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Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states.

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  1. [1] § Methods › MRI data analysis › Data preprocessing ↔ FIT/ica_fuse/ica_fuse_spm_files/ica_fuse_spm_smoothkern.m, the whole file · a weak match · score 0.59 · full width, SPM, smoothing, FWHM, Gaussian

Paper

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The authors' code

MATLAB · 55 lines · 2 KB · no license · 1 match

  1. function krn = ica_fuse_spm_smoothkern(fwhm,x,t)
  2. % Generate a Gaussian smoothing kernel
  3. % FORMAT krn = spm_smoothkern(fwhm,x,t)
  4. % fwhm - full width at half maximum
  5. % x - position
  6. % t - either 0 (nearest neighbour) or 1 (linear).
  7. % [Default: 1]
  8. %
  9. % krn - value of kernel at position x
  10. %__________________________________________________________________________
  11. %
  12. % For smoothing images, one should really convolve a Gaussian with a sinc
  13. % function. For smoothing histograms, the kernel should be a Gaussian
  14. % convolved with the histogram basis function used. This function returns
  15. % a Gaussian convolved with a triangular (1st degree B-spline) basis
  16. % function (by default). A Gaussian convolved with a hat function (0th
  17. % degree B-spline) can also be returned.
  18. %__________________________________________________________________________
  19. % Copyright (C) 2005-2011 Wellcome Trust Centre for Neuroimaging
  20. % John Ashburner
  21. % $Id: spm_smoothkern.m 4419 2011-08-03 18:42:35Z guillaume $
  22. if nargin<3, t = 1; end
  23. % Variance from FWHM
  24. s = (fwhm/sqrt(8*log(2)))^2+eps;
  25. % The simple way to do it. Not good for small FWHM
  26. % krn = (1/sqrt(2*pi*s))*exp(-(x.^2)/(2*s));
  27. if t==0
  28. % Gaussian convolved with 0th degree B-spline
  29. % int(exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= -0.5..0.5)
  30. w1 = 1/sqrt(2*s);
  31. krn = 0.5*(erf(w1*(x+0.5))-erf(w1*(x-0.5)));
  32. krn(krn<0) = 0;
  33. elseif t==1
  34. % Gaussian convolved with 1st degree B-spline
  35. % int((1-t)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= 0..1)
  36. % +int((t+1)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t=-1..0)
  37. w1 = 0.5*sqrt(2/s);
  38. w2 = -0.5/s;
  39. w3 = sqrt(s/2/pi);
  40. krn = 0.5*(erf(w1*(x+1)).*(x+1) + erf(w1*(x-1)).*(x-1) - 2*erf(w1*x ).* x)...
  41. +w3*(exp(w2*(x+1).^2) + exp(w2*(x-1).^2) - 2*exp(w2*x.^2));
  42. krn(krn<0) = 0;
  43. else
  44. error('Only defined for nearest neighbour and linear interpolation.');
  45. % If anyone knows a nice formula for a sinc function convolved with a
  46. % a Gaussian, then that could be quite useful.
  47. end

ica_fuse_spm_smoothkern.m at commit 29f72fa, no license · at the source

Overview

Authors: Anne Kerstin Thomann1, Mike Michael Schmitgen2, Jule Cara Stephan1, Laura-Louise Knoedler1, Philipp Arthur Thomann3, Kristina Szabo4, Matthias Philip Ebert1, Wolfgang Reindl1, Robert Christian Wolf2
  1. Department of Medicine II, Medical Faculty Mannheim, Heidelberg University,Theodor-Kutzer-Ufer 1-3, Mannheim, 68167 Germany
  2. Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University Hospital, Heidelberg University,Heidelberg, Germany
  3. Department of Psychiatry and Psychotherapy, SRH Clinic Karlsbad-Langensteinbach, Karlsbad, Germany
  4. Department of Neurology/Neuroimaging, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences (MCTN), University of Heidelberg,Mannheim, Germany
Journal: BMC gastroenterology, volume 26, issue 1, article 438
Dates: received 12 September 2025; accepted 2 July 2026; published online 9 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s12876-026-05097-6 · PMID 42426651 · PMCID PMC13352982 · OpenAlex W7167847926
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), depression (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
Keywords: Anxiety, Brain-Gut-Axis, Depression, Extraintestinal Symptoms, Fatigue, Inflammatory Bowel Diseases, Neurotransmitters, Resting-state brain activity
MeSH: Anxiety*, Brain*, Crohn Disease*, Depression*, Fatigue*, Nerve Net*, Adult, Case-Control Studies, Feces, Female, Humans, Leukocyte L1 Antigen Complex, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult (* major topic)
Topic: Inflammatory Bowel Disease (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Medizinische Fakultät Heidelberg der Universität Heidelberg (9149)
Citations: not cited yet (Europe PMC); 30 references in the paper

Abstract

Background: Symptoms of fatigue, depression or anxiety are frequent in Crohn’s Disease (CD) and may relate to disturbed brain-gut interactions. While more prevalent in active disease, these symptoms are also experienced by many individuals with CD during remission. Little is known about neural networks underlying such extraintestinal symptoms in CD and their relationship with the current disease state. Using a data fusion approach for functional MRI, this study investigated spatiotemporal markers of resting-state brain activity and associations with neurotransmitter systems and symptoms of fatigue, depression or anxiety in an active disease state or remission.

Methods: We examined n = 71 patients with CD in an active disease state (aCD; n = 47) or in remission (rCD; n = 24) and healthy controls (HC; n = 35). All participants underwent resting-state fMRI, completed symptom assessments for fatigue, depression and anxiety, and provided stool samples for analysis of faecal calprotectin (fCal; aCD and rCD only). Joint independent component analysis (jICA) of two resting-state brain activity parameters (temporal and spatial features) identified neural networks exhibiting disease-state-dependent alterations. Network connectivity strength was correlated with symptoms of fatigue, depression, and anxiety, as well as fecal calprotectin (fCal). We further explored associations of the networks with neurotransmitter receptor maps.

Results: JICA revealed three networks differentiating between disease states and/or between patients and controls. One network comprising affective orbitofrontal and temporal brain regions, exhibited reduced connectivity in active disease (HC vs. aCD: p = 0.003, pFDR = 0.01; aCD vs. rCD: p < 0.001, pFDR < 0.001) and was linked to serotonergic/dopaminergic transmission, fatigue, and fCal. Another network comprised sensorimotor brain regions and showed diminished connectivity in patients in remission (HC vs. rCD: p = 0.034, pFDR = 0.06; aCD vs. rCD: p = 0.003, pFDR = 0.01), correlating with depression, anxiety, and dopaminergic activity. The third network reflected the default-mode network topography and distinguished patients irrespective of disease status from controls (HC vs. aCD: p = 0.013, pFDR = 0.01; HC vs. rCD: p = 0.037, pFDR = 0.06), but showed no associations with symptoms.

Conclusions: Resting-state brain network connectivity in patients with CD differed between active disease and remission, and was associated with symptoms of fatigue, depression, and anxiety. Alterations in sensorimotor networks were linked to depressive and anxiety symptoms, whereas affect-related networks were associated with fatigue. These observations suggest that distinct brain networks may contribute to specific neuropsychiatric symptom clusters in Crohn’s disease and underscore the role of brain–gut axis mechanisms in these manifestations.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12876-026-05097-6.

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 1 match between paragraphs and lines of code.

trendscenter/fit

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 29f72fa7363b954a780c45945a9dc14a7d66d809, 20 August 2026
Languages: MATLAB (542), C (36), C/C++ (6), Python (2)
Size: 796 files, 586 scripts
Software Heritage: archived
Found in: the text, “Synthesis of spatiotemporal features translated ”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: GIFT (23 files), Statistics and Machine Learning Toolbox (9 files), FieldTrip (4 files), GIfTI library for MATLAB (4 files), SPM (4 files), NumPy (2 files), scikit-learn (2 files), SciPy (2 files), Parallel Computing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
587 files

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;
  • 586 scripts, each with its path and the digest of its content;
  • 1 match 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

All data needed to evaluate the conclusions in the paper are presented in the paper. Raw MRI data and parts of the clinical data can be provided by the corresponding authors upon reasonable request, pending scientific review and a completed material transfer agreement. Requests for underlying data should be submitted to the corresponding author.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 8 keywords, 16 MeSH terms, 1 funder, 30 references.

Cite

This paper

Thomann, A. K., Schmitgen, M. M., Stephan, J. C., Knoedler, L.-L., Thomann, P. A., Szabo, K., Ebert, M. P., Reindl, W., & Wolf, R. C. (2026). Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states. BMC gastroenterology, 26(1), 438. https://doi.org/10.1186/s12876-026-05097-6

BibTeX

@article{thomann2026brain,
author = {Thomann, Anne Kerstin and Schmitgen, Mike Michael and Stephan, Jule Cara and Knoedler, Laura-Louise and Thomann, Philipp Arthur and Szabo, Kristina and Ebert, Matthias Philip and Reindl, Wolfgang and Wolf, Robert Christian},
title = {{Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states}},
journal = {BMC gastroenterology},
year = {2026},
month = jul,
volume = {26},
number = {1},
pages = {438},
publisher = {BMC},
issn = {1471-230X},
doi = {10.1186/s12876-026-05097-6},
url = {https://doi.org/10.1186/s12876-026-05097-6},
pmid = {42426651},
pmcid = {PMC13352982}
}

RIS

TY - JOUR
AU - Thomann, Anne Kerstin
AU - Schmitgen, Mike Michael
AU - Stephan, Jule Cara
AU - Knoedler, Laura-Louise
AU - Thomann, Philipp Arthur
AU - Szabo, Kristina
AU - Ebert, Matthias Philip
AU - Reindl, Wolfgang
AU - Wolf, Robert Christian
TI - Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states
T2 - BMC gastroenterology
J2 - BMC Gastroenterol
PY - 2026
DA - 2026/07/09
VL - 26
IS - 1
SP - 438
SN - 1471-230X
PB - BMC
DO - 10.1186/s12876-026-05097-6
UR - https://doi.org/10.1186/s12876-026-05097-6
LA - en
ER -

CSL-JSON

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"container-title": "BMC gastroenterology",
"author": [
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"family": "Thomann",
"given": "Anne Kerstin"
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