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Neurophysiological, imaging and neurobiological markers of central fatigue in multiple sclerosis.

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Paper

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The authors' code

Shell · 179 lines · 5.9 KB · MIT

  1. #!/usr/bin/env bash
  2. # Written by Wu Jianxiao and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
  3. # This function warps an input volumes to a target volume space with specified ANTs warp or inverse warp files
  4. ###########################################
  5. # Main commands
  6. ###########################################
  7. main(){
  8. # Set up warp files
  9. warp=${warp_dir}/${warp_prefix}1Warp.nii.gz
  10. inverse=${warp_dir}/${warp_prefix}1InverseWarp.nii.gz
  11. affine=${warp_dir}/${warp_prefix}0GenericAffine.mat
  12. # Set up fmri_reg command
  13. if [ ! -z $fmri_reg ]; then
  14. fmri_warp="-t $fmri_reg"
  15. fi
  16. # Set up time_series command
  17. if [ $time_series -eq 1 ]; then
  18. vol4d="-e 3"
  19. fi
  20. # Warp input volume to target's T1 space
  21. output=$output_dir/${output_prefix}.nii.gz
  22. if [ ! -e $output ]; then
  23. case $warp_setting in
  24. forward)
  25. cmd="${ANTs_dir}/antsApplyTransforms -d 3 $vol4d -i $input -r $target -n $interp -t $warp -t $affine $fmri_warp"
  26. cmd="$cmd -o $output"
  27. echo $cmd
  28. eval $cmd
  29. ;;
  30. inverse)
  31. cmd="${ANTs_dir}/antsApplyTransforms -d 3 $vol4d -i $input -r $target -n $interp $fmri_warp -t [$affine, 1]"
  32. cmd="$cmd -t $inverse -o $output"
  33. echo $cmd
  34. eval $cmd
  35. ;;
  36. *)
  37. echo "Invalid warp setting. Use either 'forward' or 'inverse'"
  38. esac
  39. else
  40. echo "The input volume have already been projected into ${target}'s space by ANTs"
  41. fi
  42. }
  43. ##################################################################
  44. # Function usage
  45. ##################################################################
  46. # Usage
  47. usage() { echo "
  48. Usage: CBIG_antsApplyReg_vol2vol.sh -i input -r target -w warp_prefix -p output_prefix
  49. This script applies existing warps prepared using ANTs registration to map an input volume to a target volume.
  50. REQUIRED ARGUMENTS:
  51. -i <input> absolute path to input volume to map from
  52. -r <target> absolute path to target volume to be mapped to
  53. -w <warp_prefix> prefix of the warp files. Specifically, the affine, forward and inverse warps should have the
  54. following names:
  55. warp_prefix0GenericAffine.mat
  56. warp_prefix1Warp.nii.gz
  57. warp_prefix1InverseWarp.nii.gz
  58. -p <output_prefix> prefix for output volume
  59. OPTIONAL ARGUMENTS:
  60. -f <fmri_reg> absolute path to native-to-T1 registration file for fMRI input. The file must have been
  61. converted to format used by ANTs (e.g. .txt or .mat) if it was generated using other tools.
  62. [ default: unset ]
  63. -e <time_series> set this to 1 if the input volume is 4D (time-series)
  64. [ default: 0 ]
  65. -d <warp_dir> absolute path to the warps
  66. [ default: $(pwd)/results ]
  67. -o <output_dir> absolute path to output directory
  68. [ default: $(pwd)/results ]
  69. -s <warp setting> warping direction ('forward' for forward warp, 'inverse' for inverse warp). For example, if
  70. the target space was registered to the input space during ANTs registration, then inverse
  71. warp should be used to now map the input to the target.
  72. [ default: forward ]
  73. -t <interp> interpolation (Linear, NearestNeighbor, etc.)
  74. [ default: Linear ]
  75. -a <ANTs_dir> directory where ANTs is installed
  76. [ default: $CBIG_ANTS_DIR ]
  77. -h display help message
  78. OUTPUTS:
  79. $0 will create 1 output file in the output directory, corresponding to the input warped into target's space.
  80. For example:
  81. output_prefix.nii.gz
  82. EXAMPLE:
  83. $0 -i /path/to/my/data.nii.gz -r /path/to/my/template.nii.gz -w subject_moving_template_fixed -p data_to_template
  84. -f data_to_T1.txt -s forward
  85. " 1>&2; exit 1; }
  86. # Display help message if no argument is supplied
  87. if [ $# -eq 0 ]; then
  88. usage; 1>&2; exit 1
  89. fi
  90. ##################################################################
  91. # Assign input variables
  92. ##################################################################
  93. # Default parameter
  94. warp_dir=$(pwd)/results
  95. output_dir=$(pwd)/results
  96. warp_setting=forward
  97. interp=Linear
  98. ANTs_dir=$CBIG_ANTS_DIR
  99. time_series=0
  100. # Assign parameter
  101. while getopts "i:r:w:p:f:e:d:o:s:t:a:h" opt; do
  102. case $opt in
  103. i) input=${OPTARG} ;;
  104. r) target=${OPTARG} ;;
  105. w) warp_prefix=${OPTARG} ;;
  106. p) output_prefix=${OPTARG} ;;
  107. f) fmri_reg=${OPTARG} ;;
  108. e) time_series=${OPTARG} ;;
  109. d) warp_dir=${OPTARG} ;;
  110. o) output_dir=${OPTARG} ;;
  111. s) warp_setting=${OPTARG} ;;
  112. t) interp=${OPTARG} ;;
  113. a) ANTs_dir=${OPTARG} ;;
  114. h) usage; exit ;;
  115. *) usage; 1>&2; exit 1 ;;
  116. esac
  117. done
  118. ##################################################################
  119. # Check parameter
  120. ##################################################################
  121. if [ -z $target ]; then
  122. echo "Reference volume not defined."; 1>&2; exit 1
  123. fi
  124. if [ -z $input ]; then
  125. echo "Input volume not defined."; 1>&2; exit 1
  126. fi
  127. if [ -z $warp_prefix ]; then
  128. echo "Warp prefix not defined."; 1>&2; exit 1
  129. fi
  130. if [ -z $output_prefix ]; then
  131. echo "Output prefix not defined."; 1>&2; exit 1
  132. fi
  133. ##################################################################
  134. # Disable multi-threading
  135. ##################################################################
  136. ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
  137. export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS
  138. ##################################################################
  139. # Set up output directory
  140. ##################################################################
  141. if [ ! -d "$output_dir" ]; then
  142. echo "Output directory does not exist. Making directory now..."
  143. mkdir -p $output_dir
  144. fi
  145. ###########################################
  146. # Implementation
  147. ###########################################
  148. main

CBIG_antsApplyReg_vol2vol.sh at commit 35b5664, under MIT · at the source

Overview

Authors: Alberto Benelli1, Elisa Tatti2, Rosa Cortese3, Elisa Massucco1, Ludovico Luchetti3,4, Marco Battaglini3,4, Javier Cudeiro5, Anna de Mauro3, Jian Zhang6, Domenico Plantone3, Patrizio Pasqualetti7, Delia Righi3, Francesco Neri1,8, Maria Laura Stromillo3, Alessandra Cinti1, Alessandro Giannotta1, Francesco Lomi1, Adriano Scoccia1, Giuseppe Lai9, Nicola De Stefano3, Monica Ulivelli3, Simone Rossi1,3,8
  1. Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, 53100, Italy
  2. Department of Molecular, Cellular and Biomedical Sciences, City University of NewYork, School of Medicine, New York, NY, 10031, United States
  3. UOC Neurologia, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, 53100, Italy
  4. Siena Imaging SRL, Siena, 53100, Italy
  5. Department of Physiotherapy, Medicine, and Biomedical Sciences, NEUROcom (Neuroscience and Motor Control Group), CICA (Interdisciplinary Center for Chemistry and Biology), and Galician Brain Stimulation Centre, Universidade da Coruña, A Coruña, 15001, Spain
  6. The First Affiliated Hospital, Guangxi Medical University, Nanning, 530000, China
  7. Health Statistics, University La Sapienza, Roma, 00185, Italy
  8. Oto-Neuro-Tech Conjoined Lab, Policlinico Le Scotte, University of Siena, Siena, 53100, Italy
  9. Goldsmiths, UK Department of Psychology, University of London, London, WC1E 7HU, UK
Journal: Brain communications, volume 8, issue 3, article fcag134
Dates: received 25 August 2025; accepted 15 April 2026; published online 16 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag134 · PMID 42099304 · PMCID PMC13148770 · OpenAlex W7154608470
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning, Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: multiple sclerosis, central fatigue, neurophysiology, structural connectivity, functional connectivity
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: U.S. Department of Defense (W81XWH1910810, W81XWH-19-1, W81XWH); Fondazione Italiana Sclerosi Multipla
Citations: not cited yet (Europe PMC); 83 references in the paper
Research resources: RRID:SCR_009550

Abstract

Central fatigue affects 80% of patients with multiple sclerosis, with 60% of them claiming it as the most disabling symptom. Current research often independently explores neurophysiological, structural, or functional imaging and biological underpinnings of fatigue, thus lacking a multidimensional perspective. Here, we used a multidimensional approach to investigate the functional, structural and biological underpinnings of fatigue in MS and to assess the relative contribution of each factor. A cross-sectional study was conducted with 41 patients with relapsing–remitting multiple sclerosis and 21 healthy controls (female 14) (HC). MS patients were recruited by including only those with an Expanded Disability Status Scale score < 4, and were categorized as fatigued (MS-F: 19, Female 13, FSS ≥ 4) or non-fatigued (MS-NF: 22, Female 11, FSS < 4). Over five phases, participants underwent Transcranial Magnetic Stimulation, resting-state Electroencephalography, structural and functional Magnetic Resonance, clinical assessments, and blood tests for neurofilament light chain, serum glial fibrillary acidic protein and cytokine levels. Data were analysed using both non-parametric and parametric tests, based on the data distribution. Finally, a decision-tree model was applied to predict patient group assignment. Neurophysiologically, the two patient groups differed in several domains. Those with fatigue had increased θ-band EEG power in frontocentral regions with eyes open. Transcranial Magnetic Stimulation findings indicated significantly lower intracortical facilitation in the MS-F group. Neuroimaging revealed stronger functional connectivity between nodes of the Default Mode Network, between the left temporal node and the right prefrontal node, in the MS-F group. Furthermore, fractional anisotropy via Diffusion Tensor Imaging showed reduced white matter integrity in the corticospinal tracts and corpus callosum in these patients. No significant differences were observed in lesion load, brain volumes, clinical/psychological measures, or blood sample findings linked with neurodegeneration or inflammation; the only psychological variable that differed between the two groups was the depression scale score, with MS-F patients reporting higher scores than MS-NF patients. The decision tree analysis identified both ICF and significantly lower fractional anisotropy values as the most accurate predictors of fatigue, with a classification accuracy of 84.2%. Results highlight the importance of a multidisciplinary approach in defining central fatigue in multiple sclerosis, which would emerge through subtle, subclinical, regional abnormalities of myelin integrity and clearly manifest neurophysiological evidence of impaired glutamatergic activity in motor areas. They also suggest possible biomarkers for the diagnosis of fatigue, possibly useful for eventual targeting novel neuromodulatory treatments.

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

Repository

Its files are read in the Code ↔ Paper reader above.

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 35b5664bec8822e2f77da5e090e96f91d0095be6, 31 August 2026
Languages: MATLAB (2100), Shell (651), Python (571), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,187 files, 3,592 scripts
Software Heritage: not archived
Found in: the text, “Statistical analyses”
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 (93 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: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,998 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 availability

Due to privacy and consent restrictions, individual-level neuroimaging, neurophysiological, clinical and behavioural data are not publicly available but can be shared in anonymized form upon request to the corresponding author, subject to institutional approvals. No new MATLAB codes were generated.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 5 keywords, 2 funders, 80 references, 1 RRID.

Cite

This paper

Benelli, A., Tatti, E., Cortese, R., Massucco, E., Luchetti, L., Battaglini, M., Cudeiro, J., de Mauro, A., Zhang, J., Plantone, D., Pasqualetti, P., Righi, D., Neri, F., Stromillo, M. L., Cinti, A., Giannotta, A., Lomi, F., Scoccia, A., Lai, G., . . . Rossi, S. (2026). Neurophysiological, imaging and neurobiological markers of central fatigue in multiple sclerosis. Brain communications, 8(3), fcag134. https://doi.org/10.1093/braincomms/fcag134

BibTeX

@article{benelli2026neurophysiological,
author = {Benelli, Alberto and Tatti, Elisa and Cortese, Rosa and Massucco, Elisa and Luchetti, Ludovico and Battaglini, Marco and Cudeiro, Javier and de Mauro, Anna and Zhang, Jian and Plantone, Domenico and Pasqualetti, Patrizio and Righi, Delia and Neri, Francesco and Stromillo, Maria Laura and Cinti, Alessandra and Giannotta, Alessandro and Lomi, Francesco and Scoccia, Adriano and Lai, Giuseppe and De Stefano, Nicola and Ulivelli, Monica and Rossi, Simone},
title = {{Neurophysiological, imaging and neurobiological markers of central fatigue in multiple sclerosis}},
journal = {Brain communications},
year = {2026},
month = apr,
volume = {8},
number = {3},
pages = {fcag134},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag134},
url = {https://doi.org/10.1093/braincomms/fcag134},
pmid = {42099304},
pmcid = {PMC13148770}
}

RIS

TY - JOUR
AU - Benelli, Alberto
AU - Tatti, Elisa
AU - Cortese, Rosa
AU - Massucco, Elisa
AU - Luchetti, Ludovico
AU - Battaglini, Marco
AU - Cudeiro, Javier
AU - de Mauro, Anna
AU - Zhang, Jian
AU - Plantone, Domenico
AU - Pasqualetti, Patrizio
AU - Righi, Delia
AU - Neri, Francesco
AU - Stromillo, Maria Laura
AU - Cinti, Alessandra
AU - Giannotta, Alessandro
AU - Lomi, Francesco
AU - Scoccia, Adriano
AU - Lai, Giuseppe
AU - De Stefano, Nicola
AU - Ulivelli, Monica
AU - Rossi, Simone
TI - Neurophysiological, imaging and neurobiological markers of central fatigue in multiple sclerosis
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/04/16
VL - 8
IS - 3
SP - fcag134
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag134
UR - https://doi.org/10.1093/braincomms/fcag134
LA - en
ER -

CSL-JSON

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