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Preserved intrinsic neural timescale organization with hierarchical variation in autism spectrum disorder.

Code ↔ Paper

5 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 5 matches · 2 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 analyses › Structure–function correspondence analysis ↔ make_surface_spin_idx.m, the whole file · a weak match · score 0.83 · spherical surface, spin permutations, parcel centroids, hemisphere, nearest, reassigned
  2. [2] § Results › Individual deviations from the timescale hierarchy link sensory traits to cortical temporal dynamics ↔ calc_individual_deviations.m, lines 140–168 · score 0.80 · Sensation Seeking, Sensation Avoiding, AASP subscale, Sensory Sensitivity, PCA, variance
  3. [3] § Materials and methods › Statistical analyses › Structure–function correspondence analysis ↔ parcel_centroids_on_sphere.m, the whole file · a weak match · score 0.70 · parcel centroids, spherical surface, spin permutations, cortical
  4. [4] § Results › Individual deviations from the timescale hierarchy link sensory traits to cortical temporal dynamics ↔ calc_individual_deviations.m, lines 140–168 · score 0.69 · sensation seeking, sensation avoiding, sensory sensitivity, principal component, Individual deviations, PC
  5. [5] § Materials and methods › Statistical analyses › Quantification of individual deviations relative to the canonical hierarchical ordering of INTs ↔ calc_individual_deviations.m, lines 101–139 · score 0.61 · global offset, residual deviations, hierarchical scaling, template, TDC

Paper

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The authors' code

MATLAB · 299 lines · 8.7 KB · no license · 3 matches

  1. function [metrics,corrRes,metricTable] = calc_individual_deviations(INT,isTDC,AASP,covar,covarNames)
  2. % [metrics,corrRes,metricTable] = ...
  3. % calc_individual_deviations(INT,isASD,AASP)
  4. %% ==============================================================
  5. % Computes individual deviations from a TDC-derived INT hierarchy
  6. % template and examines their associations with sensory traits
  7. % measured by the AASP.
  8. %
  9. % Repository accompanying:
  10. % Shikauchi et al. (2026)
  11. % https://doi.org/10.64898/2026.02.27.708484
  12. %
  13. % Individual INT profiles were modeled as:
  14. %
  15. % INT_subject = alpha + beta * INT_template + residual
  16. %
  17. % where:
  18. % alpha = global shift
  19. % beta = hierarchical scaling
  20. % RMS = sqrt(mean(residual.^2))
  21. %
  22. % Input:
  23. % INT : [Nsub x 360] intrinsic neural timescales
  24. % for each participant (Glasser 360 parcels)
  25. % isTDC : [Nsub x 1] logical vector indicating TDC participants
  26. % AASP : [Nsub x 4] AASP scores or principal component scores
  27. %
  28. % Optional:
  29. % covar : [Nsub x K] covariates (e.g., Age, Sex, FD)
  30. % covarNames : names of covariates
  31. %
  32. % Output:
  33. % metrics : structure containing
  34. % alpha (global offset),
  35. % beta (hierarchical scaling),
  36. % rms (residual deviation)
  37. % corrRes : table of correlations between metrics and AASP scores
  38. % ==============================================================
  39. %% Parameter settings
  40. aaspNames = {'AASP_PC1','AASP_PC2','AASP_PC3','AASP_PC4'};
  41. metricNames = {'alpha','beta','rms'};
  42. %% Template stability analysis
  43. % Evaluate whether exclusion of a single TDC participant
  44. % changes the parcel ranking of the TDC template.
  45. [N,P] = size(INT);
  47. % Leave-one-out template construction for TDC participants.
  48. % For ASD participants, the full TDC template is used.
  49. template_full = mean(INT(isTDC,:),1,'omitnan');
  50. [~, sort_full] = sort(template_full,'ascend');
  52. tdcIdx = find(isTDC);
  53. nTDC = numel(tdcIdx);
  55. rank_shift = zeros(nTDC,1); % maximum rank shift
  56. n_swapped = zeros(nTDC,1); % number of parcels with rank changes
  58. for k = 1:nTDC
  59. i = tdcIdx(k);
  61. % Sort parcels according to the subject-specific template hierarchy.
  62. % Individual INT values are reordered using the same ranking.
  63. template_loo = mean(INT(isTDC & ( (1:size(INT,1))'~=i ),:),1,'omitnan');
  64. [~, sort_loo] = sort(template_loo,'ascend');
  66. rank_full = zeros(P,1); rank_full(sort_full) = 1:P;
  67. rank_loo = zeros(P,1); rank_loo(sort_loo) = 1:P;
  69. diff_rank = abs(rank_full - rank_loo);
  71. rank_shift(k) = max(diff_rank);
  72. n_swapped(k) = nnz(diff_rank>0);
  73. end
  75. fprintf('Max rank shift (median): %.1f\n', median(rank_shift));
  76. fprintf('Parcels swapped (median): %.0f / %d\n', median(n_swapped), P);
  77. %% Input validation
  78. assert(P==360, 'INT must be [Nsub x 360].');
  79. assert(size(AASP,1)==size(INT,1) && size(AASP,2)==4, 'AASP must be [Nsub x 4].');
  80. assert(numel(isTDC)==size(INT,1), 'isTDC must be [Nsub x 1].');
  82. if ~exist('covar','var') || isempty(covar)
  83. covar = [];
  84. covarNames = {};
  85. else
  86. assert(size(covar,1)==N, 'covar must have N rows.');
  87. if ~exist('covarNames','var') || isempty(covarNames)
  88. covarNames = arrayfun(@(k)sprintf('covar%d',k), 1:size(covar,2), 'uni', 0);
  89. end
  90. end
  92. metrics = struct();
  93. metrics.alpha = nan(N,1);
  94. metrics.beta = nan(N,1);
  95. metrics.rms = nan(N,1);
  97. tdcIdx = find(isTDC);
  98. tdcSum = sum(INT(isTDC,:), 1, 'omitnan'); % [1 x 360]
  99. tdcN = numel(tdcIdx);
  100. template_full = tdcSum / tdcN;
  101. %% Template fitting
  102. % INT_subject = alpha + beta * template + residual
  103. %
  104. % alpha : global offset from the template
  105. % beta : hierarchical scaling relative to the template
  106. % rms : magnitude of residual deviation after accounting
  107. % for offset and scaling
  108. for i = 1:N
  110. % --- 1) subject-specific template (TDC: LOO) ---
  111. if isTDC(i)
  112. template_i = (tdcSum - INT(i,:)) / (tdcN - 1);
  113. else
  114. template_i = template_full;
  115. end
  117. % --- 2) subject-specific ordering ---
  118. [templateSorted, sortIdx] = sort(template_i(:), 'ascend'); % [360 x 1]
  119. y = INT(i, sortIdx)'; % [360 x 1]
  121. good = isfinite(y) & isfinite(templateSorted);
  122. if nnz(good) < 50
  123. continue;
  124. end
  126. % --- 3) regression: y = alpha + beta*template + resid ---
  127. X = [ones(P,1), templateSorted];
  128. b = X(good,:) \ y(good);
  130. alpha_i = b(1);
  131. beta_i = b(2);
  133. yhat = X*b;
  134. resid = y - yhat;
  136. metrics.alpha(i) = alpha_i;
  137. metrics.beta(i) = beta_i;
  138. metrics.rms(i) = sqrt(mean(resid(good).^2, 'omitnan'));
  139. end
  140. %% Principal component analysis of AASP subscales
  141. % PCA is performed after z-score normalization.
  142. % Principal component scores are used in subsequent analyses.
  143. aasp_label = {'Low_Registration','Sensation_Seeking',...
  144. 'Sensory_Sensitivity','Sensation_Avoiding'};
  145. usePCA = true;
  146. if usePCA
  148. X = AASP; % N x 4
  150. ok = all(isfinite(X),2);
  151. Xok = X(ok,:);
  153. Xz = zscore(Xok);
  155. % PCA
  156. [coeff, score, ~, ~, explained] = pca(Xz);
  158. disp('Explained variance (%)');
  159. disp(explained');
  161. loadingTbl = array2table(coeff, 'VariableNames', compose('PC%d',1:4), ...
  162. 'RowNames', aasp_label);
  163. disp('Loadings (coeff):');
  164. disp(loadingTbl);
  165. AASP = score;
  166. else
  167. aaspNames = aasp_label;
  168. end
  169. %% Correlation analysis
  170. % Spearman correlations between deviation metrics
  171. % and AASP scores.
  172. corrRes = table();
  174. for m = 1:numel(metricNames)
  175. mn = metricNames{m};
  176. x = metrics.(mn);
  178. for a = 1:4
  179. y = AASP(:,a);
  181. ok = isfinite(x) & isfinite(y);
  182. if nnz(ok) < 20
  183. r = NaN; p = NaN;
  184. else
  185. [r,p] = corr(x(ok), y(ok), 'Type','Spearman');
  186. end
  188. corrRes = [corrRes; table(string(mn), string(aaspNames{a}), r, p, nnz(ok), ...
  189. 'VariableNames', {'Metric','AASP','r','p','N'})]; %#ok<AGROW>
  190. end
  191. end
  193. %% Partial correlation analysis
  194. % Controls for covariates specified in 'covar'.
  195. if ~isempty(covar)
  196. pcorrRes = table();
  197. for m = 1:numel(metricNames)
  198. mn = metricNames{m};
  199. x = metrics.(mn);
  201. for a = 1:4
  202. y = AASP(:,a);
  204. Z = covar;
  205. ok = isfinite(x) & isfinite(y) & all(isfinite(Z),2);
  206. if nnz(ok) < (20 + size(Z,2))
  207. r = NaN; p = NaN;
  208. else
  209. % Pearson partial correlation
  210. % Apply tiedrank() beforehand if a rank-based partial correlation is desired.
  211. [r,p] = partialcorr(x(ok), y(ok), Z(ok,:));
  212. end
  214. pcorrRes = [pcorrRes; table(string(mn), string(aaspNames{a}), r, p, nnz(ok), ...
  215. 'VariableNames', {'Metric','AASP','partial_r','p','N'})]; %#ok<AGROW>
  216. end
  217. end
  219. disp('--- Partial correlation results (controlling covariates) ---');
  220. disp(pcorrRes);
  221. end
  223. disp('--- Correlation results ---');
  224. disp(corrRes);
  226. metricTable = table((1:N)', isTDC(:), 'VariableNames', {'subjID','isTDC'});
  227. for m = 1:numel(metricNames)
  228. metricTable.(metricNames{m}) = metrics.(metricNames{m});
  229. end
  230. for a = 1:4
  231. metricTable.(aaspNames{a}) = AASP(:,a);
  232. end
  233. if ~isempty(covar)
  234. for k = 1:size(covar,2)
  235. metricTable.(covarNames{k}) = covar(:,k);
  236. end
  237. end
  238. %% Covariate residualization
  239. % Removes linear effects of covariates before
  240. % computing Spearman correlations.
  241. % Z = covar;
  242. %
  243. % metric_names = fieldnames(metrics);
  244. % nMetric = numel(metric_names);
  245. % nPC = size(AASP,2);
  246. % nRow = nMetric * nPC;
  247. %
  248. % % type setting
  249. % Result = table( ...
  250. % strings(nRow,1), ... % Metric
  251. % zeros(nRow,1), ... % PC
  252. % nan(nRow,1), ... % rho
  253. % nan(nRow,1), ... % p
  254. % zeros(nRow,1), ... % N
  255. % 'VariableNames', {'Metric','PC','rho','p','N'});
  256. % row = 1;
  257. % for m = 1:nMetric
  258. % Y = metrics.(metric_names{m});
  259. %
  260. % % remove covariance
  261. % Yr = residualize(Y, Z);
  262. %
  263. % for k = 1:nPC
  264. % Xk = AASP(:,k);
  265. % Xkr = residualize(Xk, Z);
  266. %
  267. % v = isfinite(Xkr) & isfinite(Yr);
  268. %
  269. % if sum(v) > 10
  270. % [rho,p] = corr(Xkr(v), Yr(v), 'Type','Spearman');
  271. % else
  272. % rho = NaN; p = NaN;
  273. % end
  274. %
  275. % Result.Metric{row,1} = metric_names{m};
  276. % Result.PC(row,1) = k;
  277. % Result.rho(row,1) = rho;
  278. % Result.p(row,1) = p;
  279. % Result.N(row,1) = sum(v);
  280. %
  281. % figure, scatter(Xkr, Yr)
  282. % row = row + 1;
  283. % end
  284. % end
  285. %
  286. % disp(Result);
  287. end
  288. %% ==============================================================
  289. function r = residualize(y, Z)
  290. % y: [N x 1]
  291. % Z: [N x K] covariates
  292. ok = all(isfinite([y Z]),2);
  293. r = nan(size(y));
  294. if sum(ok) > size(Z,2) + 2
  295. b = [ones(sum(ok),1) Z(ok,:)] \ y(ok);
  296. yhat = [ones(sum(ok),1) Z(ok,:)] * b;
  297. r(ok) = y(ok) - yhat;
  298. end
  299. end

calc_individual_deviations.m at commit f76b0d1, no license · at the source

Overview

Authors: Yumi Shikauchi1, Ryuta Aoki1,2,3, Takashi Itahashi1, Masaaki Shimizu4, Taiga Naoe1, Tsukasa Okimura1, Haruhisa Ohta1, Ryu-ichiro Hashimoto1,3, Motoaki Nakamura1
ORCID iDs: Yumi Shikauchi, Takashi Itahashi
  1. Medical Institute of Developmental Disabilities Research, Showa Medical University, Tokyo 157-8577, Japan
  2. Graduate School of Medicine, Human Brain Research Center, Kyoto University, Kyoto 606-8507, Japan
  3. Department of Language Sciences, Graduate School of Humanities, Tokyo Metropolitan University, Tokyo 192-0397, Japan
  4. Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo 113-8510, Japan
Institutions: SHOWA Medical University (Japan); Kyoto University (Japan); Tokyo Metropolitan University (Japan); Institute of Science Tokyo (Japan)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 9, article bhag121
Dates: received 17 March 2026; accepted 14 July 2026; published online 7 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag121 · PMID 42704258 · PMCID PMC13548320 · OpenAlex W7132847771
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.02.27.708484
Status: code verified
Categories: human (organism), autism (population), developmental (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: autism spectrum disorder, inter-individual variability, intrinsic neural timescale, sensory processing, temporal hierarchy
MeSH: Autism Spectrum Disorder*, Brain*, Adolescent, Adult, Brain Mapping, Child, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Agency for Medical Research and Development (JP21dm0307001, JP21dm0307105, JP18dm0307008, JP24wm0625402, JP25wm0625502); Japan Science and Technology Corporation (JPMJMS2292); JSPS KAKENHI (JP26K10270)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Intrinsic neural timescales (INTs) index the temporal decay of neural activity and form a hierarchy from fast sensorimotor to slow transmodal regions. Although altered INTs have been reported in autism spectrum disorder (ASD), it remains unclear whether this hierarchical organization is preserved and how individual variability along it relates to sensory traits. Using resting-state fMRI from 182 participants (67 ASD, 115 typically developed controls [TDC]), we estimated INTs from the autocorrelation half-life and averaged them across four runs. The cortical INT hierarchy was preserved in ASD, with comparable sensorimotor-to-transmodal organization across groups. Regions operating at longer timescales exhibited relative prolongation in ASD, with effect size increasing along the hierarchy. However, no statistically significant group differences were identified at any spatial resolution examined. To characterize individual INT profiles relative to a TDC-derived template, we decomposed each participant’s profile into global shift, hierarchical scaling, and residual deviation components. Global shifts and hierarchical scaling were associated with demographic variation (primarily sex) rather than diagnosis, whereas residual deviations showed a modest association with sensory traits characterized by reduced sensory registration. These findings indicate that large-scale temporal organization is largely preserved in ASD, while individual-specific deviations from this hierarchy may underlie variability in sensory experience.

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 5 matches between paragraphs and lines of code.

shikauchiy/int-hierarchy-analysis

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: f76b0d15ee3a321d8e79be1af2e68d585ffcf981, 15 June 2026
Languages: MATLAB (3)
Size: 4 files, 3 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
4 files
At the source: github.com/shikauchiy/int-hierarchy-analysis

The paper's code and data availability statement is in the Data section.

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;
  • 3 scripts, each with its path and the digest of its content;
  • 5 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

The imaging data analyzed in this study were obtained from the Brain/MINDS Beyond Human Brain MRI Project. Access to the dataset is available upon request through the Brain/MINDS Beyond data-sharing program at https://hbm.brainminds-beyond.jp/data.html.

To facilitate reproducibility of the analyses developed for the present study, the MATLAB code used for spin-based spatial null testing for sensory hierarchy analyses, template-based hierarchical modeling, estimation of individual deviation metrics, and statistical analyses is publicly available at https://github.com/shikauchiy/int-hierarchy-analysis.

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, 9 authors, 5 keywords, 11 MeSH terms, 3 funders, 53 references.

Cite

This paper

Shikauchi, Y., Aoki, R., Itahashi, T., Shimizu, M., Naoe, T., Okimura, T., Ohta, H., Hashimoto, R.-i., & Nakamura, M. (2026). Preserved intrinsic neural timescale organization with hierarchical variation in autism spectrum disorder. Cerebral cortex (New York, N.Y. : 1991), 36(9), bhag121. https://doi.org/10.1093/cercor/bhag121

BibTeX

@article{shikauchi2026preserved,
author = {Shikauchi, Yumi and Aoki, Ryuta and Itahashi, Takashi and Shimizu, Masaaki and Naoe, Taiga and Okimura, Tsukasa and Ohta, Haruhisa and Hashimoto, Ryu-ichiro and Nakamura, Motoaki},
title = {{Preserved intrinsic neural timescale organization with hierarchical variation in autism spectrum disorder}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = sep,
volume = {36},
number = {9},
pages = {bhag121},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag121},
url = {https://doi.org/10.1093/cercor/bhag121},
pmid = {42704258},
pmcid = {PMC13548320}
}

RIS

TY - JOUR
AU - Shikauchi, Yumi
AU - Aoki, Ryuta
AU - Itahashi, Takashi
AU - Shimizu, Masaaki
AU - Naoe, Taiga
AU - Okimura, Tsukasa
AU - Ohta, Haruhisa
AU - Hashimoto, Ryu-ichiro
AU - Nakamura, Motoaki
TI - Preserved intrinsic neural timescale organization with hierarchical variation in autism spectrum disorder
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/09/01
VL - 36
IS - 9
SP - bhag121
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag121
UR - https://doi.org/10.1093/cercor/bhag121
LA - en
ER -

CSL-JSON

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"title": "Preserved intrinsic neural timescale organization with hierarchical variation in autism spectrum disorder",
"container-title": "Cerebral cortex (New York, N.Y. : 1991)",
"author": [
{
"family": "Shikauchi",
"given": "Yumi"
},
{
"family": "Aoki",
"given": "Ryuta"
},
{
"family": "Itahashi",
"given": "Takashi"
},
{
"family": "Shimizu",
"given": "Masaaki"
},
{
"family": "Naoe",
"given": "Taiga"
},
{
"family": "Okimura",
"given": "Tsukasa"
},
{
"family": "Ohta",
"given": "Haruhisa"
},
{
"family": "Hashimoto",
"given": "Ryu-ichiro"
},
{
"family": "Nakamura",
"given": "Motoaki"
}
],
"container-title-short": "Cereb Cortex",
"volume": "36",
"issue": "9",
"page": "bhag121",
"DOI": "10.1093/cercor/bhag121",
"PMID": "42704258",
"PMCID": "PMC13548320",
"ISSN": "1047-3211",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/cercor/bhag121",
"language": "en",
"issued": {
"date-parts": [
[
2026,
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1
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]
}
}

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