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A multimodal imaging-based integrative framework for HIV-associated cognitive impairment and treatment response.

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. [1] § Materials and methods › Data analysis › Fractal dimensionality (FD) ↔ calcFD/calcFD.m, lines 1–60 · score 0.82 · Fractal dimensionality, cortical ribbon, FreeSurfer, calcFD, intermediate, unparcellated
  2. [2] § Materials and methods › Data analysis › Fractal dimensionality (FD) ↔ examples/example_nii.m, lines 23–33 · score 0.52 · Fractal dimensionality, calcFD, box, algorithm

Paper

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The authors' code

MATLAB · 308 lines · 12 KB · GPL-3.0 · 1 match

  1. function [fd,subjects] = calcFD(subjects,subjectpath,options)
  2. % Calculate the fractal dimensionality of a 3D structure.
  3. % Designed to work with intermediate files from FreeSurfer analysis pipeline
  4. % (ribbon.mgz, aparc.a2009s+aseg.mgz, and others).
  5. % Also can use other mgz volume as input (e.g., see 'benchmark folder').
  6. %
  7. % See 'wrapper_sample.m' for an example of how to use the calcFD toolbox.
  8. %
  9. % REQUIRED INPUTS:
  10. % subjects = list of subjects names in a cell array.
  11. % Alternatively accepts {'.'} to run on all subjects in folder.
  12. % {'.'} will only work if there are no non-subject directories
  13. % in the folder, but does skip 'fsaverage' and 'fmirprep'.
  14. %
  15. % subjectpath = FreeSurfer 'SUBJECTDIR' where standard directory structure is
  16. %
  17. % options = specify details of running the analysis
  18. %
  19. % options.alg = 'dilate' | 'boxcount'
  20. %
  21. % options.countFilled = 0 | 1
  22. % 0 == Surface-only (FDs)
  23. % 1 == Filled volume (FDf)
  24. %
  25. % options.aparc = 'Ribbon' | 'Dest_aparc' | 'Dest_select' | 'DKT' | 'Economo' | 'none'
  26. % 'Ribbon' == Cortical Ribbon (unparcellated)
  27. % 'Dest_aparc' == Parcellated cortical regions (Destrieux)
  28. % ** requires options.input.
  29. % 'Dest_select' == Any region in the aparc.a2009s+aseg.mgz volume,
  30. % ** requires options.input.
  31. % 'DKT' == Parcellated cortical regions (DKT).
  32. % ** requires aparc.DKTatlas+aseg.mgz (FS 6) or
  33. % aparc.DKTatlas40+aseg.mgz (FS 5.3) to exist.
  34. % The volume can be generated (FS 5.3) using:
  35. % mri_aparc2aseg --s [SUBJECTID] --annot aparc.DKTatlas40
  36. % See Madan & Kensinger (2017, Brain Informatics) for further details.
  37. % 'Economo' == Parcellated cortical regions (von Economo-Koskinas).
  38. % ** requires economo+aseg.mgz to exist.
  39. % The volume can be generated using:
  40. % mris_ca_label, mris_anatomical_stats
  41. % See Scholtens et al. (2018, NeuroImage) and
  42. % Madan & Kensinger (2018, Eur J Neurosci) for further details.
  43. % 'none' == Binarized volume to be manually entered
  44. % (e.g., benchmark volumes).
  45. %
  46. % options.input = filename string, required for 'Dest_aparc' and 'Dest_select
  47. % if options.aparc == 'Dest_aparc'
  48. % This should be a file with the name 'mask_*.txt',
  49. % where * is the value in options.input.
  50. % File should have either 74 or 148 rows, only 1 column.
  51. % If only 74 values, labels are assigned bilaterally.
  52. % Value in each row is the label to assign to that parcellated region,
  53. % based on the Destrieux et al. (2010) parcellation scheme.
  54. % See 'mask_lobe.txt' for an example.
  55. % See 'calcFD_mask.xlsx' for a list of which regions correspond to each row number.
  56. % --
  57. % if options.aparc == 'Dest_select'
  58. % This should be a file with the name 'select_*.txt',
  59. % where * is the value in options.input.
  60. % Regions correspond to intensity values in aparc.a2009s+aseg.mgz.
  61. % See FreeSurfer files (e.g., FreeSurferColorLUT.txt, ASegStatsLUT.txt,
  62. % WMParcStatsLUT.txt) for mapping of region intensities to names.
  63. % Multiple region values on the same row will be processed as a single structure.
  64. % Currently cannot use the same region in more than one row,
  65. % if need to violate this, use multiple input text files.
  66. % See 'select_subcort.txt' and 'select_ventricles.txt' for examples.
  67. %
  68. %
  69. % options.labelfile = filename string of the text file providing the label names of the analyzed brain regions.
  70. % Can be either 'none' if no such file is available or '*.txt',
  71. % see 'select_subcort_legend.txt' for an example.
  72. % If specified as 'none', brain regions will be labeled numerically.
  73. %
  74. %
  75. % options.output = filename string to output FD values to
  76. %
  77. %
  78. % OPTIONAL INPUTS:
  79. % options.boxsizes = list of numbers
  80. % Default: 2.^[0:4] (resolves to [1,2,4,8,16])
  81. % Specify what 'box sizes' (also applies to dilation algorithm) to use
  82. % when calculating FD.
  83. % Preferred to scale in powers of two.
  84. %
  85. % ----
  86. %
  87. % The calcFD toolbox is available from: http://cmadan.github.io/calcFD/.
  88. %
  89. % Please cite this paper if you use the toolbox:
  90. % Madan, C. R., & Kensinger, E. A. (2016). Cortical complexity as a measure of
  91. % age-related brain atrophy. NeuroImage, 134, 617-629.
  92. % doi:10.1016/j.neuroimage.2016.04.029
  93. %
  94. % If you use the toolbox with subcortical/ventricular structures, please also cite:
  95. % Madan, C. R., & Kensinger, E. A. (2017). Age-related differences in the structural
  96. % complexity of subcortical and ventricular structures. Neurobiology of Aging, 50, 87-95.
  97. % doi:10.1016/j.neurobiolaging.2016.10.023
  98. %
  99. %
  100. % 20180517 CRM
  101. % build 31
  102. %
  103. % 20180503 SK: modified to include text file with label names as input,
  104. % see 'options.labelfile' above and the examples in wrapper_sample.m and
  105. % wrapper_sample_subcort.m.
  107. % process optional inputs
  108. if ~isfield(options,'boxsizes')
  109. options.boxsizes = 2.^[0:4];
  110. % resolves to [1,2,4,8,16]
  111. end
  113. % make labelfile optional
  114. if ~isfield(options,'labelfile')
  115. options.labelfile = 'none';
  116. end
  117. % 20180503 SK: check if label file is provided
  118. if strcmp(options.labelfile, 'none')
  119. labelfile = 'none' ;
  120. else
  121. labelfile = options.labelfile ;
  122. end
  124. % get full list of subjects if asked
  125. if strcmp(subjects{1},'.');
  126. list = dir(fullfile(subjectpath));
  127. list = {list([list.isdir]).name};
  129. % exclude the known non-subject folders
  130. excl = [1:2]; % skip '.' and '..'
  131. excl = [excl find(cellfun(@length,strfind(list,'fsaverage')))];
  132. excl = [excl find(cellfun(@length,strfind(list,'fmriprep')))];
  133. list = list(setdiff(1:length(list),excl));
  134. subjects = list;
  136. elseif length(strfind(subjects{1},'*'))==1
  137. % subjects name has a wildcard, but only expect one entry then
  138. list = dir(fullfile(subjectpath,subjects{1}));
  139. list = {list([list.isdir]).name};
  140. subjects = list;
  141. end
  143. % error handling
  144. if ~exist('strlen')
  145. disp('MATLAB-FreeSurfer functions not found in MATLAB path.')
  146. disp('Please see https://surfer.nmr.mgh.harvard.edu/fswiki/UserContributions/FAQ#FreeSurfer.26Matlab')
  147. end
  149. for s = 1:length(subjects)
  150. fprintf('Calculating FD for subject %s...',subjects{s})
  151. failed = 0;
  152. % load the desired aparc
  153. switch options.aparc
  154. case {'ribbon','Ribbon'}
  155. vol_fname = fullfile(subjectpath,subjects{s},'mri','ribbon.mgz');
  156. vol = load_mgh(vol_fname);
  157. % in cortical ribbon, GM = 42/3
  158. vol = (vol==3) | (vol==42);
  159. labels = 1;
  161. case 'Dest_aparc'
  162. vol_fname = fullfile(subjectpath,subjects{s},'mri','aparc.a2009s+aseg.mgz');
  163. vol = load_mgh(vol_fname);
  164. labels = unique(vol);
  165. labels = labels(labels>10000);
  167. % load mask assignment
  168. mask = load(['mask_' options.input '.txt']);
  169. if length(mask) == 74
  170. % duplicate for other hemi
  171. mask = [ mask; mask ];
  172. end
  174. % reassign intensities using mask
  175. vol_mask = zeros(size(vol));
  176. for l = labels'
  177. vol_mask(vol==l) = mask(labels==l);
  178. end
  180. % replace vol with vol_mask
  181. vol = vol_mask;
  182. labels = unique(vol);
  183. labels = setdiff(labels,0);
  185. case 'Dest_select'
  186. vol_fname = fullfile(subjectpath,subjects{s},'mri','aparc.a2009s+aseg.mgz');
  187. vol = load_mgh(vol_fname);
  188. labels = unique(vol);
  190. % load select assignment
  191. select = load(['select_' options.input '.txt']);
  193. % reassign intensities using mask
  194. vol_mask = zeros(size(vol));
  195. for l = select(:)'
  196. ll = find(sum(select==l,2)); % line number
  197. % fix for limitation of 'each row to have the same number of values'
  198. vx = vol==l;
  199. if sum(vx) == 0
  200. %disp(sprintf('No match for %g.',l))
  201. else
  202. vol_mask(vx) = ll;
  203. end
  204. % patch end
  205. end
  207. % replace vol with vol_mask
  208. vol = vol_mask;
  209. labels = unique(vol);
  210. labels = setdiff(labels,0);
  212. case 'DKT'
  213. try
  214. % FreeSurfer 5.3, requires mri_aparc2aseg to also be run
  215. vol_fname = fullfile(subjectpath,subjects{s},'mri','aparc.DKTatlas40+aseg.mgz');
  216. vol = load_mgh(vol_fname);
  217. catch
  218. % FreeSurfer 6.0, volume should be created automatically by standard recon-all pipeline
  219. vol_fname = fullfile(subjectpath,subjects{s},'mri','aparc.DKTatlas+aseg.mgz');
  220. vol = load_mgh(vol_fname);
  221. end
  222. labels = unique(vol);
  223. % only the cortical regions
  224. labels = labels(labels>999);
  225. % remove the 'unknown' regions
  226. labels = setdiff(labels,[1000 2000]);
  228. case {'Economo'}
  229. % see help for direction on how to generate this volume
  230. vol_fname = fullfile(subjectpath,subjects{s},'mri','economo+aseg.mgz');
  231. vol = load_mgh(vol_fname);
  232. labels = unique(vol);
  233. % only the cortical regions
  234. labels = labels(labels>999);
  235. % remove the 'unknown' regions
  236. labels = setdiff(labels,[1000 2000]);
  238. case 'none'
  239. vol_fname = fullfile(subjectpath,[subjects{s} '.mgz']);
  240. vol = load_mgh(vol_fname);
  241. labels = unique(vol);
  242. labels = setdiff(labels,0);
  244. otherwise
  245. % not sure what to do with that request...
  246. disp(sprintf('%s is not a valid parcellation scheme',options.aparc));
  247. failed = 1;
  248. end
  249. fprintf('%g region(s).\n',length(labels))
  251. if failed == 0
  252. for l = 1:length(labels)
  253. if length(labels) > 1 & mod(l,10)==0
  254. fprintf('%g...',l)
  255. end
  257. % box sizes to measure complexity, scaled by powers of 2
  258. r = options.boxsizes;
  260. % extract portion of vol that is specific label
  261. vol_label = (vol==labels(l));
  262. % crop the volume, with padding of half of the largest box size
  263. vol_label = calcFD_volCrop(vol_label,max(r)/2);
  265. % if not counting filled voxels, need to hollow out the vol
  266. if options.countFilled == 0
  267. vol_label = calcFD_hollowVol(vol_label);
  268. end
  270. switch options.alg
  271. case 'boxcount'
  272. n = calcFD_boxcount(vol_label,r);
  273. case 'dilate'
  274. n = calcFD_dilate(vol_label,r);
  275. end
  277. if ~isnan(n)
  278. % linear fit
  279. c = [log2(r)' ones(length(r),1)] \ -log2(n)';
  280. else
  281. % failed
  282. c(1) = NaN;
  283. disp('Failed. Check input volume and options.input txt file.')
  284. end
  285. fd(s,l) = c(1); % slope
  286. end
  287. fprintf('done.\n')
  289. % for debugging
  290. % save([options.output(1:(end-4))])
  291. end
  292. end
  294. % error handling
  295. if ~exist('fd')
  296. disp('No FD values calculated. Please check that ''subjects'' and ''subjectpath'' were specified correctly.');
  297. end
  299. if isfield(options,'output')
  300. % output FD txt file to working directory
  302. calcFD_save(options.output,fd,subjects,labels,labelfile); % 20180502 SK: modified to include labelfile argument
  304. end
  305. end
  307. % delete debug temp file
  308. % delete([options.output(1:(end-4)) '.mat'])

calcFD.m at commit e49a81d, under GPL-3.0 · at the source

Overview

Authors: Xing Qiu1, Md Nasir Uddin2,3,4, Lu Wang1, Alicia Tyrell1, Abrar Faiyaz2, Nhat Hoang5, Yuchuan Zhuang4, Madalina E. Tivarus6,7, Jianhui Zhong3,5,6, Miriam T. Weber2,8, Giovanni Schifitto2,4,6
  1. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States
  2. Department of Neurology, University of Rochester, Rochester, NY, United States
  3. Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
  4. Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
  5. Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States
  6. Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
  7. Department of Neuroscience, University of Rochester, Rochester, NY, United States
  8. Department of Obstetrics and Gynecology, University of Rochester, Rochester, NY, United States
Institutions: University of Rochester (United States); University of Rochester Medicine (United States)
Journal: Frontiers in neuroscience, volume 20, article 1775047
Dates: received 24 December 2025; accepted 30 March 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1775047 · PMID 42051549 · PMCID PMC13111216 · OpenAlex W7154166492
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Complexity, fMRI & imaging
Keywords: brain, cognitive impairment, HIV, interpretable machine learning, MRI, multimodal integration, neuroimaging
Topic: HIV Research and Treatment (Virology, Immunology and Microbiology), according to OpenAlex
Funding: NIH (R01MH099921, R01NS132870)
Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Introduction: Combination antiretroviral therapy (cART) has been shown to reduce inflammation in persons with HIV (PWH), leading to overall improvements in cognition. However, these improvements are patient-dependent and not always observable over short treatment periods.

Methods: We applied a multimodal integrative model to associate various baseline MR neuroimaging metrics with baseline neurocognitive performance and their longitudinal changes over 12 weeks of cART treatment. Features in our model included volumetric data, cerebral blood flow metrics, cerebrovascular reactivity, and diffusion MRI data from cortical, subcortical, and white matter regions of the brain. Our integrative model, which includes multilayered principal component analysis, penalized regression, and feature weight back-propagation, is designed for “large p, small n” data and offers better interpretability than deep-learning methods.

Results: There is a modest association between imaging metrics and baseline neurocognitive scores for both PWH and age-matched healthy controls, driven primarily by subcortical regions. In contrast, baseline imaging features exhibited stronger associations with longitudinal changes in cognitive performance over 12 weeks of cART in PWH than with baseline cognitive scores. The multimodal integrative model outperformed all comparable unimodal models in explaining longitudinal cognitive change. Among unimodal analyses, models based on cerebral blood flow and free-water-corrected fractional anisotropy demonstrated the strongest associations. Frequently selected predictors included the frontal pole (cortical gray matter); the amygdala, putamen, and hippocampus (subcortical gray matter); and the posterior limb of the internal capsule (white matter).

Discussion: Our approach provides an interpretable statistical framework that integrates complementary information across various imaging modalities into a robust and interpretable model for short-term cognitive trajectories in PWH undergoing cART.

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 2 matches between paragraphs and lines of code.

cMadan/calcFD

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: e49a81d9f03a2bf705e10bb524201e505e4054ba, 29 April 2021
Languages: MATLAB (11)
Size: 37 files, 11 scripts
Software Heritage: not archived
Found in: the end of the paper
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
13 files
At the source: github.com/cMadan/calcFD

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 11 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);
  • 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 availability statement

The raw data supporting the conclusions of this article will be made available by the author, without undue reservation.

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

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 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 11 authors, 7 keywords, 1 funder, 55 references.

Cite

This paper

Qiu, X., Uddin, M. N., Wang, L., Tyrell, A., Faiyaz, A., Hoang, N., Zhuang, Y., Tivarus, M. E., Zhong, J., Weber, M. T., & Schifitto, G. (2026). A multimodal imaging-based integrative framework for HIV-associated cognitive impairment and treatment response. Frontiers in neuroscience, 20, 1775047. https://doi.org/10.3389/fnins.2026.1775047

BibTeX

@article{qiu2026multimodal,
author = {Qiu, Xing and Uddin, Md Nasir and Wang, Lu and Tyrell, Alicia and Faiyaz, Abrar and Hoang, Nhat and Zhuang, Yuchuan and Tivarus, Madalina E. and Zhong, Jianhui and Weber, Miriam T. and Schifitto, Giovanni},
title = {{A multimodal imaging-based integrative framework for HIV-associated cognitive impairment and treatment response}},
journal = {Frontiers in neuroscience},
year = {2026},
month = apr,
volume = {20},
pages = {1775047},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1775047},
url = {https://doi.org/10.3389/fnins.2026.1775047},
pmid = {42051549},
pmcid = {PMC13111216}
}

RIS

TY - JOUR
AU - Qiu, Xing
AU - Uddin, Md Nasir
AU - Wang, Lu
AU - Tyrell, Alicia
AU - Faiyaz, Abrar
AU - Hoang, Nhat
AU - Zhuang, Yuchuan
AU - Tivarus, Madalina E.
AU - Zhong, Jianhui
AU - Weber, Miriam T.
AU - Schifitto, Giovanni
TI - A multimodal imaging-based integrative framework for HIV-associated cognitive impairment and treatment response
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/04/13
VL - 20
SP - 1775047
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1775047
UR - https://doi.org/10.3389/fnins.2026.1775047
LA - en
ER -

CSL-JSON

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"id": "10.3389/fnins.2026.1775047",
"type": "article-journal",
"title": "A multimodal imaging-based integrative framework for HIV-associated cognitive impairment and treatment response",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Qiu",
"given": "Xing"
},
{
"family": "Uddin",
"given": "Md Nasir"
},
{
"family": "Wang",
"given": "Lu"
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{
"family": "Tyrell",
"given": "Alicia"
},
{
"family": "Faiyaz",
"given": "Abrar"
},
{
"family": "Hoang",
"given": "Nhat"
},
{
"family": "Zhuang",
"given": "Yuchuan"
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{
"family": "Tivarus",
"given": "Madalina E."
},
{
"family": "Zhong",
"given": "Jianhui"
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"family": "Weber",
"given": "Miriam T."
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"given": "Giovanni"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1775047",
"DOI": "10.3389/fnins.2026.1775047",
"PMID": "42051549",
"PMCID": "PMC13111216",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1775047",
"language": "en",
"issued": {
"date-parts": [
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}
}

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In common: Tools for NIfTI and ANALYZE image (MATLAB), FreeSurfer, structural MRI / diffusion, clinical / translational, 1 reference
[10] doi:10.7554/elife.107661 [code]
In vivo mapping of striatal neurodegeneration in Huntington's disease with Soma and Neurite Density Imaging.
Journal: eLife
In common: Tools for NIfTI and ANALYZE image (MATLAB), structural MRI / diffusion, clinical / translational, other condition, 3 references

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