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Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia.

Code ↔ Paper

18 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 18 matches · 13 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Resting-state fMRI post-processing ↔ utils/bandpass_filtering.m, the whole file · a weak match · score 0.84 · bandpass filtered, zero phase, attenuation, passband, ripple, stopband
  2. [2] § Methods › Amplitude imbalance dynamics › Impact of transition probabilities on Markov chain convergence and entropy rate ↔ dynamics/utils/compute_markov_sensitivity.m, the whole file · a weak match · score 0.80 · perturbation magnitude, perturbed transition, entropy rate, spectral gap, logarithmic, Markov
  3. [3] § Methods › Amplitude imbalance dynamics › Impact of transition probabilities on Markov chain convergence and entropy rate ↔ dynamics/utils/compute_markov_sensitivity.m, the whole file · a weak match · score 0.73 · aggregated transition matrix, Markov chain, entropy rates, renormalizing, rows, sum
  4. [4] § Methods › Amplitude imbalance dynamics › Convergence analysis ↔ dynamics/utils/compute_markov_dynamics.m, the whole file · a weak match · score 0.69 · variation distance, spectral gap, stationary distribution, TV, tolerance, chain
  5. [5] § Methods › Amplitude imbalance dynamics › Impact of transition probabilities on Markov chain convergence and entropy rate ↔ dynamics/utils/markovEntropyRate.m, the whole file · a weak match · score 0.67 · entropy rates, Markov chain, transition matrix, rows, probability, sum
  6. [6] § Methods › Amplitude imbalance dynamics › Markov chain & stationary distribution ↔ dynamics/utils/markovEntropyRate.m, the whole file · a weak match · score 0.62 · transition probability matrix, Markov chain, stationary distribution, vector
  7. [7] § Methods › Amplitude imbalance dynamics › Markov chain & stationary distribution ↔ dynamics/utils/compute_stationary_dist.m, the whole file · a weak match · score 0.62 · transition probability matrix, Markov chain, stationary distribution, vector
  8. [8] § Methods › Resting-state fMRI post-processing ↔ utils/post_processing.m, lines 1–41 · score 0.61 · 0.01–0.15 Hz, despiking, quality, detrending, post, filtered
  9. [9] § Results › Whole-brain nDTW reveals widespread amplitude divergence with symptom-specific circuit signatures in schizophrenia ↔ high_frequency/example_null_model.m, lines 5–25 · score 0.59 · 0.01–0.15 Hz, 0.01–0.198 Hz, frequency bands, pipeline, 0.01 Hz
  10. [10] § Results › Whole-brain nDTW reveals widespread amplitude divergence with symptom-specific circuit signatures in schizophrenia ↔ high_frequency/example_statistical_effect.m, lines 5–25 · score 0.59 · 0.01–0.15 Hz, 0.01–0.198 Hz, frequency bands, pipeline, 0.01 Hz
  11. [11] § Methods › Amplitude imbalance dynamics › Convergence analysis ↔ dynamics/example_dynamics.m, lines 110–177 · score 0.58 · spectral gap, stationary distribution, variation, TV, tolerance, probability
  12. [12] § Methods › Validation of high fMRI frequency results › Null hypothesis test ↔ high_frequency/utils/pr_null_model.m, the whole file · a weak match · score 0.57 · phase randomization, generates surrogate, PR, validation
  13. [13] § Methods › Dynamic time warping as a measure of amplitude disparity ↔ dtw_framework/dtw_custom.m, lines 1–77 · score 0.54 · optimal warping, cost matrix, window, distance, Dynamic
  14. [14] § Methods › Group difference and clinical association analyses ↔ cognitive_associations/utils/compute_glm_associations.m, the whole file · a weak match · score 0.54 · clinical scores, Poisson, GLM, age, sex, covariate
  15. [15] § Methods › Amplitude imbalance dynamics › Markov chain & stationary distribution ↔ dynamics/utils/checkIsErgodic.m, the whole file · a weak match · score 0.53 · Markov chain, aperiodic, irreducible, finite, ergodic
  16. [16] § Methods › Amplitude imbalance dynamics › Markov chain & stationary distribution ↔ dynamics/utils/compute_stationary_dist.m, the whole file · a weak match · score 0.53 · transition probability matrices, Markov chain, stationary, dynamics
  17. [17] § Methods › Group difference and clinical association analyses ↔ group_analysis/utils/compute_glm_group_difference.m, the whole file · a weak match · score 0.52 · frame displacement, GLM, models, age, sex, covariate
  18. [18] § Methods › Allowable window size for fMRI application ↔ utils/bandpass_filtering.m, the whole file · a weak match · score 0.51 · filter design, Butterworth, mitigated, cutoff, signal

Paper

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

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The authors' code

MATLAB · 48 lines · 1.4 KB · no license · 2 matches

  1. function filteredSignal = bandpass_filtering(signal, Tr, band, cutoff)
  2. % bandpass_filtering - Applies a Butterworth bandpass filter to a time series.
  3. %
  4. % Syntax:
  5. % filteredSignal = bandpass_filtering(Tr, band, signal)
  6. %
  7. % Inputs:
  8. % signal - Input time series (vector).
  9. % Tr - Sampling interval (seconds).
  10. % band - Two-element vector specifying the [low high] passband frequencies (Hz).
  11. % cutoff - Two-element vector specifying the factor of passband.
  13. %
  14. % Optional Inputs:
  15. % pad_size - Number of padding points to mitigate edge effects (default: 100).
  16. %
  17. % Output:
  18. % filteredSignal - The bandpass-filtered time series.
  19. %
  20. % Example:
  21. % filteredSignal = bandpass_filtering(1, [0.01 0.1], randn(200,1));
  22. %
  23. % Author: Sir-Lord
  25. % Default padding size (if required for further processing)
  26. if nargin < 5
  27. pad_size = 100;
  28. end
  30. % Define sampling and Nyquist frequencies
  31. Fs = 1 / Tr;
  32. nyquist = Fs / 2;
  34. % Normalize the passband and define a wider stopband for robust filtering
  35. Wp = band / nyquist;
  36. Ws = [band(1)*cutoff(1) band(2)*cutoff(2)]/nyquist;
  38. % Define filter design parameters
  39. Rp = 3; % Passband ripple (dB)
  40. Rs = 30; % Stopband attenuation (dB)
  42. % Design a Butterworth bandpass filter
  43. [n, Wn] = buttord(Wp, Ws, Rp, Rs);
  44. [b, a] = butter(n, Wn, 'bandpass');
  46. % Apply zero-phase filtering to avoid phase distortion
  47. filteredSignal = filtfilt(b, a, signal);
  48. end

bandpass_filtering.m at commit eb9c368, no license · at the source

Overview

Authors: Sir-Lord Wiafe1, Spencer Kinsey1, Najme Soleimani1, Raymond O Nsafoa2, Nigar Khasayeva1, Amritha Harikumar1, Robyn Miller1, Vince D Calhoun1
ORCID iDs: Sir-Lord Wiafe, Spencer Kinsey, Vince D Calhoun
  1. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303 USA
  2. Kwame Nkrumah University of Science and Technology (KNUST) Hospital, Kumasi, 00233 Ghana
Institutions: Georgia Institute of Technology (United States); Emory University (United States); Georgia State University (United States); Center for Translational Research in Neuroimaging and Data Science (United States); Kwame Nkrumah University of Science and Technology (Ghana)
Journal: Translational psychiatry, volume 16, issue 1, article 429
Dates: received 19 December 2025; accepted 6 July 2026; published online 10 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04278-x · PMID 42431898 · PMCID PMC13503884 · OpenAlex W7167902188
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), schizophrenia / psychosis (population)
Methods: Spectral & time-frequency, Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: Neuroscience, Schizophrenia
MeSH: Brain*, Cognition*, Schizophrenia*, Schizophrenic Psychology*, Adult, Connectome, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (NSF) (2112455)
Citations: not cited yet (Europe PMC); 72 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

Sirlord-Sen/time_resolved_dynamic_time_warping

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: eb9c368f24731d4a67e93e409ffffb9b7f869fb2, 4 December 2025
Languages: MATLAB (42)
Size: 44 files, 42 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 (16 files), Signal Processing Toolbox (5 files), GIFT (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
43 files
At the source: github.com/Sirlord-Sen/time_resolved_dynamic_time_warping

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41398-026-04278-x.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 42 scripts, each with its path and the digest of its content;
  • 18 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 statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41398-026-04278-x.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 10 MeSH terms, 1 funder, 60 references.

Cite

This paper

Wiafe, S.-L., Kinsey, S., Soleimani, N., Nsafoa, R. O., Khasayeva, N., Harikumar, A., Miller, R., & Calhoun, V. D. (2026). Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia. Translational psychiatry, 16(1), 429. https://doi.org/10.1038/s41398-026-04278-x

BibTeX

@article{wiafe2026aberrant,
author = {Wiafe, Sir-Lord and Kinsey, Spencer and Soleimani, Najme and Nsafoa, Raymond O and Khasayeva, Nigar and Harikumar, Amritha and Miller, Robyn and Calhoun, Vince D},
title = {{Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia}},
journal = {Translational psychiatry},
year = {2026},
month = jul,
volume = {16},
number = {1},
pages = {429},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04278-x},
url = {https://doi.org/10.1038/s41398-026-04278-x},
pmid = {42431898},
pmcid = {PMC13503884}
}

RIS

TY - JOUR
AU - Wiafe, Sir-Lord
AU - Kinsey, Spencer
AU - Soleimani, Najme
AU - Nsafoa, Raymond O
AU - Khasayeva, Nigar
AU - Harikumar, Amritha
AU - Miller, Robyn
AU - Calhoun, Vince D
TI - Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/07/10
VL - 16
IS - 1
SP - 429
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04278-x
UR - https://doi.org/10.1038/s41398-026-04278-x
LA - en
ER -

CSL-JSON

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"container-title": "Translational psychiatry",
"author": [
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"family": "Wiafe",
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