OSCR

Associations between chronic placental inflammation, fetal brain development and later autism traits.

Code ↔ Paper

1 match 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 1 match · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Neuroimaging data acquisition and analysis › Whole-brain functional connectome construction ↔ +nla/+qualityControl/checkHeadMotion.m, the whole file · a weak match · score 0.64 · Quality control, motion, Functional connectivity, distance, regression, cortex

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 121 lines · 4.7 KB · MIT · 1 match

  1. function checkHeadMotion(fig, input_struct, motion, remove_index)
  3. network_atlas = input_struct.net_atlas;
  4. functional_connectivity = input_struct.func_conn;
  5. if remove_index ~= 0
  6. [new_netatlas, functional_connectivity] = nla.removeNetworks(input_struct.net_atlas, input_struct.net_atlas.nets(remove_index).name, strcat(input_struct.net_atlas.name, '_-', input_struct.net_atlas.nets(remove_index).name), input_struct.func_conn);
  7. network_atlas = nla.NetworkAtlas(new_netatlas);
  8. if ~isa(functional_connectivity, 'nla.TriMatrix')
  9. functional_connectivity = nla.TriMatrix(functional_connectivity);
  10. end
  11. end
  13. prog = uiprogressdlg(fig, 'Title', 'Generating figures', 'Message', 'Generating head motion figures');
  14. prog.Value = 0.02;
  15. distances = nla.helpers.euclidianDistanceROIs(network_atlas);
  16. prog.Value = 0.75;
  17. [r_vec, p_vec] = corr(motion, functional_connectivity.v', 'type', 'Pearson');
  18. if any(isnan(r_vec))
  19. msgbox("Please choose another column for motion variable. NaN's are present or value is constant")
  20. return
  21. end
  23. prob = nla.TriMatrix(network_atlas.numROIs());
  24. r = nla.TriMatrix(network_atlas.numROIs());
  25. h = nla.TriMatrix(network_atlas.numROIs(), 'logical');
  26. prob.v = p_vec';
  27. r.v = r_vec';
  28. h.v = nla.lib.fdr_bh(prob.v);
  30. prog.Value = 0.98;
  32. %% Visualization of head motion on brain
  33. color_scale = 1000;
  34. color_map = turbo(color_scale);
  35. mesh_alpha = 0.5;
  36. ROI_radius = 4;
  37. ctx = nla.gfx.MeshType.STD;
  38. llimit = -0.3;
  39. ulimit = 0.3;
  41. fig = nla.gfx.createFigure(1800, 900);
  42. matrix_plot = nla.gfx.plots.MatrixPlot(fig, "FC-motion correlation (Pearson's r)", r, network_atlas.nets,...
  43. nla.gfx.FigSize.LARGE, 'lower_limit', llimit, 'upper_limit', ulimit);
  44. matrix_plot.displayImage();
  45. width = matrix_plot.image_dimensions("image_width");
  46. height = matrix_plot.image_dimensions("image_height");
  47. fig.Position(3) = width * 2;
  48. fig.Position(4) = height;
  50. ax = subplot('Position', [0.780, 0.540, 0.20, 0.40]);
  51. nla.gfx.setTitle(ax, sprintf("FC-motion correlation (Pearson's r) (q < 0.05)\n"));
  52. nla.gfx.drawROIsOnCortex(ax, network_atlas, ctx, mesh_alpha, ROI_radius, nla.gfx.ViewPos.DORSAL, false,...
  53. nla.gfx.BrainColorMode.NONE);
  55. for col = 1:network_atlas.numROIs()
  56. for row = (col + 1):network_atlas.numROIs()
  57. if h.get(row, col)
  58. pos1 = network_atlas.ROIs(row).pos;
  59. pos2 = network_atlas.ROIs(col).pos;
  61. edge_color = nla.gfx.valToColor(r.get(row, col), llimit, ulimit, color_map);
  63. p = plot3([pos1(1), pos2(1)], [pos1(2), pos2(2)], [pos1(3), pos2(3)], 'Color', edge_color, 'LineWidth', 2);
  64. p.Annotation.LegendInformation.IconDisplayStyle = 'off';
  65. end
  66. end
  67. end
  69. light('Position',[0,100,100],'Style','local');
  71. num_ticks = 10;
  72. colormap(ax, color_map);
  73. cb = colorbar(ax);
  75. ticks = [0:num_ticks];
  76. cb.Ticks = double(ticks) ./ num_ticks;
  78. % tick labels
  79. labels = {};
  80. for i = ticks
  81. labels{i + 1} = sprintf("%.2g", llimit + (i * ((double(ulimit - llimit) / num_ticks))));
  82. end
  83. cb.TickLabels = labels;
  84. caxis(ax, [0, 1]);
  86. %% Distribution of corr
  87. ax = subplot('Position', [0.525, 0.075, 0.1875, 0.425]);
  88. nla.gfx.setTitle(ax, "FC-Motion Correlation Histogram");
  89. histogram(ax, r_vec, 'EdgeColor', 'black', 'FaceColor', 'black');
  90. xlabel(ax, 'FC-Motion Correlation (Pearson r)');
  92. %% Heatmap of corr/distance
  93. ax = subplot('Position', [0.755, 0.075, 0.225, 0.425]);
  94. nla.gfx.setTitle(ax, "FC-Motion Correlation vs. ROI Distance");
  95. [values, centers] = hist3([distances.v, r_vec'], [50, 50]);
  96. imagesc(ax, centers{:}, values');
  97. xlabel(ax, 'Euclidian Distance');
  98. ylabel(ax, 'FC-Motion Correlation (Pearson r)');
  99. colorbar(ax);
  100. axis(ax, 'xy');
  102. % Least-squares regression line
  103. lsline_coeff = polyfit(distances.v, r_vec', 1);
  104. lsline_x = linspace(ax.XLim(1), ax.XLim(2), 2);
  105. lsline_y = polyval(lsline_coeff, lsline_x);
  106. hold('on');
  107. plot(lsline_x, lsline_y, 'r');
  109. %% Summary statistics
  110. percent_sig = (sum(h.v) ./ numel(h.v)) * 100;
  111. med_abs_corr = median(abs(r.v));
  112. fc_motion_distance_corr = corr(r.v, distances.v);
  114. ax = subplot('Position', [0.525, 0.95, 0.1875, 0.40]);
  115. nla.gfx.hideAxes(ax);
  116. text(ax, 0, 0, sprintf("Percent of significant edges: %0.2f%%\nMedian absolute correlation: %0.2f\nFC-motion-distance correlation: %0.2f",...
  117. percent_sig, med_abs_corr, fc_motion_distance_corr), 'HorizontalAlignment', 'left', 'VerticalAlignment', 'top');
  119. close(prog);
  120. end

checkHeadMotion.m at commit 37fe497, under MIT · at the source

Overview

Authors: Iris Menu1,2, Lanxin Ji1, Bosi Chen1,3, Tanya Bhatia1, Mark Duffy1, Sofia Trapaga1, Suzanne M Jacques4, Faisal Qureshi4, Adam Eggebrecht5, Muriah D Wheelock5, Christopher J Trentacosta6,7, Moriah E Thomason1,8,9
ORCID iDs: Iris Menu, Lanxin Ji, Bosi Chen, Tanya Bhatia, Sofia Trapaga
  1. Department of Child & Adolescent Psychiatry, NYU Langone Health, New York, NY, 10016, USA
  2. Université Paris Cité, LaPsyDÉ, CNRS, Paris, F-75005, France
  3. Autism Center, Child Mind Institute, New York, NY, USA
  4. Department of Pathology, Wayne State University School of Medicine, Detroit, MI, USA
  5. Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
  6. Department of Psychology, Wayne State University, Detroit, MI, 48202, USA
  7. Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI, 48202, USA
  8. Department of Population Health, NYU Langone Health, New York, NY, 10016, USA
  9. Neuroscience Institute, NYU Langone Health, New York, NY, 10016, USA
Institutions: Centre National de la Recherche Scientifique (France); Université Paris Cité (France); NYU Langone Health (United States); Laboratoire de Psychologie du Développement et de L’Education de l’enfant (France); New York University (United States); Child Mind Institute (United States); Wayne State University (United States); Washington University in St. Louis (United States)
Journal: Brain, behavior, & immunity - health, volume 57, article 101351
Dates: received 31 August 2026; accepted 8 September 2026; published online 10 September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.bbih.2026.101351 · PMID 42756825 · PMCID PMC13583612 · OpenAlex W7212166723
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), autism (population), developmental (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Preprocessing
Keywords: Fetal brain development, Chronic placental inflammation, Prenatal inflammation, Resting-state functional connectivity, Fetal programming
Topic: Tryptophan and brain disorders (Biological Psychiatry, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (1k99hd113873-01, MH141129, MH122447, DA055338, ES032294, R00EB029343)
Citations: not cited yet (Europe PMC); 98 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Zenodo 14051966

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: zenodo.org/records/14051966

wheelocklab/networklevelanalysis

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 37fe497199d9af89c5b2a32dedd560f4b520af13, 21 September 2026
Languages: MATLAB (192), Python (2), C (1)
Size: 333 files, 195 scripts
Software Heritage: not archived
Found in: “Code availability statement”
Holds: README, license file, environment (pyproject.toml, requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: Statistics and Machine Learning Toolbox (9 files), Parallel Computing Toolbox (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
197 files
At the source: github.com/WheelockLab/NetworkLevelAnalysis/releases/tag/v1.0.0

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/10.1016/j.bbih.2026.101351.

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:

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

The paper has a data 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/10.1016/j.bbih.2026.101351.

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 3, 28 September 2026

  • Authors: added Iris Menu (0000-0001-7587-2493); Lanxin Ji (0000-0003-4509-0225); Bosi Chen (0000-0002-0117-9757); Tanya Bhatia (0009-0002-0212-5117); Sofia Trapaga (0000-0001-7772-0418); removed Iris Menu; Lanxin Ji; Bosi Chen; Tanya Bhatia; Sofia Trapaga
  • Funding: added National Institutes of Health: 1k99hd113873-01, MH141129, MH122447, DA055338, ES032294, R00EB029343

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 12 authors, 5 keywords, 96 references.

Cite

This paper

Menu, I., Ji, L., Chen, B., Bhatia, T., Duffy, M., Trapaga, S., Jacques, S. M., Qureshi, F., Eggebrecht, A., Wheelock, M. D., Trentacosta, C. J., & Thomason, M. E. (2026). Associations between chronic placental inflammation, fetal brain development and later autism traits. Brain, behavior, & immunity - health, 57, 101351. https://doi.org/10.1016/j.bbih.2026.101351

BibTeX

@article{menu2026associations,
author = {Menu, Iris and Ji, Lanxin and Chen, Bosi and Bhatia, Tanya and Duffy, Mark and Trapaga, Sofia and Jacques, Suzanne M and Qureshi, Faisal and Eggebrecht, Adam and Wheelock, Muriah D and Trentacosta, Christopher J and Thomason, Moriah E},
title = {{Associations between chronic placental inflammation, fetal brain development and later autism traits}},
journal = {Brain, behavior, \& immunity - health},
year = {2026},
month = sep,
volume = {57},
pages = {101351},
publisher = {Elsevier},
issn = {2666-3546},
doi = {10.1016/j.bbih.2026.101351},
url = {https://doi.org/10.1016/j.bbih.2026.101351},
pmid = {42756825},
pmcid = {PMC13583612}
}

RIS

TY - JOUR
AU - Menu, Iris
AU - Ji, Lanxin
AU - Chen, Bosi
AU - Bhatia, Tanya
AU - Duffy, Mark
AU - Trapaga, Sofia
AU - Jacques, Suzanne M
AU - Qureshi, Faisal
AU - Eggebrecht, Adam
AU - Wheelock, Muriah D
AU - Trentacosta, Christopher J
AU - Thomason, Moriah E
TI - Associations between chronic placental inflammation, fetal brain development and later autism traits
T2 - Brain, behavior, & immunity - health
J2 - Brain Behav Immun Health
PY - 2026
DA - 2026/09/10
VL - 57
SP - 101351
SN - 2666-3546
PB - Elsevier
DO - 10.1016/j.bbih.2026.101351
UR - https://doi.org/10.1016/j.bbih.2026.101351
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.bbih.2026.101351",
"type": "article-journal",
"title": "Associations between chronic placental inflammation, fetal brain development and later autism traits",
"container-title": "Brain, behavior, & immunity - health",
"author": [
{
"family": "Menu",
"given": "Iris"
},
{
"family": "Ji",
"given": "Lanxin"
},
{
"family": "Chen",
"given": "Bosi"
},
{
"family": "Bhatia",
"given": "Tanya"
},
{
"family": "Duffy",
"given": "Mark"
},
{
"family": "Trapaga",
"given": "Sofia"
},
{
"family": "Jacques",
"given": "Suzanne M"
},
{
"family": "Qureshi",
"given": "Faisal"
},
{
"family": "Eggebrecht",
"given": "Adam"
},
{
"family": "Wheelock",
"given": "Muriah D"
},
{
"family": "Trentacosta",
"given": "Christopher J"
},
{
"family": "Thomason",
"given": "Moriah E"
}
],
"container-title-short": "Brain Behav Immun Health",
"volume": "57",
"page": "101351",
"DOI": "10.1016/j.bbih.2026.101351",
"PMID": "42756825",
"PMCID": "PMC13583612",
"ISSN": "2666-3546",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.bbih.2026.101351",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
10
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.dcn.2026.101745 [code]
Prenatal neural network organization and later executive function development.
Journal: Developmental cognitive neuroscience
In common: fMRI, 13 references
[2] doi:10.1111/joa.70225
Immune proteomic associations with indices of cortical development in 5-year-old children.
Journal: Journal of anatomy
In common: 5 references
[3] doi:10.1038/s41467-026-74215-5 [code]
Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy.
Journal: Nature communications
In common: Parallel Computing Toolbox, Statistics and Machine Learning Toolbox, fMRI, 2 references
[4] doi:10.1038/s44220-026-00656-y [code]
Multiscale heterogeneity of atypical functional connectivity in autism.
Journal: Nature. Mental health
In common: Statistics and Machine Learning Toolbox, autism, fMRI, 2 references
[5] doi:10.1016/j.dcn.2026.101789 [code]
Feasibility of precision functional mapping in youth multi-echo fMRI data.
Journal: Developmental cognitive neuroscience
In common: Parallel Computing Toolbox, Statistics and Machine Learning Toolbox, developmental, fMRI, 1 reference
[6] doi:10.3389/fnins.2026.1785001
A single-cell transcriptomic atlas of the periventricular proliferative zone in the late gestation fetal brain in the pigtail macaque.
Journal: Frontiers in neuroscience
In common: 3 references
[7] doi:10.1093/cercor/bhag034 [code]
Individual differences in adolescent cortical development are associated with neighborhood characteristics: Longitudinal findings from the ABCD study.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: developmental, 3 references
[8] doi:10.1002/hbm.70496 [code]
Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning.
Journal: Human brain mapping
In common: autism, fMRI, 2 references
[9] doi:10.1016/j.neuron.2026.04.011 [code]
Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.
Journal: Neuron
In common: Parallel Computing Toolbox, Statistics and Machine Learning Toolbox, fMRI, 1 reference
[10] doi:10.1002/hbm.70493 [code]
Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.
Journal: Human brain mapping
In common: 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.