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

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  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 · Report a mismatch

Paper

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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 (how the code is found and 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 27 October 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: 2 checks, the latest on 27 October 2026: the link answers (HTTP 200)
  • 27 October 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

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Read it in the paper: doi.org/10.1016/j.bbih.2026.101351.

Tracing map

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Data

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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

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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

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"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"
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{
"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"
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"family": "Wheelock",
"given": "Muriah D"
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{
"family": "Trentacosta",
"given": "Christopher J"
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{
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"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": [
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2026,
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10
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]
}
}

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