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Individualized mapping of functional brain networks in older adulthood.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › IU & IADRC datasets: Detailed individualized mapping procedure ↔ network_encroachment.m, lines 1–70 · score 0.68 · lobe, Human, Template matching, MTL, PMN, PON
  2. [2] § Methods › IU dataset: Theory of mind localizer analysis ↔ PFM-Tutorial/pfm_tutorial.m, lines 76–90 · score 0.68 · Gaussian kernel, spatially smoothed, sigma, geodesic, volumes, Workbench
  3. [3] § Methods › IU & IADRC datasets: Detailed individualized mapping procedure ↔ network_state.m, lines 1–89 · score 0.67 · Template matching, MTL, PMN, PON, Tpole, VAN
  4. [4] § Methods › IU and IADRC: Image preprocessing › Denoising rest and movie-watching fMRI ↔ PFM-Tutorial/pfm_tutorial.m, lines 76–90 · score 0.64 · Gaussian kernel, spatially smoothed, geodesic, volumes, Workbench
  5. [5] § Methods › IU and IADRC: Image preprocessing › Denoising rest and movie-watching fMRI ↔ network_state.m, lines 1–89 · score 0.52 · XCP, DCAN, framewise, derivatives, filtering, censoring

Paper

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

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The authors' code

MATLAB · 176 lines · 7.2 KB · no license · 2 matches

  1. %% A tutorial covering precision functional mapping using an example dataset.
  3. %% Before you begin.
  5. % add dependencies to Matlab search path
  6. addpath(genpath([pwd '/PFM-Tutorial/Utilities']));
  8. % define path to some software packages that will be needed
  9. InfoMapBinary = '/home/charleslynch/miniconda3/bin/infomap'; % path to infomap binary; code tested on version 2.0.0
  10. WorkbenchBinary = '/usr/local/workbench/bin_linux64/wb_command'; % path to workbench binary; code tested on version 1.4.2
  12. % number of
  13. % workers
  14. nWorkers = 5;
  16. %% Step 1: Temporal Concatenation of fMRI data from all sessions.
  18. % define subject directory and name;
  19. Subdir = [pwd '/WCM-ME/derivatives/sub-ME01/'];
  20. Subject = 'ME01';
  22. % define & create
  23. % the pfm directory;
  24. PfmDir = [Subdir '/pfm/'];
  25. mkdir(PfmDir);
  27. % count the number of imaging sessions;
  28. nSessions = length(dir([Subdir '/processed_restingstate_timecourses/ses-func*']));
  30. % preallocate;
  31. ConcatenatedData = [];
  33. % sweep through
  34. % the sessions;
  35. for i = 1:nSessions
  37. % count the number of runs in this session
  38. nRuns = length(dir([Subdir '/processed_restingstate_timecourses/ses-func' sprintf('%02d',i) '/*run-*.dtseries.nii']));
  40. % sweep
  41. % through
  42. % the runs;
  43. for ii = 1:nRuns
  45. % load the denoised & fs_lr_32k surface-registered CIFTI file for run "ii" from session "i"...
  46. Cifti = ft_read_cifti_mod([Subdir '/processed_restingstate_timecourses/ses-func' sprintf('%02d',i) '/sub-' Subject '_ses-func' sprintf('%02d',i) '_task-rest_run-' sprintf('%02d',ii) '_bold_32k_fsLR.dtseries.nii']);
  47. Cifti.data = Cifti.data - mean(Cifti.data,2); % demean
  48. Tmask = load([Subdir '/processed_restingstate_timecourses/ses-func' sprintf('%02d',i) '/sub-' Subject '_ses-func' sprintf('%02d',i) '_task-rest_run-' sprintf('%02d',ii) '_bold_32k_fsLR_tmask.txt']);
  49. ConcatenatedData = [ConcatenatedData Cifti.data(:,Tmask==1)]; % 1 (Low motion timepoints) == FD < 0.3mm, 0 (High motion timepoints) == FD > 0.3mm
  51. end
  53. end
  55. % make a single CIFTI containing
  56. % time-series from all scans;
  57. ConcatenatedCifti = Cifti;
  58. ConcatenatedCifti.data = ConcatenatedData;
  60. %% Step 2: Make a distance matrix.
  62. % define fs_lr_32k midthickness surfaces;
  63. MidthickSurfs{1} = [Subdir '/fs_LR/fsaverage_LR32k/' Subject '.L.midthickness.32k_fs_LR.surf.gii'];
  64. MidthickSurfs{2} = [Subdir '/fs_LR/fsaverage_LR32k/' Subject '.R.midthickness.32k_fs_LR.surf.gii'];
  66. % make the distance matrix;
  67. pfm_make_dmat(ConcatenatedCifti,MidthickSurfs,PfmDir,nWorkers,WorkbenchBinary); %
  69. % optional: regress adjacent cortical signal from subcortex to reduce artifactual coupling
  70. % (for example, between cerebellum and visual cortex, or between putamen and insular cortex)
  71. [ConcatenatedCifti] = pfm_regress_adjacent_cortex(ConcatenatedCifti,[PfmDir '/DistanceMatrix.mat'],20);
  73. % write out the CIFTI file;
  74. ft_write_cifti_mod([Subdir '/pfm/sub-ME01_task-rest_concatenated_32k_fsLR.dtseries.nii'],ConcatenatedCifti);
  76. %% Step 3: Apply spatial smoothing.
  78. % define a range of gaussian
  79. % smoothing kernels (in sigma)
  80. KernelSizes = [0.85 1.7 2.55];
  82. % sweep a range of
  83. % smoothing kernels;
  84. for k = KernelSizes
  86. % smooth with geodesic (for surface data) and Euclidean (for volumetric data) Gaussian kernels;
  87. system([WorkbenchBinary ' -cifti-smoothing ' PfmDir '/sub-ME01_task-rest_concatenated_32k_fsLR.dtseries.nii '...
  88. num2str(k) ' ' num2str(k) ' COLUMN ' PfmDir '/sub-ME01_task-rest_concatenated_smoothed' num2str(k) '_32k_fsLR.dtseries.nii -left-surface ' MidthickSurfs{1} ' -right-surface ' MidthickSurfs{2} ' -merged-volume']);
  90. end
  92. %% Step 4: Run infomap.
  94. % load your concatenated resting-state dataset, pick whatever level of spatial smoothing you want
  95. ConcatenatedCifti = ft_read_cifti_mod([PfmDir '/sub-ME01_task-rest_concatenated_smoothed2.55_32k_fsLR.dtseries.nii']);
  97. % define inputs;
  98. DistanceMatrix = [Subdir '/pfm/DistanceMatrix.mat']; % can be path to file
  99. DistanceCutoff = 10; % in mm; usually between 10 to 30 mm works well.
  100. GraphDensities = flip([0.0001 0.0002 0.0005 0.001 0.002 0.005 0.01 0.02 0.05]); %
  101. NumberReps = 50; % number of times infomap is run;
  102. BadVertices = []; % optional, but you could include regions to ignore, if you know there is bad signal there.
  103. Structures = {'CORTEX_LEFT','CEREBELLUM_LEFT','ACCUMBENS_LEFT','CAUDATE_LEFT','PALLIDUM_LEFT','PUTAMEN_LEFT','THALAMUS_LEFT','HIPPOCAMPUS_LEFT','AMYGDALA_LEFT','ACCUMBENS_LEFT','CORTEX_RIGHT','CEREBELLUM_RIGHT','ACCUMBENS_RIGHT','CAUDATE_RIGHT','PALLIDUM_RIGHT','PUTAMEN_RIGHT','THALAMUS_RIGHT','HIPPOCAMPUS_RIGHT','AMYGDALA_RIGHT','ACCUMBENS_RIGHT'};
  105. % run infomap
  106. pfm_infomap(ConcatenatedCifti,DistanceMatrix,PfmDir,GraphDensities,NumberReps,DistanceCutoff,BadVertices,Structures,nWorkers,InfoMapBinary);
  108. % remove some intermediate files (optional)
  109. system(['rm ' Subdir '/pfm/*.net']);
  110. system(['rm ' Subdir '/pfm/*.clu']);
  111. system(['rm ' Subdir '/pfm/*Log*']);
  113. % define inputs;
  114. Input = [PfmDir '/Bipartite_PhysicalCommunities.dtseries.nii'];
  115. Output = 'Bipartite_PhysicalCommunities+SpatialFiltering.dtseries.nii';
  116. MinSize = 50; % in mm^2
  118. % perform spatial filtering
  119. pfm_spatial_filtering(Input,PfmDir,Output,MidthickSurfs,MinSize,WorkbenchBinary);
  121. %% Step 5: Algorithmic assignment of network identities to infomap communities.
  123. % load the priors;
  124. load('priors.mat');
  126. % define inputs;
  127. Ic = ft_read_cifti_mod([PfmDir '/Bipartite_PhysicalCommunities+SpatialFiltering.dtseries.nii']);
  128. Output = 'Bipartite_PhysicalCommunities+AlgorithmicLabeling';
  129. Column = 6; % column 6, representing graph density 0.01% in this example.
  131. % run the network identification algorithm;
  132. pfm_identify_networks(ConcatenatedCifti,Ic,MidthickSurfs,Column,Priors,Output,PfmDir,WorkbenchBinary);
  134. %% Step 6: Review algorithmic network assignments, optionally adjust labels manually if needed.
  136. % define inputs
  137. XLS = [PfmDir '/Bipartite_PhysicalCommunities+AlgorithmicLabeling_NetworkLabels+ManualDecisions.xls'];
  138. Output = 'Bipartite_PhysicalCommunities+FinalLabeling';
  140. % OPTIONAL: update network assignments according to manual decisions;
  141. pfm_parse_manual_decisions(Ic,Column,MidthickSurfs,Priors,XLS,Output,PfmDir,WorkbenchBinary);
  143. %% Step 7: Calculate size of each functional brain network
  145. % define inputs
  146. FunctionalNetworks = ft_read_cifti_mod([PfmDir '/Bipartite_PhysicalCommunities+FinalLabeling.dlabel.nii']);
  147. VA = ft_read_cifti_mod([Subdir '/fs_LR/fsaverage_LR32k/' Subject '.midthickness_va.32k_fs_LR.dscalar.nii']);
  148. Structures = {'CORTEX_LEFT','CORTEX_RIGHT'}; % in this case, cortex only.
  150. % calculate the size of each functional brain network
  151. NetworkSize = pfm_calculate_network_size(FunctionalNetworks,VA,Structures);
  153. close all; % blank slate
  154. H = figure; % prellocate parent figure
  155. set(H,'position',[1 1 325 400]); hold;
  157. % unique functional networks;
  158. uCi = unique(nonzeros(FunctionalNetworks.data));
  160. % sweep through
  161. % the networks;
  162. for i = 1:length(uCi)
  163. Tmp = nan(1,length(Priors.NetworkLabels));
  164. Tmp(i) = NetworkSize(i);
  165. barh(Tmp,'FaceColor',Priors.NetworkColors(i,:));
  166. text((NetworkSize(i)+0.1),i,[num2str(NetworkSize(i),3) '%']);
  167. end
  169. % make it pretty;
  170. yticklabels(Priors.NetworkLabels);
  171. yticks(1:length(uCi)); ylim([0 21]);
  172. xlim([0 20]); xticks(0:5:20);
  173. set(gca,'fontname','arial','fontsize',10,'TickLength',[0 0],'TickLabelInterpreter','none');
  174. xlabel('% of Cortical Surface');
  175. print(gcf,[PfmDir '/FunctionalNetworkSizes'],'-dpdf');

pfm_tutorial.m at commit 454a28f, no license · at the source

Overview

Authors: Colleen Hughes1, Anne C. Krendl1, Roberto C. French1, Shannon L. Risacher2,3, Yu-Chien Wu3,4,5, Andrew J. Saykin3,6, Richard Betzel7,8
ORCID iDs: Colleen Hughes, Anne C. Krendl, Roberto C. French, Shannon L. Risacher, Yu-Chien Wu, Andrew J. Saykin, Richard Betzel
  1. Psychological and Brain Sciences Department, Indiana University Bloomington, Bloomington, IN, United States
  2. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
  3. Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, United States
  4. Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
  5. Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
  6. Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, United States
  7. Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
  8. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States
Institutions: Indiana University Bloomington (United States); Indiana University (United States); Indiana University School of Medicine; Wake Forest University Health Sciences (United States); Wake Forest University (United States); Indiana University Indianapolis (United States); University of Minnesota (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1285
Dates: received 20 February 2026; accepted 28 May 2026; published online 29 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1285 · PMID 42382506 · PMCID PMC13317017 · OpenAlex W7163317241
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, fMRI & imaging
Keywords: aging, functional magnetic resonance imaging, functional brain networks, resting-state, network segregation, precision functional mapping
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute on Aging (AG070931, AG075044, AG083951, P30 AG072976); Lilly Endowment
Citations: not cited yet (Europe PMC); 97 references in the paper
Research resources: 2011 RRID:SCR_002502, 2019 RRID:SCR_016216

Abstract

The functional network architecture of the aging brain undergoes significant systematic and idiosyncratic changes. Emergent individualized network mapping approaches may yield better or more sensitive explanatory insight about age-related neural and behavioral variability, although most applications have focused on young adults. In the current study, we tested the validity and impact of mapping individual-specific topography in two fMRI datasets comprising 112 young (18–35 years) and 176 older adults (60–92 years). Older adults had more idiosyncratic network topography than young adults. Individualized maps from resting-state fMRI improved network homogeneity and fidelity to social cognitive task fMRI activations and exhibited intra-individual stability and inter-individual discriminability over a 2-year interval. Last, traditional group-averaged (vs. individualized) network mapping had a moderate-to-large impact on individual-level estimates of network segregation, a widely-studied measure of functional brain aging. Therefore, individualized network mapping captures important heterogeneity in older adulthood and may yield more precise characterization of neurocognitive aging.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

DCAN-Labs/compare_matrices_to_assign_networks

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4e32763d80a14697e9dab92b90287632ef655a84, 6 March 2026
Languages: MATLAB (57), Shell (3)
Size: 89 files, 60 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cifti-matlab (35 files), Statistics and Machine Learning Toolbox (11 files), FieldTrip (9 files), Parallel Computing Toolbox (2 files), Signal Processing Toolbox (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
62 files
At the source: github.com/DCAN-Labs/compare_matrices_to_assign_networks

cjl2007/PFM-Depression

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 454a28ffef3b02d576e122d7e81ddc2ce98a4653, 18 May 2026
Languages: MATLAB (12)
Size: 15 files, 12 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files), Statistics and Machine Learning Toolbox (3 files), Parallel Computing Toolbox (2 files), GIfTI library for MATLAB (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/cjl2007/PFM-Depression

The paper's code and data availability statement is in the Data section.

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;
  • 72 scripts, each with its path and the digest of its content;
  • 5 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

Datasets cited

Data and Code Availability

The IU young and older cohort data supporting the conclusions of the current work are available upon request to the first author. The IADRC cohort data supporting the conclusions of the current work could be requested via https://medicine.iu.edu/research-centers/alzheimers. The Dworetsky HCP probabilistic network templates are publicly available at www.midbatlas.io. Data files in CIFTI format supporting the visualizations of some key results are available at https://osf.io/ys28f/. The current report used the following public code repositories to generate and analyze features of individualized network maps: https://github.com/DCAN-Labs/compare_matrices_to_assign_networks (Hermosillo et al., 2024 and https://github.com/cjl2007/PFM-Depression (Lynch et al., 2024). Effect sizes and their bootstrapped 95% confidence intervals were calculated using the MATLAB Measures of Effect Size Toolbox (https://github.com/hhentschke/measures-of-effect-size-toolbox).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 6 keywords, 2 funders, 95 references, 2 RRIDs.

Cite

This paper

Hughes, C., Krendl, A. C., French, R. C., Risacher, S. L., Wu, Y.-C., Saykin, A. J., & Betzel, R. (2026). Individualized mapping of functional brain networks in older adulthood. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1285. https://doi.org/10.1162/imag.a.1285

BibTeX

@article{hughes2026individualized,
author = {Hughes, Colleen and Krendl, Anne C. and French, Roberto C. and Risacher, Shannon L. and Wu, Yu-Chien and Saykin, Andrew J. and Betzel, Richard},
title = {{Individualized mapping of functional brain networks in older adulthood}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1285},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1285},
url = {https://doi.org/10.1162/imag.a.1285},
pmid = {42382506},
pmcid = {PMC13317017}
}

RIS

TY - JOUR
AU - Hughes, Colleen
AU - Krendl, Anne C.
AU - French, Roberto C.
AU - Risacher, Shannon L.
AU - Wu, Yu-Chien
AU - Saykin, Andrew J.
AU - Betzel, Richard
TI - Individualized mapping of functional brain networks in older adulthood
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/29
VL - 4
SP - IMAG.a.1285
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1285
UR - https://doi.org/10.1162/imag.a.1285
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1285",
"type": "article-journal",
"title": "Individualized mapping of functional brain networks in older adulthood",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
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"family": "Hughes",
"given": "Colleen"
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{
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"given": "Richard"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1285",
"DOI": "10.1162/imag.a.1285",
"PMID": "42382506",
"PMCID": "PMC13317017",
"ISSN": "2837-6056",
"publisher": "MIT Press",
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"language": "en",
"issued": {
"date-parts": [
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]
}
}

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