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Brain network analysis in Alzheimer's disease and mild cognitive impairment using high-density diffuse optical tomography.

Code ↔ Paper

13 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 13 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Functional connectivity › Local dynamics ↔ matlab/FindHighNodes.m, the whole file · a weak match · score 0.81 · hub occurrence, core hubs, occurring hubs, high clustering, eigenvector centrality, properties
  2. [2] § Materials and Methods › Graph theory analysis ↔ matlab/Calc3NodeMotif.m, the whole file · a weak match · score 0.74 · node motifs, open triad, chain, clique, star, square
  3. [3] § Materials and Methods › Graph theory analysis ↔ matlab/CalcPartCoeff.m, the whole file · a weak match · score 0.68 · dorsal attention, Participation coefficients, somatomotor, temporoparietal, partitioning, modules
  4. [4] § Materials and Methods › Graph theory analysis ↔ matlab/CalcGraphProperties.m, lines 138–187 · score 0.66 · Brain Connectivity Toolbox, Eigenvector centrality, weights, efficiency, strength, metrics
  5. [5] § Results › Functional connectivity › Global dynamics ↔ src/hddot/graphTheory/gt03_plotting.py, lines 12–89 · score 0.61 · global efficiency, degree density, eigenvector centrality, graph theory, strength, HC
  6. [6] § Materials and Methods › Graph theory analysis ↔ src/hddot/graphTheory/gt09_motif.py, lines 37–111 · score 0.61 · open triad, chain, clique, star, motifs, square
  7. [7] § Results › Functional connectivity › Global dynamics ↔ src/hddot/graphTheory/gt11_correlations.py, lines 23–106 · score 0.60 · graph theory metrics, global efficiency, degree density, strength, HC, clustering
  8. [8] § Materials and Methods › Head modelling and registration ↔ mp2rage_run_remove_background.m, lines 56–118 · score 0.59 · bias field correcting, SPM, masks, positions
  9. [9] § Materials and Methods › Graph theory analysis ↔ matlab/Calc3NodeMotif.m, the whole file · a weak match · score 0.56 · adjacency matrix, undirected, quantified, Edge, thresholded, graph
  10. [10] § Appendix › Details of MRI acquisition ↔ mp2rage_run_remove_background.m, lines 56–118 · score 0.55 · bias field corrected, SPM, noised
  11. [11] § Materials and Methods › Graph theory analysis ↔ matlab/CalcModularity.m, the whole file · a weak match · score 0.55 · Louvain algorithm, Modularity, partitioning, coefficient, connectivity, Graph
  12. [12] § Materials and Methods › Statistical analysis ↔ src/hddot/graphTheory/gt11_correlations.py, lines 23–106 · score 0.55 · graph theory metrics, Spearman, Pearson, MMSE, FDR, Correlations
  13. [13] § Results › Functional connectivity › Local dynamics ↔ matlab/CalcPartCoeff.m, the whole file · a weak match · score 0.55 · dorsal attention, control network, salience, clustering, connectivity

Paper

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

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The authors' code

MATLAB · 83 lines · 2.9 KB · no license · 2 matches

  1. function motif_counts = Calc3NodeMotif(w_mtx)
  2. %A 3-node motif analysis involves identifying and quantifying all possible
  3. % triadic patterns (motifs) in a network. In an undirected network, there
  4. % are 2 unique 3-node motifs: triangles (fully connected 3-node subgraphs),
  5. % and open triads (2 nodes connected with a shared neighbor).
  6. %
  7. % Triangle motifs often indicate strong interconnectivity (e.g., cliques or
  8. % densely connected groups). Open triads reflect less cohesive structures.
  9. %
  10. % The adjacency matrix must be undirected and have no subgraphs (NaNs).
  13. % Initialize total motif counts
  14. norm_triangle_count = NaN(size(w_mtx,3),1);
  15. norm_open_triad_count = NaN(size(w_mtx,3),1);
  17. counts4nodes = table();
  19. for subj = 1:size(w_mtx, 3)
  21. A = w_mtx(:,:,subj);
  23. % Preprocessing similarity matrix
  24. A(isnan(A)) = 0; % replace NaNs with 0
  25. A_binary = A > 0; % binarize the matrix (threshold > 0)
  26. A_binary = max(A_binary, A_binary'); % ensure symmetry (if undirected)
  28. % Remove isolated nodes
  29. degrees = sum(A_binary, 2); % Degree of each node
  30. non_isolated = degrees > 0; % Logical index for non-isolated nodes
  31. A_filtered = A_binary(non_isolated, non_isolated);
  33. %A_filtered = A_binary;
  34. n = size(A_filtered, 1); % Number of nodes in the filtered matrix
  36. % total_triads = n * (n-1) * (n-2) / 6; % Total number of 3-node subgraphs
  37. % edge_density = sum(A_filtered(:)) / (n * (n-1)); % Density of the network
  38. % expected_triangles = edge_density^3 * total_triads; % Approximation for triangles
  41. % Initialize motif counts for subj
  42. triangle_count = 0;
  43. open_triad_count = 0;
  45. % Enumerate all possible triads
  46. for i = 1:n-2
  47. for j = i+1:n-1
  48. for k = j+1:n
  49. % Subgraph of 3 nodes
  50. subgraph = A_filtered([i j k], [i j k]);
  52. % Count edges in the subgraph
  53. edges = sum(subgraph(:)) / 2; % Divide by 2 for undirected graphs
  55. % Classify the subgraph
  56. if edges == 3
  57. triangle_count = triangle_count + 1;
  58. elseif edges == 2
  59. open_triad_count = open_triad_count + 1;
  60. end
  61. end
  62. end
  63. end
  65. % Normalization by Total Motif Count: divide the count of each motif type
  66. % by the total number of 3-node subgraphs in the network to get a
  67. % proportion.
  68. % The total number of 3-node subgraphs is given the Newton binomial:
  69. % (n choose 3) = n * (n-1) * (n-2) / 6
  70. counts.triangle(subj,1) = triangle_count/nchoosek(n, 3);
  71. counts.open_triad(subj,1) = open_triad_count/nchoosek(n, 3);
  73. motif_4nodes = count_4node_motifs(A_filtered);
  74. counts4nodes{end+1, :} = motif_4nodes{:,:} ./ nchoosek(n, 4);
  75. end
  77. counts4nodes.Properties.VariableNames = {'chain', ' star', ' triangle_extra', 'square', 'clique'};
  79. motif_counts = struct2table(counts);
  81. motif_counts = horzcat(motif_counts, counts4nodes);
  83. return

Calc3NodeMotif.m at commit 2205cb0, no license · at the source

Overview

Authors: Emilia Butters1,2, Liam Collins-Jones1,3, Rickson C Mesquita4, Deepshikha Acharya1, Elizabeth McKiernan2, Axel AS Laurell2, Audrey Low2,5, Sruthi Srinivasan1, John T O’Brien2, Li Su2,6, Gemma Bale1,7
ORCID iDs: Emilia Butters
  1. Department of Engineering, University of Cambridge, Cambridge, United Kingdom
  2. Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
  3. Milner Therapeutics Institute, University of Cambridge, Cambridge, United Kingdom
  4. School of Computer Science, University of Birmingham, Birmingham, United Kingdom
  5. Department of Radiology, Mayo Clinic, Rochester, MN, United States
  6. Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, United Kingdom
  7. Department of Physics, University of Cambridge, Cambridge, United Kingdom
Institutions: University of Cambridge (United Kingdom); University of Birmingham (United Kingdom); Mayo Clinic (United States); University of Sheffield (United Kingdom)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1208
Dates: received 28 April 2025; accepted 18 March 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1208 · PMID 42052506 · PMCID PMC13112212 · OpenAlex W7140526370
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism), Alzheimer's / dementia (population), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Connectivity, Graphs, Machine learning, fMRI & imaging
Keywords: dementia, Alzheimer’s disease, mild cognitive impairment, near-infrared spectroscopy, optical imaging
Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR203312)
Citations: not cited yet (Europe PMC); 127 references in the paper

Abstract

Dementia is associated with altered resting-state connectivity, measures of which could aid in its early detection and monitoring. High-density diffuse optical tomography (HD-DOT) is well suited to detect these alterations at scale due to its numerous practical advantages, but it has not yet been applied to dementia. In this study, we investigated resting-state functional connectivity across the prefrontal cortex in individuals with mild cognitive impairment (MCI, n = 22), Alzheimer’s disease (AD, n = 21), and in healthy controls (n = 22). A graph theoretical approach was taken to characterise both global and local patterns of prefrontal connectivity over a 5-minute resting period. We found that individuals with MCI exhibited denser and stronger networks with shorter path lengths, which normalised in AD, accompanied by a redistribution of network hubs that were less stable. These results perhaps reflect the recruitment of additional connections in the early stages of pathology to maintain short-term network stability, which is ultimately associated with less efficient and more fragmented network organisation in later stages. Following the demonstration of HD-DOT’s capacity to detect differences between healthy ageing and AD-type cognitive impairment, this work opens up new possibilities for the use of optical imaging in the study of this clinical population and HD-DOT’s potential for scalable clinical use.

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 13 matches between paragraphs and lines of code.

emiliavioletb/ImagingNeuroscience

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2205cb0f59170078fc926d37d45a6c6a4065203c, 25 April 2025
Languages: Python (23), MATLAB (9)
Size: 42 files, 32 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: Matplotlib (7 files), pandas (4 files), Brain Connectivity Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files), statsmodels (2 files), NetworkX (1 file), NumPy (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
33 files
At the source: github.com/emiliavioletb/ImagingNeuroscience

benoitberanger/mp2rage

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 73e481a3c51001295f05c4d413f312ecc5dc733d, 18 February 2026
Languages: MATLAB (14)
Size: 22 files, 14 scripts
Software Heritage: archived
Found in: the text, “Details of MRI acquisition”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (7 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
16 files
At the source: github.com/benoitberanger/mp2rage

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;
  • 46 scripts, each with its path and the digest of its content;
  • 13 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 and Code Availability

The code is available at www.github.com/emiliavioletb/ImagingNeuroscience. Data are available upon reasonable request from accredited researchers, in accordance with our ethical approval.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 11 authors, 5 keywords, 1 funder, 126 references.

Cite

This paper

Butters, E., Collins-Jones, L., Mesquita, R. C., Acharya, D., McKiernan, E., Laurell, A. A., Low, A., Srinivasan, S., O’Brien, J. T., Su, L., & Bale, G. (2026). Brain network analysis in Alzheimer's disease and mild cognitive impairment using high-density diffuse optical tomography. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1208. https://doi.org/10.1162/imag.a.1208

BibTeX

@article{butters2026brain,
author = {Butters, Emilia and Collins-Jones, Liam and Mesquita, Rickson C and Acharya, Deepshikha and McKiernan, Elizabeth and Laurell, Axel AS and Low, Audrey and Srinivasan, Sruthi and O’Brien, John T and Su, Li and Bale, Gemma},
title = {{Brain network analysis in Alzheimer's disease and mild cognitive impairment using high-density diffuse optical tomography}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1208},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1208},
url = {https://doi.org/10.1162/imag.a.1208},
pmid = {42052506},
pmcid = {PMC13112212}
}

RIS

TY - JOUR
AU - Butters, Emilia
AU - Collins-Jones, Liam
AU - Mesquita, Rickson C
AU - Acharya, Deepshikha
AU - McKiernan, Elizabeth
AU - Laurell, Axel AS
AU - Low, Audrey
AU - Srinivasan, Sruthi
AU - O’Brien, John T
AU - Su, Li
AU - Bale, Gemma
TI - Brain network analysis in Alzheimer's disease and mild cognitive impairment using high-density diffuse optical tomography
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/24
VL - 4
SP - IMAG.a.1208
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1208
UR - https://doi.org/10.1162/imag.a.1208
LA - en
ER -

CSL-JSON

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