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Spatial Collinearity Constrains Multivariate Molecular-Enriched Network Estimation.

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The 3 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › HCP Test–Retest Dataset ↔ functions/preprocess.m, the whole file · a weak match · score 0.81 · CIFTI grayordinates, phase encoding, hp2000 clean, preprocessing, timeseries, voxels · Report a mismatch
  2. [2] § Methods › HCP Test–Retest Dataset ↔ external_packages/matlab/non_default_packages/palm/palm-alpha109/fileio/@nifti/private/nifti1.h, lines 1070 to 1158 · score 0.60 · phase encoding direction, EPI, slices, space · Report a mismatch
  3. [3] § Methods › HCP Test–Retest Dataset ↔ functions/preprocess.m, the whole file · a weak match · score 0.51 · Human Connectome Project, fMRI, HCP · Report a mismatch

Paper

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The authors' code

MATLAB · 124 lines · 3.6 KB · MIT · 2 matches

  1. % This script dose a few additional preprocessing steps on top of HCP
  2. % minimal preprocessing pipeline (Glasser et al 2013)
  3. % 1. Spatial smoothing
  4. % 2. Wishart filtering
  5. % And concatenate two runs (L-R and R-L phase encoding)
  6. clear
  7. close all
  8. addpath ../masks
  9. addpath ../functions/wishart
  10. addpath ../functions
  12. % Subcortex mask
  13. insFile='subcortex_mask.nii';
  15. % Gray matter mask
  16. gmFile='GMmask.nii';
  18. % Subject ID
  19. SubID='100206';
  21. % Gaussian smoothness in mm
  22. FWHM=6;
  24. % Voxel size in mm
  25. voxelsize=2;
  27. % minimally preprocessed fMRI data
  28. % Data Source: Human Connectome Project: https://www.humanconnectome.org/
  29. dataFile1='rfMRI_REST1_LR_hp2000_clean.nii.gz';% L-R phase encoding
  30. dataFile2='rfMRI_REST1_RL_hp2000_clean.nii.gz';% R-L phase encoding
  32. %subcortex mask
  33. [~,ins_msk]=read(insFile); ind_ins=find(ins_msk);
  35. %Gray matter mask
  36. [~,gm_msk]=read(gmFile); ind_msk=find(gm_msk);
  38. % index of subcortex into gray matter
  39. ind_ind_ins=zeros(1,length(ind_ins));
  40. for i=1:length(ind_ins)
  41. ind_ind_ins(i)=find(ind_ins(i)==ind_msk);
  42. end
  44. % Process images
  45. % L-R phase encoding
  46. system(['gunzip ',dataFile1]);
  47. dataFile1=dataFile1(1:end-3);
  49. fprintf('Read and smooth data with FWHM=%dmm\n',FWHM);
  50. [~,data]=read(dataFile1);
  51. T=size(data,4); % Number of time points
  52. xLR=zeros(T,length(ind_msk));
  53. frst=0;
  54. for i=1:T
  55. data(:,:,:,i)=imgaussfilt3(data(:,:,:,i),FWHM/voxelsize/2.355);
  56. tmp=data(:,:,:,i);
  57. xLR(i,:)=tmp(ind_msk);
  58. show_progress(i,T,frst);frst=1;
  59. end
  61. clear data
  63. % Perform Wishart filter. Glasser et al. 2016
  64. fprintf('Wishart filtering\n')
  65. DEMDT=1; %Use 1 if demeaning and detrending (e.g. a timeseries) or -1 if not doing this (e.g. a PCA series)
  66. VN=1; %Initial variance normalization dimensionality
  67. Iterate=2; %Iterate to convergence of dim estimate and variance normalization
  68. NDist=2; %Number of Wishart Filters to apply (for most single subject CIFTI grayordinates data 2 works well)
  70. Out=icaDim(xLR',DEMDT,VN,Iterate,NDist);
  71. xLR=Out.data';
  73. x_insLR=xLR(:,ind_ind_ins);
  75. % Demean and std
  76. xLR=detrend(xLR,'constant'); xLR=xLR./repmat(std(xLR),T,1); %remove mean and make std=1
  77. x_insLR=detrend(x_insLR,'constant'); x_insLR=x_insLR./repmat(std(x_insLR),T,1);
  79. clear i j Out
  81. % R-L phase encoding
  82. system(['gunzip ',dataFile2]);
  83. dataFile2=dataFile2(1:end-3);
  85. fprintf('Read and smooth Data with FWHM=%dmm\n',FWHM)
  86. [~,data]=read(dataFile2);
  87. T=size(data,4); %number of time points
  88. xRL=zeros(T,length(ind_msk));
  89. frst=0;
  90. for i=1:T
  91. data(:,:,:,i)=imgaussfilt3(data(:,:,:,i),FWHM/voxelsize/2.355);
  92. tmp=data(:,:,:,i);
  93. xRL(i,:)=tmp(ind_msk);
  94. show_progress(i,T,frst);frst=1;
  95. end
  97. clear data
  99. % Perform Wishart filter
  100. fprintf('Wishart filtering\n')
  101. DEMDT=1; %Use 1 if demeaning and detrending (e.g. a timeseries) or -1 if not doing this (e.g. a PCA series)
  102. VN=1; %Initial variance normalization dimensionality
  103. Iterate=2; %Iterate to convergence of dim estimate and variance normalization
  104. NDist=2; %Number of Wishart Filters to apply (for most single subject CIFTI grayordinates data 2 works well)
  106. Out=icaDim(xRL',DEMDT,VN,Iterate,NDist);
  107. xRL=Out.data';
  109. x_insRL=xRL(:,ind_ind_ins);
  111. % Demean and std
  112. xRL=detrend(xRL,'constant'); xRL=xRL./repmat(std(xRL),T,1); %remove mean and make std=1
  113. x_insRL=detrend(x_insRL,'constant'); x_insRL=x_insRL./repmat(std(x_insRL),T,1);
  115. clear i j Out
  117. % Concatenate the two runs
  118. x=[xLR;xRL]; % Time series of all gray matter voxels
  119. x_ins=[x_insLR;x_insRL]; % Time series of subcortical voxels
  120. T=size(x_insLR,1) + size(x_insRL,1);
  122. % Demean
  123. x=detrend(x,'constant'); x=x./repmat(std(x),T,1); %remove mean and make std=1
  124. x_ins=detrend(x_ins,'constant'); x_ins=x_ins./repmat(std(x_ins),T,1); %remove mean and make std=1

preprocess.m at commit dcad934, under MIT · at the source

Overview

Authors: Timothy Lawn1,2,3, Johan Nakuci4, Steve C R Williams2, Federico Turkheimer2, Mitul A Mehta2
ORCID iDs: Timothy Lawn, Johan Nakuci, Steve C R Williams, Mitul A Mehta
  1. Oxford Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
  2. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
  3. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
  4. U.S. Army DEVCOM Army Research Laboratory, Adelphi, Maryland, USA
Institutions: Harvard University (United States); King's College London (United Kingdom); University of Oxford (United Kingdom); Massachusetts General Hospital (United States); Wellcome Centre for Integrative Neuroimaging (United Kingdom); Athinoula A. Martinos Center for Biomedical Imaging (United States); DEVCOM Army Research Laboratory (United States); United States Army Combat Capabilities Development Command (United States)
Journal: Human brain mapping, volume 47, issue 14, article e70645
Dates: received 10 June 2026; accepted 10 September 2026; published online 20 September 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70645 · PMID 42764460 · PMCID PMC13590927 · OpenAlex W7164544987
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.06.10.731385
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), PET / SPECT (modality), human (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: collinearity, connectivity, fMRI, networks, PET, pharmacoimaging, REACT
MeSH: Brain*, Connectome*, Magnetic Resonance Imaging*, Nerve Net*, Positron-Emission Tomography*, Receptors, Neurotransmitter*, Humans (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (343923/Z/26/Z); NIMH NIH HHS (U54 MH091657); National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Analyses of neuroimaging data increasingly leverage the distribution of neurotransmitter receptors derived from positron emission tomography (PET) to bridge the gap between micro‐ and macro‐scale brain function. However, these receptor maps are highly spatially overlapping, which can give rise to interpretive and analytical challenges. Here, we systematically investigate the impact of spatial collinearity among PET maps in the context of Receptor‐Enriched Analysis of functional Connectivity by Targets (REACT), a method that uses receptor maps as spatial regressors to derive subject‐level molecular‐enriched functional connectivity networks. Exhaustive combinatorial analysis across 19 receptor and transporter maps showed that collinearity scales rapidly with the number of receptors modelled simultaneously, and that the underlying correlation structure was relatively stable across parcellation scales, reflecting the intrinsic organisation of neurotransmitter systems. Using test–retest fMRI data from the Human Connectome Project, we demonstrate that modelling greater numbers of receptors degrades the reliability of molecular‐enriched networks derived from conventional multivariate REACT models, and that collinearity among receptor maps drives this degradation. An alternative univariate approach, in which each receptor is modelled independently, yielded more reliable networks and, when applied to a within‐subjects study of LSD compared to placebo, better recovered the role of the 5‐HT2A receptor in LSD's neural effects. These findings identify spatial collinearity as a fundamental constraint on multivariate molecular‐enriched network estimation. We therefore recommend univariate models as the default, with multivariate models used only where specifically motivated and reported alongside the univariate result.

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 3 matches between paragraphs and lines of code.

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 October 2026
Evidence: files inventoried
Commit: 634f676630929a71297852d01dd92a287103e861, 1 October 2026
Languages: MATLAB (2110), Shell (652), Python (589), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,316 files, 3,621 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (130 files), PyTorch (122 files), FreeSurfer (94 files), SciPy (61 files), FieldTrip (60 files), Statistics and Machine Learning Toolbox (50 files), FSL (44 files), SPM (40 files), GIfTI library for MATLAB (10 files), Image Processing Toolbox (8 files), Connectome Workbench (8 files), scikit-learn (5 files), AFNI (2 files), Matplotlib (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file)
Availability: 2 checks, the latest on 27 October 2026: the link answers
  • 27 October 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal

yetianmed/subcortex

License: MIT
State: the link answers, verified on 11 November 2026
Evidence: files inventoried
Commit: dcad93421ea8021d6c5738df0a915a2223cd82aa, 5 July 2025
Languages: MATLAB (44)
Size: 317 files, 44 scripts
Software Heritage: archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (7 files), Statistics and Machine Learning Toolbox (4 files), boundedline (1 file), Signal Processing Toolbox (1 file), SPM (1 file)
Availability: 2 checks, the latest on 11 November 2026: the link answers
  • 11 November 2026: the link answers
  • 1 October 2026: the link answers
46 files
At the source: github.com/yetianmed/subcortex

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

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Data

Datasets cited

Data Availability Statement

This work made use of publicly available datasets. The PET map data (https://github.com/netneurolab/hansen_receptors), Human Connectome Project Young Adult (HCP‐YA) test–retest data (https://balsa.wustl.edu/project?project=HCP_Retest) and LSD pharmacoimaging data (https://openneuro.org/datasets/ds003059/versions/1.0.0) are all freely available for download. The Schaefer cortical atlas (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal) and Tian subcortical atlas (https://github.com/yetianmed/subcortex) are also freely available.

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

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Version 1, 1 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 7 MeSH terms, 3 funders, 52 references.

Cite

This paper

Lawn, T., Nakuci, J., Williams, S. C. R., Turkheimer, F., & Mehta, M. A. (2026). Spatial Collinearity Constrains Multivariate Molecular-Enriched Network Estimation. Human brain mapping, 47(14), e70645. https://doi.org/10.1002/hbm.70645

BibTeX

@article{lawn2026spatial,
author = {Lawn, Timothy and Nakuci, Johan and Williams, Steve C R and Turkheimer, Federico and Mehta, Mitul A},
title = {{Spatial Collinearity Constrains Multivariate Molecular-Enriched Network Estimation}},
journal = {Human brain mapping},
year = {2026},
month = oct,
volume = {47},
number = {14},
pages = {e70645},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70645},
url = {https://doi.org/10.1002/hbm.70645},
pmid = {42764460},
pmcid = {PMC13590927}
}

RIS

TY - JOUR
AU - Lawn, Timothy
AU - Nakuci, Johan
AU - Williams, Steve C R
AU - Turkheimer, Federico
AU - Mehta, Mitul A
TI - Spatial Collinearity Constrains Multivariate Molecular-Enriched Network Estimation
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/10/01
VL - 47
IS - 14
SP - e70645
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70645
UR - https://doi.org/10.1002/hbm.70645
LA - en
ER -

CSL-JSON

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