_struct.(vnames{i});
  94. else
  95. output_table.(vnames{i}) = {output_struct.(vnames{i})};
  96. end
  97. end
  98. else
  99. output_table = struct2table(output_struct);
  100. output_table = sortrows(output_table, 'p', 'ascend');
  101. end
  103. end

func_corr_PANSS_CPZ.m at commit 74fce67, no license · at the source

Overview

Authors: Frigyes Samuel Racz1,2,3, Kinga Farkas4, Melinda Becske4, Hajnalka Molnar4, Zsuzsanna Fodor4, Peter Mukli3,5,6,7, Gabor Csukly4
ORCID iDs: Frigyes Samuel Racz, Kinga Farkas, Peter Mukli
  1. Department of Neurology, The University of Texas at Austin,1601 Trinity St., Austin, 78712 TX USA
  2. Mulva Clinic for the Neurosciences, The University of Texas at Austin,1601 Trinity St., Austin, 78712 TX USA
  3. Department of Physiology, Semmelweis University,37-47 Tuzolto St., Budapest, 1094 Hungary
  4. Department of Psychiatry and Psychotherapy, Semmelweis University,6 Balassa St., Budapest, 1083 Hungary
  5. Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center,112 NE 13th St., Oklahoma City, 73117 OK USA
  6. Department of Neurosurgery, University of Oklahoma Health Sciences Center,1000 N Lincoln Blvd., Oklahoma City, 73104 OK USA
  7. Institute of Preventive Medicine and Public Health, Semmelweis University,4 Nagyvarad Sq., Budapest, 1089 Hungary
Institutions: The University of Texas at Austin (United States); Semmelweis University (Hungary); University of Oklahoma Health Sciences Center (United States)
Journal: Translational psychiatry, volume 16, issue 1, article 315
Dates: received 16 June 2025; accepted 17 April 2026; published online 2 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04055-w · PMID 42069713 · PMCID PMC13280145 · OpenAlex W7159941532
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), schizophrenia / psychosis (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Schizophrenia, Neuroscience
MeSH: Alpha Rhythm*, Beta Rhythm*, Brain*, Schizophrenia*, Adult, Case-Control Studies, Electroencephalography, Female, Humans, Male, Middle Aged (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: This research was supported by the Hungarian Research Foundation Grant (OTKA FK 138385) and Hungarian Research Foundation Grant (OTKA PD 146424), as well as the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA-25 funding scheme
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

Spectral features of the electroencephalogram (EEG) are essential for providing clinically relevant biomarkers in schizophrenia (SZ). Despite literature indicating altered short-scale neural dynamics in SZ, however, band-limited power (BLP) indices are rarely assessed in a time-resolved manner. To address this, here we evaluated static and dynamic BLP indices in a sample of 30 SZ patients and 31 healthy control (HC) individuals. Guided by recent findings on power spectral dynamics in SZ, we estimated total, and decomposed fractal and oscillatory BLP in a sliding window manner from resting-state EEG recordings collected in eyes-closed resting-state. The SZ cohort was characterized by elevated baseline of total fractal power (p = 0.0015, |r| = 0.4073), while its temporal variability was comparable between the two study groups. On the other hand, spectral power in the alpha (p < 10−4, d = 1.0503) and beta (p = 0.0022, |r| = 0.3925) regimes exhibited reduced fluctuation in SZ compared to HC, with no between-group differences in their baselines. Furthermore, alpha variability could be attributed to alterations in isolated oscillatory activity, while variability in beta-BLP over the dorsal attention network was found correlated with negative symptoms in SZ (Spearman r = −0.4994, p = 0.0055). Finally, surrogate data testing indicated altered phase dynamics in SZ as a potential mechanism for diminished BLP fluctuations.

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 12 matches between paragraphs and lines of code.

samuelracz/schizophrenia_BLP_dynamics

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 74fce677ae5c1a111ca256c683c72325893b594e, 21 January 2026
Languages: MATLAB (45)
Size: 205 files, 45 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (20 files), EEGLAB (5 files), Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
46 files
At the source: github.com/samuelracz/schizophrenia_BLP_dynamics

Code availability

All data analyses were carried out via MATLAB (version 2023b) utilizing the EEGLAB toolbox [31] (version 2020.0) along with custom analysis codes. MATLAB codes developed and used for analyzing the data, statistical evaluations (including raw and adjusted p-values, test statistic values and effect size measures) and visualizing outcomes (along with pre-processed EEG) are provided in a public GitHub repository at https://github.com/samuelracz/schizophrenia_BLP_dynamics. Note that this Online Supplement also contains detailed statistical reports exported in tabular format and also as MATLAB workspaces.

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

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;
  • 45 scripts, each with its path and the digest of its content;
  • 12 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

Datasets cited

Data availability

The eyes-closed resting-state data analyzed this study has been made available in a public Zenodo repository titled “Resting-state EEG, clinical, and demographics data from schizophrenia patients and age-matched healthy controls (https://zenodo.org/records/14808296)” at 10.5281/zenodo.14808295.

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, 7 authors, 2 keywords, 11 MeSH terms, 1 funder, 72 references.

Cite

This paper

Racz, F. S., Farkas, K., Becske, M., Molnar, H., Fodor, Z., Mukli, P., & Csukly, G. (2026). Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia. Translational psychiatry, 16(1), 315. https://doi.org/10.1038/s41398-026-04055-w

BibTeX

@article{racz2026diminished,
author = {Racz, Frigyes Samuel and Farkas, Kinga and Becske, Melinda and Molnar, Hajnalka and Fodor, Zsuzsanna and Mukli, Peter and Csukly, Gabor},
title = {{Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia}},
journal = {Translational psychiatry},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {315},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04055-w},
url = {https://doi.org/10.1038/s41398-026-04055-w},
pmid = {42069713},
pmcid = {PMC13280145}
}

RIS

TY - JOUR
AU - Racz, Frigyes Samuel
AU - Farkas, Kinga
AU - Becske, Melinda
AU - Molnar, Hajnalka
AU - Fodor, Zsuzsanna
AU - Mukli, Peter
AU - Csukly, Gabor
TI - Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/05/02
VL - 16
IS - 1
SP - 315
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04055-w
UR - https://doi.org/10.1038/s41398-026-04055-w
LA - en
ER -

CSL-JSON

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