OSCR

Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis.

Code ↔ Paper

6 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 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Exploratory receptor binding atlases and MMN source-reconstructed signal analysis ↔ eeg-pet_extracting.m, the whole file · a weak match · score 0.89 · NIfTI, receptor density, MMN maps, PET map, alignment, imcalc
  2. [2] § Methods › Statistical analysis › Linear regressions and LMM for exploratory receptor binding atlases and MMN source-reconstructed signal analysis ↔ eeg-pet_extracting.m, the whole file · a weak match · score 0.69 · NIfTI, receptor density, variable, regional, atlas, MMN
  3. [3] § Methods › Statistical analysis › Leave one out multiple regression models by hallucination subtype ↔ loocvd-regress_example.r, lines 1–45 · score 0.63 · cross validation, complex VH, regression, predicting, severity, connection
  4. [4] § Methods › Statistical analysis › Second-level analysis with PEB ↔ pdp_peb_recursive_final_Bmatexample.m, lines 127–139 · score 0.56 · spm_dcm_peb, winning model, bmr
  5. [5] § Results › vMMN differences correlate with decreased top-down connectivity in PD-VH ↔ pdp_peb_recursive_final_Bmatexample.m, lines 1–43 · score 0.51 · parametrical empirical, Recursive PEB, connected
  6. [6] § Methods › Statistical analysis › Second-level analysis with PEB ↔ pdp_peb_recursive_final_Bmatexample.m, lines 100–124 · score 0.51 · free energy, recursive PEB, model

Paper

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

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The authors' code

MATLAB · 143 lines · 4.7 KB · no license · 3 matches

  1. %--------------------------------------------------------------------------
  2. % SECOND-LEVEL ANALYSIS: PARAMETRICAL EMPIRICAL BAYES PEB
  3. %--------------------------------------------------------------------------
  5. clear all;
  6. %--------------------------------------------------------------------------
  7. % Setup a group DCM
  8. %-------------------------------------------------------------------------
  10. % Select all DCM files
  11. dcms = spm_select ();
  13. %if cannot find headmodel give path:
  14. %headmodel = ft_read_headmodel ('/PATH/single_subj_T1_EEG_BEM.mat');
  16. % turn character array in cell array
  17. GCM = cellstr ( dcms );
  18. % DCM filenames -> DCM structures
  19. GCM = spm_dcm_load (GCM);
  21. %--------------------------------------------------------------------------
  22. %if needed load GCM and dm
  23. load('design_matrix3.mat');
  24. load('GCM_full3.mat');
  26. %--------------------------------------------------------------------------
  27. % Setup of the design matrix
  28. %--------------------------------------------------------------------------
  30. %M.Q - choice of precision components: all', the between-subject
  31. %variability for each DCM connection will be individually estimated.
  32. M = struct ();
  33. M.Q = 'all ';
  34. M.X = X;
  35. M. Xnames = X_labels;
  38. %--------------------------------------------------------------------------
  39. %________________________________ RECURSIVE PEB __________________________%
  40. %________________________________ MATRIX B _______________________________%
  41. %--------------------------------------------------------------------------
  43. clear C PEB F Ep labels fam_idx families
  45. %% basics for recursive peb
  46. C_fwd = {'B{1}(3,1)','B{1}(4,2)','B{1}(5,3)','B{1}(6,4)','B{1}(5,1)','B{1}(6,2)'}; % forward conns
  47. C_bwd = {'B{1}(1,3)','B{1}(2,4)','B{1}(3,5)','B{1}(4,6)','B{1}(1,5)','B{1}(2,6)'}; % backward conns
  48. C_self = {'B{1}(1,1)','B{1}(2,2)','B{1}(3,3)','B{1}(4,4)','B{1}(5,5)','B{1}(6,6)'}; % self conns (diagonal elements)
  49. C_lateral = {'B{1}(1,2)','B{1}(2,1)','B{1}(3,4)','B{1}(4,3)','B{1}(5,6)','B{1}(6,5)'}; %lateral/crosshemi
  51. %% --- Parameter groups definitions (names + sets; no lateral) ---
  52. %% comment in and out whatever you want, this system does not have issues
  53. % with N of models used and labels like the previous version
  55. families = {
  56. 'B(full extrinsic+self)', [C_fwd, C_bwd, C_self]
  57. 'B(full)', [C_fwd, C_bwd]
  58. 'B(forward)', C_fwd
  59. 'B(backward)', C_bwd
  60. 'B(forward+self)', [C_fwd, C_self]
  61. 'B(backward+self)', [C_bwd, C_self]
  62. 'B(self)', C_self
  63. 'B(forward+s)', [C_fwd, {'S'}]
  64. 'B(back+s)', [C_bwd, {'S'}]
  65. % 'B (cross-hemi)', C_lateral
  66. 'B(bwd)+G', [[C_bwd], {'S'}]
  67. 'B(fwd)+G', [[C_fwd], {'S'}]
  68. 'B(full)+G', [[C_fwd, C_bwd], {'S'}]
  69. % 'S', {'S'}
  70. 'G', {'G'}
  71. 'T', {'T'}
  72. % 'G+S', {'G','S'}
  73. };
  75. K = size(families,1);
  77. % Normalise labels to a 1xK cell array of char
  78. raw_labels = families(:,1); % Kx1 (cell or string)
  79. if isstring(raw_labels) % string array -> cellstr
  80. labels = cellstr(raw_labels);
  81. elseif iscell(raw_labels)
  82. labels = raw_labels;
  83. else % char array fallback
  84. labels = cellstr(raw_labels);
  85. end
  86. labels = labels(:)';
  90. %% --- Run PEB for each family ---
  91. F = nan(K,1);
  92. PEB = cell(K,1);
  94. for k = 1:K
  95. Cset = families{k,2};
  96. PEB{k} = spm_dcm_peb(GCM, M, Cset);
  97. F(k) = PEB{k}.F;
  98. end
  100. %% --- Winner and plots (Free Energy: bigger is better) ---
  101. [~, order] = sort(F,'descend');
  102. best_idx = order(1); second_idx = order(2);
  103. dF = F(best_idx) - F(second_idx);
  105. save ('/..../recursive_PEB.mat','PEB');
  107. figure;
  109. % Panel 1: ΔF (winner at 0)
  110. subplot(2,1,1);
  111. bar(F - max(F));
  112. ylabel('\DeltaF (model - best)');
  113. title(sprintf('Family BMC: winner = %s, \\DeltaF_{best-second}=%.2f', ...
  114. labels{best_idx}, F(best_idx)-F(order(2)))); % <-- single { }
  115. set(gca,'XTick',1:K,'XTickLabel',labels); xtickangle(45);
  117. % Panel 2: softmax posteriors
  118. model_post = spm_softmax(F - max(F));
  119. subplot(2,1,2);
  120. bar(model_post);
  121. ylabel('Model posterior (approx.)');
  122. title('Family posteriors from Free Energy');
  123. set(gca,'XTick',1:K,'XTickLabel',labels); xtickangle(45);
  124. set(gcf,'color','w');
  127. %% Repeat PEB in the winning model and run BMR
  128. %--------------------------------------------------------------------------
  132. [REB, RCM] = spm_dcm_peb(GCM,M, MODEL);
  133. RMA = spm_dcm_peb_bmc(REB);
  134. spm_dcm_peb_review (RMA ,GCM);
  136. % example for other type of model, like second best
  137. [REB, RCM] = spm_dcm_peb(GCM,M, {'B(full)'});
  138. RMA = spm_dcm_peb_bmc(REB);
  139. spm_dcm_peb_review (RMA ,GCM);

pdp_peb_recursive_final_Bmatexample.m at commit e66cacd, no license · at the source

Overview

Authors: Miriam Vignando1, Dominic Ffytche2, Ndabezinhle Mazibuko1, Giulio Palma3, Anjali Bhat4, Marcella Montagnese5, Sonali Dave6, Yen F Tai7, Lucia Batzu8, Valentina Leta8,9, K Ray Chaudhuri8, Caroline H Williams Gray10, Mitul A Mehta1
ORCID iDs: Miriam Vignando, Dominic Ffytche, Ndabezinhle Mazibuko, Marcella Montagnese, Yen F Tai, Mitul A Mehta
  1. Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
  2. Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
  3. Department of Psychology, University of Southampton, Southampton, UK
  4. Department of Psychology, University of Exeter, Exeter, UK
  5. Department of Clinical Neurosciences, Herchel Smith Building, University of Cambridge, Cambridge, UK
  6. University of London, London, UK
  7. Imperial College London, Faculty of Medicine, Department of Brain Sciences, The Hammersmith Hospital, London, UK
  8. Parkinson Foundation Centre of Excellence, King’s College Hospital NHS Foundation Trust, London, UK
  9. Fondazione IRCCS Istituto Neurologico Carlo Besta, Department of Clinical Neurosciences, Parkinson and Movement Disorders Unit, Milan, Italy
  10. John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge/Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
Institutions: King's College London (United Kingdom); University of Southampton (United Kingdom); University of Exeter (United Kingdom); University of Cambridge (United Kingdom); University of London (United Kingdom); Hammersmith Hospital (United Kingdom); Imperial College London (United Kingdom); King's College Hospital NHS Foundation Trust (United Kingdom); Fondazione IRCCS Istituto Neurologico Carlo Besta (Italy); Cambridge University Hospitals NHS Foundation Trust (United Kingdom)
Journal: Nature. Mental health, volume 4, issue 6, pages 994-1009
Dates: received 9 May 2025; accepted 11 May 2026; published online 10 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44220-026-00669-7 · PMID 42291779 · PMCID PMC13259922 · OpenAlex W7164158954
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), Parkinson's (population), schizophrenia / psychosis (population), computational (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, Source localization, fMRI & imaging
Keywords: Parkinson's disease, Computational neuroscience, Cognitive ageing
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute for Health and Care Research ((NIHR203312); Department of Health and Social Care; King's College London; University College London; Alzheimer’s Research UK (ARUK-RF2022B-002); NIHR Maudsley Biomedical Research Centre; Medical Research Council (MR/R005931/1, NIHR203312, MR/W029235/1); NIHR Cambridge Biomedical Research Centre (NIHR 203312)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Psychosis and visual hallucinations (VH) in Parkinson’s disease (PD) substantially impact patient outcomes, yet the underlying neural mechanisms remain unclear, limiting effective treatments. Here we used dynamic causal modeling to leverage the fast temporal dynamics captured with electroencephalography data during a visual mismatch negativity task in people with PD with (N = 20) and without (N = 18) VH to examine effective connectivity. We found reduced top-down and enhanced bottom-up connectivity in ventral visual and prefrontal regions during task performance in PD-VH, suggesting deficits in sensory prediction updating and an overreliance on visual input. Connectivity patterns differed with hallucination complexity, with complex VH being associated with altered top-down and bottom-up right-hemisphere connectivity, and multimodal hallucinations to more widespread bilateral disruption. Increased task activity, as computed with source reconstruction, correlated positively with normative serotonergic 5-HT2A receptor distribution. These findings highlight specific neural targets for early therapeutic interventions, supporting a transdiagnostic computational architecture of hallucinations.

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 6 matches between paragraphs and lines of code.

VMiri/visual-MMN-DCM-

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e66cacd71aaa880e0cd8ba74b8926ca369522c40, 3 June 2026
Languages: MATLAB (2), Python (1), R (1)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (2 files), BrainSMASH (1 file), caret (1 file), ggplot2 (1 file), NiBabel (1 file), NumPy (1 file), SciPy (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/VMiri/visual-MMN-DCM-

OSF umd89

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (2), R (1), Python (1)
Size: 6 files, 4 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: BrainSMASH (1 file), caret (1 file), ggplot2 (1 file), NiBabel (1 file), NumPy (1 file), SciPy (1 file), SPM (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/umd89

Code availability

All the code used in the study is available via GitHub at https://github.com/VMiri/visual-MMN-DCM- and on the study OSF page (https://osf.io/umd89).

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

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;
  • 7 scripts, each with its path and the digest of its content;
  • 6 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 data supporting the analyses presented in the main text is openly available from the King’s College London research data repository, KORDS, at 10.18742/32205198, including clinical and demographic data and connection strengths for all the connections in the models. Raw EEG files will be shared upon completing a data sharing agreement. All PET maps are available via GitHub at https://github.com/netneurolab/neuromaps and https://github.com/juryxy/JuSpace. To request the raw data, contact the corresponding author.

All the code used in the study is available via GitHub at https://github.com/VMiri/visual-MMN-DCM- and on the study OSF page (https://osf.io/umd89).

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 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media
  • Funding: added National Institute for Health and Care Research: (NIHR203312; Department of Health and Social Care; King's College London; University College London; Alzheimer’s Research UK: ARUK-RF2022B-002; NIHR Maudsley Biomedical Research Centre; Medical Research Council: MR/R005931/1, NIHR203312, MR/W029235/1; NIHR Cambridge Biomedical Research Centre: NIHR 203312

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 3 keywords, 78 references.

Cite

This paper

Vignando, M., Ffytche, D., Mazibuko, N., Palma, G., Bhat, A., Montagnese, M., Dave, S., Tai, Y. F., Batzu, L., Leta, V., Chaudhuri, K. R., Williams Gray, C. H., & Mehta, M. A. (2026). Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis. Nature. Mental health, 4(6), 994-1009. https://doi.org/10.1038/s44220-026-00669-7

BibTeX

@article{vignando2026deviations,
author = {Vignando, Miriam and Ffytche, Dominic and Mazibuko, Ndabezinhle and Palma, Giulio and Bhat, Anjali and Montagnese, Marcella and Dave, Sonali and Tai, Yen F and Batzu, Lucia and Leta, Valentina and Chaudhuri, K Ray and Williams Gray, Caroline H and Mehta, Mitul A},
title = {{Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis}},
journal = {Nature. Mental health},
year = {2026},
month = jun,
volume = {4},
number = {6},
pages = {994--1009},
publisher = {Springer Science+Business Media},
issn = {2731-6076},
doi = {10.1038/s44220-026-00669-7},
url = {https://doi.org/10.1038/s44220-026-00669-7},
pmid = {42291779},
pmcid = {PMC13259922}
}

RIS

TY - JOUR
AU - Vignando, Miriam
AU - Ffytche, Dominic
AU - Mazibuko, Ndabezinhle
AU - Palma, Giulio
AU - Bhat, Anjali
AU - Montagnese, Marcella
AU - Dave, Sonali
AU - Tai, Yen F
AU - Batzu, Lucia
AU - Leta, Valentina
AU - Chaudhuri, K Ray
AU - Williams Gray, Caroline H
AU - Mehta, Mitul A
TI - Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis
T2 - Nature. Mental health
J2 - Nat Ment Health
PY - 2026
DA - 2026/06/10
VL - 4
IS - 6
SP - 994
EP - 1009
SN - 2731-6076
PB - Springer Science+Business Media
DO - 10.1038/s44220-026-00669-7
UR - https://doi.org/10.1038/s44220-026-00669-7
LA - en
ER -

CSL-JSON

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