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Optimizing functional connectivity scanning conditions for predicting autistic traits.

Code ↔ Paper

2 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 2 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 › Connectome-based predictive modeling ↔ CPM_code/cpm_main.m, the whole file · a weak match · score 0.70 · head motion, partial correlation, connectivity matrices, feature selection, split, edge
  2. [2] § Methods › Preprocessing of functional imaging data ↔ preproc_template/template_skull_strip.sh, lines 1–12 · score 0.57 · optiBET, skull stripping, preprocessing

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 · 83 lines · 2.8 KB · no license · 1 match

  1. function [y_predict, performance,randinds,pmask_hold] = cpm_main(x,y,varargin)
  2. % Performs Connectome-Based Predictive Modeling (CPM)
  3. %
  4. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  5. %
  6. % REQUIRED INPUTS
  7. % x Predictor variable (e.g., connectivity matrix)
  8. % Allowed dimensions are 2D (n x nsubs) OR 3D (nx m x nsubs)
  9. % y Outcome variable (e.g., behavioral scores)
  10. % Allowed dimensions are 2D (i x nsubs)
  11. % z Variable to use during partial correlation during
  12. % feature selection (e.g., head motion)
  13. % Allowed dimensions are 2D (i x nsubs)
  14. % 'pthresh' p-value threshold for feature selection
  15. % 'kfolds' Number of partitions for dividing the sample
  16. % (e.g., 2 =split half, 10 = ten fold)
  17. % 'corrtype' Type of correlation we want to do (Pearson, partial,
  18. % etc.)
  19. % 'z' If using partialcorr, indicates the variable to
  20. % control for
  21. %
  22. %
  23. %
  24. % OUTPUTS
  25. % y_predict Predictions of outcome variable
  26. % performance Correlation between predicted and actual values of y
  27. % randinds How we are randomizing for CV (note that y_predict is
  28. % output in the same order as y
  29. % pmask keeping track of which edges are used in CPM
  30. %
  31. % Example:
  32. %
  33. % [y_predict, performance,randinds,pmask] = cpm_main(matrices,behav,'pthresh',0.05,'kfolds',10,'corrtype','partial','z',head_motion);
  34. %
  35. %
  36. % References:
  37. % If you use this script, please cite:
  38. % Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M.,
  39. % Papademetris, X., & Constable, R. T. (2017). Using connectome-based
  40. % predictive modeling to predict individual behavior from brain connectivity.
  41. % Nature Protocols, 12(3), 506.
  42. %
  43. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  46. %% Parse input
  47. p=inputParser;
  48. defaultpthresh=0.01;
  49. defaultkfolds=2;
  50. defaultcorrtype='pearsons';
  51. defaultz=[];
  53. addRequired(p,'x',@isnumeric);
  54. addRequired(p,'y',@isnumeric); % must be n x nsubs
  55. addParameter(p,'pthresh',defaultpthresh,@isnumeric);
  56. addParameter(p,'kfolds',defaultkfolds,@isnumeric);
  57. addParameter(p,'corrtype',defaultcorrtype);
  58. addParameter(p,'z',defaultz,@isnumeric);
  60. parse(p,x,y,varargin{:});
  62. pthresh = p.Results.pthresh;
  63. kfolds = p.Results.kfolds;
  64. corrtype=p.Results.corrtype;
  65. z=p.Results.z;
  68. clearvars p
  70. %% Check for errors
  71. [x,y]=cpm_check_errors(x,y,kfolds);
  73. %% Train & test Connectome-Based Predictive Model
  74. [y_predict,randinds,pmask_hold]=cpm_cv(x,y,pthresh,kfolds,'corrtype',corrtype,'z',z);
  78. %% Assess performance
  79. [performance(1),performance(2)]=corr(y_predict(:),y(:));
  81. %fprintf('\nDone.\n')
  83. end

cpm_main.m at commit 55ff3df, no license · at the source

Overview

Authors: Corey Horien1,2,3, Francesca Mandino4, Abigail S Greene2,5, Xilin Shen4, Kelly Powell6, Angelina Vernetti6, David O’Connor7, Brendan D Adkinson2, Link Tejavibulya8, James C McPartland6,9, Fred R Volkmar6,9, Marvin Chun9,10, Katarzyna Chawarska6,11,12, Evelyn M R Lake4,10,13, Monica D Rosenberg14,15, Theodore Satterthwaite1,3,16, Dustin Scheinost4,6,10,11,17, Emily S Finn18, R Todd Constable2,4,13,17,19
ORCID iDs: Corey Horien, Francesca Mandino, Xilin Shen, David O’Connor, Brendan D Adkinson, James C McPartland, Katarzyna Chawarska, Monica D Rosenberg, Theodore Satterthwaite, Dustin Scheinost, Emily S Finn, R Todd Constable
19 affiliations
  1. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
  2. MD–PhD Program, Yale School of Medicine, New Haven, CT USA
  3. Penn Lifespan Informatics and Neuroimaging Center (PennLINC), University of Pennsylvania, Philadelphia, PA USA
  4. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT USA
  5. Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA USA
  6. Child Study Center, Yale School of Medicine, New Haven, CT USA
  7. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
  8. Department of Radiology, Weill Cornell Medicine, New York, NY USA
  9. Department of Psychology, Yale University, New Haven, CT USA
  10. Wu Tsai Institute, Yale University, New Haven, CT USA
  11. Department of Statistics and Data Science, Yale University, New Haven, CT USA
  12. Department of Pediatrics, Yale School of Medicine, New Haven, CT USA
  13. Department of Biomedical Engineering, Yale University, New Haven, CT USA
  14. Department of Psychology, University of Chicago, Chicago, IL USA
  15. Neuroscience Institute, University of Chicago, Chicago, IL USA
  16. Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA USA
  17. Interdepartmental Neuroscience Program, Yale University, New Haven, CT USA
  18. Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH USA
  19. Department of Neurosurgery, Yale School of Medicine, New Haven, CT USA
Institutions: Yale University (United States); University of Pennsylvania (United States); Brigham and Women's Hospital (United States); Icahn School of Medicine at Mount Sinai (United States); Weill Cornell Medicine (United States); University of Chicago (United States); Dartmouth College (United States)
Journal: Nature. Mental health, volume 4, issue 5, pages 792-805
Dates: received 10 January 2025; accepted 3 March 2026; published online 21 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s44220-026-00623-7 · PMID 42137910 · PMCID PMC13167459 · OpenAlex W7155094561
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), autism (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging
Keywords: Diagnostic markers, Attention
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (T32GM007205, TR001864); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R25MH119043, P50MH115716)
Citations: not cited yet (Europe PMC); 129 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 2 matches between paragraphs and lines of code.

clhorien/tasks_versus_rest_in_autism_prediction

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 55ff3dfbda427a8369976acd73a5de256f137296, 10 September 2025
Languages: MATLAB (5), Shell (1)
Size: 14 files, 6 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 (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
7 files
At the source: github.com/clhorien/tasks_versus_rest_in_autism_prediction/tree/main

Code availability statement

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Read it in the paper: doi.org/10.1038/s44220-026-00623-7.

Tracing map

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  • 6 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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/s44220-026-00623-7.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 2 keywords, 2 funders, 122 references.

Cite

This paper

Horien, C., Mandino, F., Greene, A. S., Shen, X., Powell, K., Vernetti, A., O’Connor, D., Adkinson, B. D., Tejavibulya, L., McPartland, J. C., Volkmar, F. R., Chun, M., Chawarska, K., Lake, E. M. R., Rosenberg, M. D., Satterthwaite, T., Scheinost, D., Finn, E. S., & Constable, R. T. (2026). Optimizing functional connectivity scanning conditions for predicting autistic traits. Nature. Mental health, 4(5), 792-805. https://doi.org/10.1038/s44220-026-00623-7

BibTeX

@article{horien2026optimizing,
author = {Horien, Corey and Mandino, Francesca and Greene, Abigail S and Shen, Xilin and Powell, Kelly and Vernetti, Angelina and O’Connor, David and Adkinson, Brendan D and Tejavibulya, Link and McPartland, James C and Volkmar, Fred R and Chun, Marvin and Chawarska, Katarzyna and Lake, Evelyn M R and Rosenberg, Monica D and Satterthwaite, Theodore and Scheinost, Dustin and Finn, Emily S and Constable, R Todd},
title = {{Optimizing functional connectivity scanning conditions for predicting autistic traits}},
journal = {Nature. Mental health},
year = {2026},
month = apr,
volume = {4},
number = {5},
pages = {792--805},
publisher = {Springer Science+Business Media},
issn = {2731-6076},
doi = {10.1038/s44220-026-00623-7},
url = {https://doi.org/10.1038/s44220-026-00623-7},
pmid = {42137910},
pmcid = {PMC13167459}
}

RIS

TY - JOUR
AU - Horien, Corey
AU - Mandino, Francesca
AU - Greene, Abigail S
AU - Shen, Xilin
AU - Powell, Kelly
AU - Vernetti, Angelina
AU - O’Connor, David
AU - Adkinson, Brendan D
AU - Tejavibulya, Link
AU - McPartland, James C
AU - Volkmar, Fred R
AU - Chun, Marvin
AU - Chawarska, Katarzyna
AU - Lake, Evelyn M R
AU - Rosenberg, Monica D
AU - Satterthwaite, Theodore
AU - Scheinost, Dustin
AU - Finn, Emily S
AU - Constable, R Todd
TI - Optimizing functional connectivity scanning conditions for predicting autistic traits
T2 - Nature. Mental health
J2 - Nat Ment Health
PY - 2026
DA - 2026/04/21
VL - 4
IS - 5
SP - 792
EP - 805
SN - 2731-6076
PB - Springer Science+Business Media
DO - 10.1038/s44220-026-00623-7
UR - https://doi.org/10.1038/s44220-026-00623-7
LA - en
ER -

CSL-JSON

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