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Multiscale heterogeneity of atypical functional connectivity in autism.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Mapping FC ↔ mixtureModelNormalize.m, the whole file · a weak match · score 0.72 · Gaussian gamma mixture, noise, correlations, matrices, modeling, connectivity
  2. [2] § Results › FC deviations are heterogeneous at the connection level ↔ region_overlap.m, lines 1–109 · score 0.58 · extreme FC deviations, normative modeling, deviation degree, permutations, AUC, matrix
  3. [3] § Results › Heterogeneous connection-level deviations converge on common brain regions ↔ region_overlap.m, lines 1–109 · score 0.51 · extreme FC deviations, deviation degree, curve, AUC, overlap, thresholds

Paper

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

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The authors' code

MATLAB · 133 lines · 4.7 KB · no license · 2 matches

  1. % Region-level overlap analysis of extreme FC deviations (|Z| > thr).
  2. % For each region, count how many edges carry an extreme deviation
  3. % (the region's "deviation degree"). For each degree threshold j = 1..t,
  4. % compute the proportion of subjects with degree >= j, integrate across
  5. % thresholds (AUC), and test the ASD - TD AUC difference against a
  6. % label-shuffling null with FDR.
  7. %
  8. % This demo uses random Z-scores so the script runs standalone. Swap the
  9. % synthetic block for your own normative model output to use on real data.
  11. clear; clc; rng(42);
  13. % Parameters
  14. nRoi = 390;
  15. K = nRoi;
  16. thr = 2.3;
  17. t = 20; % max deviation-degree threshold
  18. nP = 10000;
  20. % --- Synthetic data (replace with real Z-scores) -------------------------
  21. % In the real pipeline z_asd and z_td come from a GPR normative model
  22. % (PCNtoolkit);
  23. n_asd = 759;
  24. n_td = 504;
  25. nEdges = nRoi * (nRoi - 1) / 2;
  27. z_asd = randn(n_asd, nEdges);
  28. z_td = randn(n_td, nEdges);
  29. % ------------------------------------------------------------------------
  31. % For each subject and region, count extreme positive / negative
  32. % deviations to that region (nodal degree of the binarised
  33. % deviation adjacency matrix).
  34. [degree_pos_asd, degree_neg_asd] = compute_deviation_degree(z_asd, thr, nRoi);
  35. [degree_pos_td, degree_neg_td] = compute_deviation_degree(z_td, thr, nRoi);
  37. % Stack groups into a single [Nsubs x nRoi] matrix.
  38. % The "- 1" shift lets the j = 1..t threshold loop below evaluate "strictly
  39. % more than one extreme edge" starting from j = 1.
  40. z_idx = logical([ones(n_asd,1); zeros(n_td,1)]);
  41. degree_pos_orig = [degree_pos_asd - 1, degree_pos_td - 1]';
  42. degree_neg_orig = [degree_neg_asd - 1, degree_neg_td - 1]';
  43. Nsubs = size(degree_pos_orig, 1);
  45. cnt_neg = zeros(1, K);
  46. cnt_pos = zeros(1, K);
  48. % Permutation test. First iteration is the observed labelling; the rest
  49. % shuffle subjects across the combined stack.
  50. tic
  51. for p = 1:nP
  52. if p == 1
  53. idx = 1:Nsubs;
  54. else
  55. idx = randperm(Nsubs);
  56. end
  57. asd_mask = z_idx(idx);
  58. td_mask = ~asd_mask;
  60. overlap_pos_asd = zeros(t, K);
  61. overlap_neg_asd = zeros(t, K);
  62. overlap_pos_td = zeros(t, K);
  63. overlap_neg_td = zeros(t, K);
  65. % For each degree threshold j, compute the proportion of subjects per
  66. % region whose deviation degree meets or exceeds j. Sweeping j gives
  67. % an overlap-vs-degree curve for each region.
  68. for j = 1:t
  69. degree_pos = degree_pos_orig >= j;
  70. degree_neg = degree_neg_orig >= j;
  72. overlap_pos_asd(j,:) = sum(degree_pos(asd_mask,:)) / n_asd;
  73. overlap_neg_asd(j,:) = sum(degree_neg(asd_mask,:)) / n_asd;
  74. overlap_pos_td(j,:) = sum(degree_pos(td_mask,:)) / n_td;
  75. overlap_neg_td(j,:) = sum(degree_neg(td_mask,:)) / n_td;
  76. end
  78. % Summarise each curve by its AUC (trapezoidal), then take the ASD-TD
  79. % difference as the test statistic.
  80. auc_pos_asd = trapz(overlap_pos_asd);
  81. auc_neg_asd = trapz(overlap_neg_asd);
  82. auc_pos_td = trapz(overlap_pos_td);
  83. auc_neg_td = trapz(overlap_neg_td);
  85. delta_auc_pos = auc_pos_asd - auc_pos_td;
  86. delta_auc_neg = auc_neg_asd - auc_neg_td;
  88. if p == 1
  89. % Store the observed statistics and curves for later plotting
  90. delta_auc_pos1 = delta_auc_pos;
  91. delta_auc_neg1 = delta_auc_neg;
  92. perc_diff_pos = (delta_auc_pos ./ auc_pos_td) * 100;
  93. perc_diff_neg = (delta_auc_neg ./ auc_neg_td) * 100;
  94. overlap_pos_asd1 = overlap_pos_asd;
  95. overlap_neg_asd1 = overlap_neg_asd;
  96. overlap_pos_td1 = overlap_pos_td;
  97. overlap_neg_td1 = overlap_neg_td;
  98. else
  99. cnt_neg = cnt_neg + (delta_auc_neg >= delta_auc_neg1);
  100. cnt_pos = cnt_pos + (delta_auc_pos >= delta_auc_pos1);
  101. end
  102. end
  103. toc
  105. % Uncorrected permutation p-values, then BH-FDR across regions
  106. punc_neg = cnt_neg / nP;
  107. punc_pos = cnt_pos / nP;
  108. [p_neg] = mafdr(punc_neg, 'BHFDR', true);
  109. [p_pos] = mafdr(punc_pos, 'BHFDR', true);
  112. %% Helper function
  113. % For each subject: threshold edges at |Z| > thr, build the binary
  114. % deviation adjacency matrix, and return each region's nodal degree
  115. % (number of extreme-deviation edges attached to it), separately for
  116. % positive and negative deviations.
  117. function [degree_pos, degree_neg] = compute_deviation_degree(z, thr, nRoi)
  118. z_log_pos = z > thr;
  119. z_log_neg = z < -thr;
  121. % Vector -> nRoi x nRoi x nSubs
  122. adj_pos = recon3dMat(z_log_pos', nRoi);
  123. adj_neg = recon3dMat(z_log_neg', nRoi);
  125. nSubs = size(adj_pos, 3);
  126. degree_pos = zeros(nRoi, nSubs);
  127. degree_neg = zeros(nRoi, nSubs);
  129. for i = 1:nSubs
  130. degree_pos(:,i) = degrees_und(adj_pos(:,:,i));
  131. degree_neg(:,i) = degrees_und(adj_neg(:,:,i));
  132. end
  133. end

region_overlap.m at commit 8569bec, no license · at the source

Overview

Authors: Iva Ilioska1,2, Marianne Oldehinkel2, Alberto Llera2, Maroš Rovný3, Ting Mei2, Seyed Mostafa Kia4, Dorothea L. Floris2,5, Julian Tillmann6,7, Rosemary J. Holt8, Eva Loth9, Tony Charman6, Declan G. M. Murphy9, Christine Ecker6,10, Tobias Banaschewski11, Maarten Mennes2, Christian F. Beckmann2,12, Andre Marquand2, Jan K. Buitelaar2,13, Alex Fornito14
ORCID iDs: Iva Ilioska, Alberto Llera, Seyed Mostafa Kia, Eva Loth, Tony Charman, Declan G. M. Murphy, Tobias Banaschewski, Andre Marquand, Jan K. Buitelaar
14 affiliations
  1. Department of Psychiatry, University of Cambridge,Cambridge, UK
  2. Department of Medical Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center,Nijmegen, the Netherlands
  3. MRC CBU, University of Cambridge,Cambridge, UK
  4. Department of Intelligent Systems, Tilburg University,Tilburg, the Netherlands
  5. Methods of Plasticity Research, Department of Psychology, University of Zürich,Zurich, Switzerland
  6. Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London,London, UK
  7. Roche Pharma Research and Early Development, Roche Innovation Center Basel,Basel, Switzerland
  8. Autism Research Centre, Department of Psychiatry, University of Cambridge,Cambridge, UK
  9. Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London,London, UK
  10. Department of Child and Adolescent Psychiatry, University Hospital, Goethe University,Frankfurt am Main, Germany
  11. Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg,Mannheim, Germany
  12. Centre for Functional MRI of the Brain, University of Oxford,Oxford, UK
  13. Karakter Child and Adolescent Psychiatry University Center,Nijmegen, the Netherlands
  14. Turner Institute for Brain and Mental Health, School of Psychological Sciences, and and Monash Biomedical Imaging, Monash University,Melbourne, Victoria Australia
Institutions: University of Cambridge (United Kingdom); Radboud University Medical Center (Netherlands); Tilburg University (Netherlands); University of Zurich (Switzerland); King's College London (United Kingdom); Roche (Switzerland) (Switzerland); Goethe University Frankfurt (Germany); Central Institute of Mental Health (Germany); University of Oxford (United Kingdom); Karakter (Netherlands); Monash University (Australia)
Journal: Nature. Mental health, volume 4, issue 6, pages 1010-1020
Dates: received 8 September 2025; accepted 22 April 2026; published online 1 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44220-026-00656-y · PMID 42291776 · PMCID PMC13259941 · OpenAlex W7163035110
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), autism (population), systems (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Autism spectrum disorders
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Radboud Universitair Medisch Centrum (Radboudumc); ZonMw (452172019); Innovative Medicines Initiative (115300, 777394); EC | Seventh Framework Programme (EC Seventh Framework Programm) (FP7/2007-2013); EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (643051, 642996); Sylvia and Charles Viertel Charitable Foundation (Viertel Charitable Foundation) (3274306)
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Group-mean comparisons often identify atypical functional connectivity in autism, but it remains unclear whether these findings consistently manifest at the individual level. Here we use normative modeling to quantify the interindividual heterogeneity of atypical functional connectivity across multiple brain scales using multicenter resting-state functional magnetic resonance imaging data from 1,824 participants (796 autistic individuals and 1,028 neurotypical controls) in a cross-sectional study across 32 sites. We find that no single functional connectivity estimate showed extreme deviation from normative expectations in more than 4% of people in either group. However, these deviations converged on common regions and networks in autistic people, who showed up to double the level of overlap compared with controls. Specifically, autistic participants demonstrated convergent hypoconnectivity in sensorimotor and attention regions and convergent hyperconnectivity between frontoparietal and default mode networks. Functional connectivity deviation patterns significantly predicted social and cognitive abilities. These findings demonstrate that autism exhibits scale-dependent heterogeneity, characterized by normative variability at the connection level but significant convergence at regional and network scales. These convergent regions and networks may be used to identify targets for individualized therapeutic development.

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 3 matches between paragraphs and lines of code.

ivaili/MultiscaleHeterogeneity

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8569becaed81dfb076c11a2ebd0287067d2b157b, 6 July 2026
Languages: MATLAB (10)
Size: 11 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brain Connectivity Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/ivaili/MultiscaleHeterogeneity

Code availability

Analysis code used for this manuscript is available via GitHub at https://github.com/ivaili/MultiscaleHeterogeneity. Software versions for the data analysis are: FMRIB Software Library 5.0.10, Matlab R2018b, Spyder(Python 3.12) and Predictive Clinical Neuroscience Toolkit 0.20, and scikit-learn 0.24.2 code for the analyses is available via GitHub at https://github.com/ivaili/MultiscaleHeterogeneity.

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

Tracing map

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

Data collected within the ABIDE 1 and 2 initiatives are available for public use on the following links: http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html and http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html. Data from the EU-AIMS LEAP consortium are stored at the central EU-AIMS database at the Pasteur Institute in Paris. These data are currently only accessible to consortium members with an analysis proposal approved and will become publicly available via a secure database in the near future (https://elixir-luxembourg.org/).

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 1 keyword, 6 funders, 39 references.

Cite

This paper

Ilioska, I., Oldehinkel, M., Llera, A., Rovný, M., Mei, T., Kia, S. M., Floris, D. L., Tillmann, J., Holt, R. J., Loth, E., Charman, T., Murphy, D. G. M., Ecker, C., Banaschewski, T., Mennes, M., Beckmann, C. F., Marquand, A., Buitelaar, J. K., & Fornito, A. (2026). Multiscale heterogeneity of atypical functional connectivity in autism. Nature. Mental health, 4(6), 1010-1020. https://doi.org/10.1038/s44220-026-00656-y

BibTeX

@article{ilioska2026multiscale,
author = {Ilioska, Iva and Oldehinkel, Marianne and Llera, Alberto and Rovný, Maroš and Mei, Ting and Kia, Seyed Mostafa and Floris, Dorothea L. and Tillmann, Julian and Holt, Rosemary J. and Loth, Eva and Charman, Tony and Murphy, Declan G. M. and Ecker, Christine and Banaschewski, Tobias and Mennes, Maarten and Beckmann, Christian F. and Marquand, Andre and Buitelaar, Jan K. and Fornito, Alex},
title = {{Multiscale heterogeneity of atypical functional connectivity in autism}},
journal = {Nature. Mental health},
year = {2026},
month = jun,
volume = {4},
number = {6},
pages = {1010--1020},
publisher = {Springer Science+Business Media},
issn = {2731-6076},
doi = {10.1038/s44220-026-00656-y},
url = {https://doi.org/10.1038/s44220-026-00656-y},
pmid = {42291776},
pmcid = {PMC13259941}
}

RIS

TY - JOUR
AU - Ilioska, Iva
AU - Oldehinkel, Marianne
AU - Llera, Alberto
AU - Rovný, Maroš
AU - Mei, Ting
AU - Kia, Seyed Mostafa
AU - Floris, Dorothea L.
AU - Tillmann, Julian
AU - Holt, Rosemary J.
AU - Loth, Eva
AU - Charman, Tony
AU - Murphy, Declan G. M.
AU - Ecker, Christine
AU - Banaschewski, Tobias
AU - Mennes, Maarten
AU - Beckmann, Christian F.
AU - Marquand, Andre
AU - Buitelaar, Jan K.
AU - Fornito, Alex
TI - Multiscale heterogeneity of atypical functional connectivity in autism
T2 - Nature. Mental health
J2 - Nat Ment Health
PY - 2026
DA - 2026/06/01
VL - 4
IS - 6
SP - 1010
EP - 1020
SN - 2731-6076
PB - Springer Science+Business Media
DO - 10.1038/s44220-026-00656-y
UR - https://doi.org/10.1038/s44220-026-00656-y
LA - en
ER -

CSL-JSON

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