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Longitudinal development of the anterior insula-nucleus accumbens white matter pathway through adolescence predicts risk taking in young adulthood.

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7 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 7 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › DWI preprocessing and tractography ↔ s_mrtrix_track_ains_nacc.m, the whole file · a weak match · score 0.82 · MRtrix, FA cutoff, white matter mask, Lmax, tracking, curvature
  2. [2] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › DWI preprocessing and tractography ↔ s_dtiInit.m, the whole file · a weak match · score 0.73 · AC PC space, isotropic voxels, raw, transformation, preprocessing, DWI
  3. [3] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › DWI preprocessing and tractography ↔ gen_mrtrix_track_roi2roi.m, the whole file · a weak match · score 0.71 · spherical deconvolution, MRtrix, probabilistic, tracking, cutoff, curvature
  4. [4] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › DWI preprocessing and tractography ↔ s_mrtrix_track_ains_nacc.m, the whole file · a weak match · score 0.67 · anterior insula, white matter mask, subcortical, tracking, NAcc, brain
  5. [5] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › DWI acquisition ↔ s_dtiInit.m, the whole file · a weak match · score 0.66 · phase encoding, isotropic voxels, flip, DWI
  6. [6] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › Structural coherence of the AIns-NAcc tract ↔ s_mrtrix_tractprofiles_ainsnacc.m, lines 175–246 · score 0.61 · tract profile, diffusion properties, sub, MD, AD, FA
  7. [7] § Methods › Diffusion-weighted imaging (DWI; Timepoints 1–4) › DWI preprocessing and tractography ↔ s_mrtrix_tractprofiles_ainsnacc.m, lines 86–129 · score 0.50 · AC PC, mrDiffusion

Paper

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The authors' code

MATLAB · 139 lines · 6.5 KB · no license · 2 matches

  1. function fibersPDB = s_mrtrix_track_ains_nacc
  2. %
  3. % This functions shows how to track between two ROIS using mrtrix.
  4. %
  5. % How the code works:
  6. % 1. We load two ROIs in the brain, for example anterior insula and NAcc
  7. % 2. We create union ROI by combining the two ROIs. The union ROI is seeded
  8. %` for the fibers. mrtrix will initiate and terminate fibers only
  9. % within the union ROI.
  10. % 3. We created a white matter mask. This mask is generally a large portion
  11. % of the white matter. A portion that contains both union ROIs. For example
  12. % the whole right hemisphere.
  13. % 4. We use mrtrix to track between the rightanterior insula and right NAcc.
  14. % mrtrix will initiate fibers by seeding within the union ROI and it will
  15. % only keep fibers that have paths within the white matter mask.
  16. %
  17. % The final result of this script is to generate lot's of candidate fibers
  18. % that specifically start and end in our ROIs.
  19. %
  20. % INPUTS: none
  21. % OUTPUTS: the final name of the ROI created at each iteration
  22. %
  23. % Written by Franco Pestilli (c) Stanford University Vistasoft
  25. % Path to subjects
  26. datapath = '/media/lcne/matproc';
  28. % Subject names
  29. subjects = {'am160914'};
  31. % Hemispheres
  32. hemis = {'lh','rh'};
  34. for isubj = 1:length(subjects)
  36. subjectDir = [subjects{isubj}];
  37. subjectRefImg = [subjects{isubj} '_t1_acpc.nii.gz'];
  38. dtFile = fullfile(baseDir, subjectDir, '/dti96trilin/dt6.mat');
  39. refImg = fullfile(baseDir, subjectDir, subjectRefImg);
  40. fibersFolder = fullfile(baseDir, subjectDir, '/dti96trilin/fibers/mrtrix/');
  41. roiFolder = fullfile(baseDir, subjectDir, 'ROIs');
  43. % We want to track the subcortical pathway
  44. fromRois = '_antshortins_fd';
  45. toRois = {'_nacc_aseg_fd'};
  46. wmMaskFS = '_wmmask_fs_fd';
  48. % Set up the MRtrix tracking parameters
  49. trackingAlgorithm = {'prob'};
  50. lmax = [10]; % The appropriate value depends on # of directions. For 32, use lower #'s like 4 or 6. For 70+ dirs, 6 or 10 is good [10];
  51. maxNFibers2try2find = 5000; % 10000; % this the number of fibers to find
  52. maxNFibers2try = 500000; %1000000; % this is the max number of fibers to try before giving up
  53. cutoff = 0.1; %FA cutoff along path
  54. initcutoff = 0.1; %FA cutoff at seed
  55. curvature = 1; %curvature radius. formula: angle = 2 * asin (S / (2*R)), S=step-size, R=radius of curvature
  56. stepsize = 0.2; %voxel-voxel step distance
  57. wmMask = [];
  59. for hemi = 1:length(hemis)
  60. fromRoiHemi = [hemis{hemi} fromRois];
  61. wmMaskHemi = [hemis{hemi} wmMaskFS];
  62. wmMaskName = fullfile(roiFolder, wmMaskHemi);
  64. % Make an (include) white matter mask ROI. This mask is the smallest
  65. % set of white matter that contains both ROIS (fromRois and toRois)
  66. %
  67. % We use a nifti ROi to select the portion of the White matter to use for
  68. % seeding
  69. [~, wmMaskName] = dtiRoiNiftiFromMat(wmMaskName,refImg,wmMaskName,1);
  71. % Then transform the niftis into .mif
  72. [p,f,e] = fileparts(wmMaskName);
  73. wmMaskMifName = fullfile(p,sprintf('%s.mif',f));
  74. wmMaskNiftiName = sprintf('%s.nii.gz',wmMaskName);
  75. mrtrix_mrconvert(wmMaskNiftiName, wmMaskMifName);
  77. % This first step initializes all the files necessary for mrtrix.
  78. % This can take a long time.
  79. files = mrtrix_init(dtFile,lmax,fibersFolder,wmMask);
  81. % Convert the ROIs from .mat or .nii.gz to .mif format.
  82. fromRoiName = fullfile(baseDir, subjectDir, '/ROIs/', fromRoiHemi);
  83. [~, fromRoiName] = dtiRoiNiftiFromMat(fromRoiName, refImg, fromRoiName, 1);
  84. fromRoiMifName = fullfile(p,sprintf('%s.mif',fromRoiHemi));
  85. fromRoiNiftiName = sprintf('%s.nii.gz',fromRoiName);
  86. mrtrix_mrconvert(fromRoiNiftiName, fromRoiMifName);
  88. % loop across toRois, must recreate hemisphere name
  89. for i_roi = 1:length(toRois)
  90. toRoiHemi = [hemis{hemi} toRois{i_roi}];
  91. toRoiName = fullfile(baseDir, subjectDir, '/ROIs/', toRoiHemi);
  92. [~, toRoiName] = dtiRoiNiftiFromMat(toRoiName, refImg, toRoiName, 1);
  93. toRoiMifName = fullfile(p,sprintf('%s.mif',toRoiHemi));
  94. toRoiNiftiName = sprintf('%s.nii.gz',toRoiName);
  95. mrtrix_mrconvert(toRoiNiftiName, toRoiMifName);
  96. end
  98. % Create joint from/to Rois to use as a mask
  99. for nRoi = 1:length(toRois)
  100. % MRTRIX tracking between 2 ROIs template.
  101. roi{1} = fullfile(baseDir, subjectDir, '/ROIs/', fromRoiHemi);
  102. % loop across toRois, must recreate hemisphere name
  103. toRoiHemi = [hemis{hemi} toRois{nRoi}];
  104. roi{2} = fullfile(baseDir, subjectDir, '/ROIs/', toRoiHemi);
  106. roi1 = dtiRoiFromNifti([roi{1} '.nii.gz'],[],[],'.mat');
  107. roi2 = dtiRoiFromNifti([roi{2} '.nii.gz'],[],[],'.mat');
  109. % Make a union ROI to use as a seed mask:
  110. % We will generate as many seeds as requested but only inside the voume
  111. % defined by the Union ROI.
  112. %
  113. % The union ROI is used as seed, fibers will be generated starting ONLy
  114. % within this union ROI.
  115. roiUnion = roi1; % seed union roi with roi1 info
  116. roiUnion.name = ['union of ' roi1.name ' and ' roi2.name]; % r lgn calcarine';
  117. roiUnion.coords = vertcat(roiUnion.coords,roi2.coords);
  118. roiName = fullfile(baseDir, subjectDir, '/ROIs/',[roi1.name '_' roi2.name '_union']);
  119. [~, seedMask] = dtiRoiNiftiFromMat(roiUnion,refImg,roiName,1);
  120. seedRoiNiftiName= sprintf('%s.nii.gz',seedMask);
  121. seedRoiMifName = sprintf('%s.mif',seedMask);
  123. % Transform the niftis into .mif
  124. mrtrix_mrconvert(seedRoiNiftiName, seedRoiMifName);
  126. % We cd into the folder where we want to sae the fibers.
  127. cd(fibersFolder);
  129. % We genenrate and save the fibers in the current folder.
  130. [fibersPDB{nRoi}, status, results] = gen_mrtrix_track_roi2roi(files, [roi{1} '.mif'], [roi{2} '.mif'], ...
  131. seedRoiMifName, wmMaskMifName, trackingAlgorithm{1}, ...
  132. maxNFibers2try2find, maxNFibers2try, cutoff, initcutoff, curvature, stepsize);
  134. %fgWrite(fibersPDB,['fibername'],'pwd')
  135. end
  136. end
  137. end
  139. return

s_mrtrix_track_ains_nacc.m at commit c06caf6, no license · at the source

Overview

Authors: Lauren R Borchers1, Chase Antonacci1,2, Josiah K Leong3,4, Ian H Gotlib1
ORCID iDs: Chase Antonacci
  1. Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, USA
  2. Neurosciences Interdepartmental Program, Stanford University School of Medicine, 290 Jane Stanford Way, Stanford, CA 94305, USA
  3. Department of Psychological Science, University of Arkansas, 216 Memorial Hall, Fayetteville, AR 72701, USA
  4. Alice L. Walton School of Medicine, 1001 NE J Street, Bentonville, AR 72712, USA
Institutions: Stanford University (United States); Stanford Medicine (United States); Alice L. Walton School of Medicine (United States); University of Arkansas at Fayetteville (United States)
Journal: Developmental cognitive neuroscience, volume 79, article 101730
Dates: received 22 December 2025; accepted 22 April 2026; published online 24 April 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.dcn.2026.101730 · PMID 42054974 · PMCID PMC13137198 · OpenAlex W7155531225
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: Anterior insula, Nucleus accumbens, Diffusion-weighted imaging, White matter, Risk taking, Adolescence
MeSH: Insular Cortex*, Nucleus Accumbens*, Risk-Taking*, White Matter*, Adolescent, Child, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Longitudinal Studies, Male, Neural Pathways, Young Adult (* major topic)
Topic: Prenatal Substance Exposure Effects (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: Arkansas Biosciences Institute; National Institute of Mental Health (R37MH101495)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Adolescence is characterized by increases in risk-taking behaviors, including substance use, sexual activity, and impulsive decision-making. Neuroimaging research implicates the anterior insula (AIns) and nucleus accumbens (NAcc) in risk evaluation and reward motivation; however, little is known about how the structural pathway connecting these regions develops across adolescence or whether variation in its development predicts later risk-taking. In this study, we examined whether longitudinal changes in white-matter connectivity between the AIns and NAcc during adolescence predict risk-taking in early adulthood. A total of 196 youth (ages 9–20 years) contributed 486 diffusion-weighted imaging scans across four waves spanning six years. We reconstructed the AIns-NAcc tract using probabilistic tractography and extracted fractional anisotropy (FA) along the tract. At a fifth assessment (ages 19–23), 105 participants completed the Adolescent and Young Adult Health Questionnaire; exploratory factor analysis identified a risk-taking factor indexing substance use and sexual risk-taking. We used linear mixed-effects models to characterize developmental trajectories of FA and tested their associations with early adult risk-taking, adjusting for biological sex, early life stress, and behavioral sensitivity to reward and punishment. FA increased linearly across adolescence in both hemispheres (right: β=0.17, p < .001; left: β=0.10, p = .023). Critically, individuals exhibiting shallower increases in FA across adolescence in the right hemisphere reported greater engagement in risk-taking behaviors in early adulthood (ΔR2=4.0%, p = .024). These findings suggest that adolescence represents a sensitive period during which individual differences in maturation of the right AIns-NAcc pathway prospectively shape later risk-taking, highlighting the importance of longitudinally modeling structural connectivity in reward-related circuits.

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

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josiahkl/spantracts

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: c06caf6a6b65f6edebca01b7d7705a78c4a05f07, 10 July 2020
Languages: MATLAB (10)
Size: 11 files, 10 scripts
Software Heritage: archived
Found in: the text, “DWI preprocessing and tractography”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
11 files
At the source: github.com/josiahkl/spantracts

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 14 MeSH terms, 2 funders, 60 references.

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This paper

Borchers, L. R., Antonacci, C., Leong, J. K., & Gotlib, I. H. (2026). Longitudinal development of the anterior insula-nucleus accumbens white matter pathway through adolescence predicts risk taking in young adulthood. Developmental cognitive neuroscience, 79, 101730. https://doi.org/10.1016/j.dcn.2026.101730

BibTeX

@article{borchers2026longitudinal,
author = {Borchers, Lauren R and Antonacci, Chase and Leong, Josiah K and Gotlib, Ian H},
title = {{Longitudinal development of the anterior insula-nucleus accumbens white matter pathway through adolescence predicts risk taking in young adulthood}},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = apr,
volume = {79},
pages = {101730},
publisher = {Elsevier},
issn = {1878-9293},
doi = {10.1016/j.dcn.2026.101730},
url = {https://doi.org/10.1016/j.dcn.2026.101730},
pmid = {42054974},
pmcid = {PMC13137198}
}

RIS

TY - JOUR
AU - Borchers, Lauren R
AU - Antonacci, Chase
AU - Leong, Josiah K
AU - Gotlib, Ian H
TI - Longitudinal development of the anterior insula-nucleus accumbens white matter pathway through adolescence predicts risk taking in young adulthood
T2 - Developmental cognitive neuroscience
J2 - Dev Cogn Neurosci
PY - 2026
DA - 2026/04/24
VL - 79
SP - 101730
SN - 1878-9293
PB - Elsevier
DO - 10.1016/j.dcn.2026.101730
UR - https://doi.org/10.1016/j.dcn.2026.101730
LA - en
ER -

CSL-JSON

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