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Multi-voxel MR-spectroscopy signatures and associations with EEG network hyperexcitability and clinical symptomatology in borderline personality disorder.

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  1. [1] § Methods › Magnetic resonance spectroscopy analysis and quality checks ↔ python/avg_q/mne.py, lines 49–87 · score 0.55 · FreeSurfer, MRI, volume, command, voxel, fitting

Paper

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The authors' code

Python · 102 lines · 4 KB · GPL-3.0 · 1 match

  1. import numpy
  2. import mne
  3. def to_Evokeds(epochs, montage='standard_1020'):
  4. '''epochs is a list of epochs as avg_q.numpy_Script epochs
  5. '''
  6. evokeds=[]
  7. for epoch in epochs:
  8. info=mne.create_info(ch_names=epoch.channelnames,sfreq=epoch.sfreq,ch_types='eeg')
  9. # cf https://mne.tools/dev/generated/mne.EvokedArray.html#mne.EvokedArray
  10. ev=mne.EvokedArray(
  11. epoch.data.transpose(),
  12. info,
  13. tmin= -epoch.beforetrig/epoch.sfreq,
  14. comment=epoch.comment,
  15. nave=epoch.nrofaverages,
  16. kind='average')
  17. ev.set_montage(montage)
  18. evokeds.append(ev)
  19. return evokeds
  20. def mne_Epochsource(mne_epochs):
  21. import avg_q.numpy_Script
  22. epochs=[]
  23. for thisepoch in mne_epochs:
  24. epoch=avg_q.numpy_Script.numpy_epoch(thisepoch.data.transpose())
  25. epoch.sfreq=thisepoch.info['sfreq']
  26. epoch.beforetrig= -round(thisepoch.tmin*thisepoch.info['sfreq'])
  27. epoch.nrofaverages=thisepoch.nave
  28. epoch.comment=' '.join([thisepoch.comment,thisepoch.get_channel_types()[0]])
  29. epoch.channelnames=thisepoch.ch_names
  30. epoch.channelpos=[x[:3] for x in mne.channels.find_layout(thisepoch.info).pos]
  31. epochs.append(epoch)
  32. epochsource=avg_q.numpy_Script.numpy_Epochsource(epochs)
  33. return epochsource
  34. def SphereSourceSpace_coords(circle_divisions=25, percentage=85):
  35. # Iter to yield theta and phi coordinates regularly distributed on a sphere
  36. # (open at the bottom towards theta=90° by 100-percentage)
  37. # This is separated out to reconstruct those coordinates later if needed
  38. dphi=numpy.pi/circle_divisions
  39. for theta in numpy.arange(dphi,numpy.pi*percentage/100,dphi):
  40. rdphi=dphi/numpy.sin(theta)
  41. # Correct this angle to avoid irregular (too close) distances between first and last point
  42. rdphi=2*numpy.pi/numpy.round(2*numpy.pi/rdphi)
  43. for phi in numpy.arange(0,2*numpy.pi,rdphi):
  44. yield (theta,phi)
  45. def make_SphereSourceSpace(sphereHM, radius=80, circle_divisions=25, percentage=85):
  46. '''sphereHM is a fitted spherical head model (mne.make_sphere_model)
  47. circle_divisions defines the number of sources on a full circle
  48. percentage is the coverage of the full sphere, leaving out part of its bottom
  49. '''
  50. # Try to approximate sources on a sphere surface by using "exclude"
  51. # Actually mne.setup_source_space() is for distributing sources on a surface
  52. # but requires FreeSurfer files
  53. # Source in /usr/lib/python3.11/site-packages/mne/source_space/_source_space.py
  54. #src1 = mne.setup_volume_source_space(sphere=sphere, pos=10.0, exclude=76) # 10mm grid
  55. # src is a 1-element list of dict, 'rr' contains the coordinates of *all* originally created sources
  56. # 'inuse' defines which are actually used
  57. r=radius/1000
  58. rr=numpy.array([
  59. (r*numpy.sin(theta)*numpy.sin(phi),r*numpy.sin(theta)*numpy.cos(phi),r*numpy.cos(theta))
  60. for theta,phi in SphereSourceSpace_coords(circle_divisions, percentage)
  61. ])
  62. np=rr.shape[0]
  63. ssdict={
  64. 'type': 'discrete',
  65. 'coord_frame': mne._fiff.constants.FIFF.FIFFV_COORD_MRI,
  66. 'id': mne._fiff.constants.FIFF.FIFFV_MNE_SURF_UNKNOWN,
  67. 'np': np,
  68. 'nuse': np,
  69. 'rr': rr+sphereHM['r0'],
  70. 'inuse': numpy.full((np,),True),
  71. 'nn': numpy.array([[0,0,1]]).repeat(np,axis=0),
  72. 'vertno': numpy.arange(np),
  73. 'neighbor_vert': numpy.array([[-1]*26]).repeat(np,axis=0),
  74. 'ntri': 0,
  75. 'use_tris': None,
  76. 'nearest': None,
  77. 'dist': None,
  78. #'src_mri_t': mne.source_space._source_space._make_voxel_ras_trans(move=numpy.array([0,0,0]),voxel_size=numpy.ones(3),ras=numpy.eye(3)),
  79. }
  80. import os
  81. return mne.SourceSpaces([ssdict], dict(working_dir=os.getcwd(), command_line="None"))
  82. def VolSourceToEvoked(stc, src, r0, ev):
  83. info=mne.create_info(stc.shape[0], stc.sfreq, ch_types='eeg')
  84. source_ev=mne.EvokedArray(
  85. stc.data,
  86. info,
  87. tmin=stc.tmin,
  88. comment=ev.comment,
  89. nave=ev.nave,
  90. kind='average')
  91. montage=ev.get_montage()
  92. ch_pos={ch_name: src[0]['rr'][int(ch_name)]-r0 for ch_name in source_ev.ch_names}
  93. dig=mne.channels.make_dig_montage(ch_pos=ch_pos, nasion=montage.dig[1]['r'], lpa=montage.dig[0]['r'], rpa=montage.dig[2]['r'], hsp=None, hpi=None, coord_frame='head')
  94. source_ev.set_montage(dig)
  95. return source_ev

mne.py at commit c90c29c, under GPL-3.0 · at the source

Overview

Authors: Andrea Schlump1,2, Bernd Feige1, Swantje Matthies1, Katharina von Zedtwitz1, Isabelle Matteit1, Kathrin Nickel1, Katharina Domschke1,3, Marco Reisert4,5, Thomas Lange4, Markus Heinrichs2, Dominique Endres1, Ludger Tebartz van Elst1, Simon Maier1
ORCID iDs: Kathrin Nickel
  1. Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
  2. Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy - University of Freiburg, Freiburg, Germany
  3. German Center for Mental Health (DZPG), Partner Site Berlin/Potsdam, Berlin, Germany
  4. Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Berlin, Germany
  5. Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
Journal: Frontiers in psychiatry, volume 17, article 1708563
Dates: received 18 September 2025; accepted 10 February 2026; published online 13 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fpsyt.2026.1708563 · PMID 41908118 · PMCID PMC13022906 · OpenAlex W7135185649
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: borderline personality disorder, electroencephalography, GABA, glutamate, IRDA/IRTA, NAA, spectroscopy
Topic: Personality Disorders and Psychopathology (Clinical Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Introduction: Previous studies have reported altered gamma-aminobutyric acid (GABA) and glutamate levels in borderline personality disorder (BPD), suggesting disruptions in excitatory-inhibitory neurotransmission. Electroencephalographic (EEG) research has indicated potential network hyperexcitability in BPD, evidenced by increased intermittent rhythmic delta and theta activity (IRDA/IRTA), which may reflect compensatory stabilization mechanisms. This study used multi-voxel magnetic resonance spectroscopic imaging (MRSI) to explore neurochemical abnormalities and their relationships with IRDA/IRTA, psychometric and neuropsychological measures.

Methods: Sixty-six female patients diagnosed with BPD (mean age: 30.2 ± 9.7 years) and 29 age-matched female healthy controls (mean age: 27.8 ± 8.0 years) received spirally encoded 3D MRSI scans. GABA, glutamate plus glutamine (Glx), total creatine (tCr) and total N-acetylaspartate (tNAA) were quantified and reported as ratios relative to tNAA and/or tCr. The resulting spectroscopic images were analyzed using LCModel and FreeSurfer software, and IRDA/IRTA detection in a clinical EEG session was performed using independent component analysis. Metabolite ratios were analyzed using hierarchical linear mixed-effects models (ROIs nested within participants), with fixed effects of group or, in separate models, continuous predictors (IRDA/IRTA and psychometric/neuropsychological measures), ROI, and their interaction, and a subject-level random intercept. ROI-specific effects were quantified using estimated marginal means and within-ROI contrasts (emmeans).

Results: No metabolite ratio showed a significant BPD vs. control difference. In BPD, IRDA/IRTA-related measures were positively associated with Glx/tCr and Glx/tNAA in the accumbens, and with Glx/tCr in the right caudal anterior cingulate cortex (cACC). BSL-supplement scores showed ROI-dependent associations with tCr/tNAA, with positive ROI-specific effects in the bilateral caudate, right pallidum, and right putamen. Alertness measures were linked to GABA/tCr, Glx/tNAA, and tCr/tNAA (including caudate, pallidum/putamen, cACC, and thalamus), while divided attention omissions and working-memory errors were associated with higher Glx/tCr in the cACC, isthmus cingulate, and hippocampus. ROI-dependent associations were also observed for IQ and verbal learning/recognition with GABA/tCr and tNAA/tCr.

Discussion: While no robust group differences emerged, mixed-models using continuous predictors linked higher Glx ratios in nucleus accumbens/cACC to IRDA/IRTA and higher striatal tCr/tNAA to BPD symptom severity and neurocognitive performance. These exploratory multimodal signatures point to cortico-striato-limbic mechanisms in BPD and should be confirmed in larger samples.

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.

berndf/avg_q

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: c90c29cd7061fc17ca95f082d23bf5765a696b4d, 3 September 2026
Languages: C (371), Python (53), C/C++ (50), C++ (13), R (4), MATLAB (1)
Size: 833 files, 492 scripts
Software Heritage: archived
Found in: the text, “EEG IRDA/IRTA analysis”
Holds: README, license file, continuous integration, documentation
Not found: CITATION.cff, environment file, tests
Tools: NumPy (5 files), SciPy (5 files), Matplotlib (2 files), h5py (1 file), MNE-Python (1 file), pandas (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
494 files

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  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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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 raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 13 authors, 7 keywords, 64 references.

Cite

This paper

Schlump, A., Feige, B., Matthies, S., von Zedtwitz, K., Matteit, I., Nickel, K., Domschke, K., Reisert, M., Lange, T., Heinrichs, M., Endres, D., Tebartz van Elst, L., & Maier, S. (2026). Multi-voxel MR-spectroscopy signatures and associations with EEG network hyperexcitability and clinical symptomatology in borderline personality disorder. Frontiers in psychiatry, 17, 1708563. https://doi.org/10.3389/fpsyt.2026.1708563

BibTeX

@article{schlump2026multi,
author = {Schlump, Andrea and Feige, Bernd and Matthies, Swantje and von Zedtwitz, Katharina and Matteit, Isabelle and Nickel, Kathrin and Domschke, Katharina and Reisert, Marco and Lange, Thomas and Heinrichs, Markus and Endres, Dominique and Tebartz van Elst, Ludger and Maier, Simon},
title = {{Multi-voxel MR-spectroscopy signatures and associations with EEG network hyperexcitability and clinical symptomatology in borderline personality disorder}},
journal = {Frontiers in psychiatry},
year = {2026},
month = mar,
volume = {17},
pages = {1708563},
publisher = {Frontiers Media SA},
issn = {1664-0640},
doi = {10.3389/fpsyt.2026.1708563},
url = {https://doi.org/10.3389/fpsyt.2026.1708563},
pmid = {41908118},
pmcid = {PMC13022906}
}

RIS

TY - JOUR
AU - Schlump, Andrea
AU - Feige, Bernd
AU - Matthies, Swantje
AU - von Zedtwitz, Katharina
AU - Matteit, Isabelle
AU - Nickel, Kathrin
AU - Domschke, Katharina
AU - Reisert, Marco
AU - Lange, Thomas
AU - Heinrichs, Markus
AU - Endres, Dominique
AU - Tebartz van Elst, Ludger
AU - Maier, Simon
TI - Multi-voxel MR-spectroscopy signatures and associations with EEG network hyperexcitability and clinical symptomatology in borderline personality disorder
T2 - Frontiers in psychiatry
J2 - Front Psychiatry
PY - 2026
DA - 2026/03/13
VL - 17
SP - 1708563
SN - 1664-0640
PB - Frontiers Media SA
DO - 10.3389/fpsyt.2026.1708563
UR - https://doi.org/10.3389/fpsyt.2026.1708563
LA - en
ER -

CSL-JSON

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"id": "10.3389/fpsyt.2026.1708563",
"type": "article-journal",
"title": "Multi-voxel MR-spectroscopy signatures and associations with EEG network hyperexcitability and clinical symptomatology in borderline personality disorder",
"container-title": "Frontiers in psychiatry",
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