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

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[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

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The authors' code

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

import numpy
import mne
def to_Evokeds(epochs, montage='standard_1020'):
'''epochs is a list of epochs as avg_q.numpy_Script epochs
'''
evokeds=[]
for epoch in epochs:
info=mne.create_info(ch_names=epoch.channelnames,sfreq=epoch.sfreq,ch_types='eeg')
# cf https://mne.tools/dev/generated/mne.EvokedArray.html#mne.EvokedArray
ev=mne.EvokedArray(
epoch.data.transpose(),
info,
tmin= -epoch.beforetrig/epoch.sfreq,
comment=epoch.comment,
nave=epoch.nrofaverages,
kind='average')
ev.set_montage(montage)
evokeds.append(ev)
return evokeds
def mne_Epochsource(mne_epochs):
import avg_q.numpy_Script
epochs=[]
for thisepoch in mne_epochs:
epoch=avg_q.numpy_Script.numpy_epoch(thisepoch.data.transpose())
epoch.sfreq=thisepoch.info['sfreq']
epoch.beforetrig= -round(thisepoch.tmin*thisepoch.info['sfreq'])
epoch.nrofaverages=thisepoch.nave
epoch.comment=' '.join([thisepoch.comment,thisepoch.get_channel_types()[0]])
epoch.channelnames=thisepoch.ch_names
epoch.channelpos=[x[:3] for x in mne.channels.find_layout(thisepoch.info).pos]
epochs.append(epoch)
epochsource=avg_q.numpy_Script.numpy_Epochsource(epochs)
return epochsource
def SphereSourceSpace_coords(circle_divisions=25, percentage=85):
# Iter to yield theta and phi coordinates regularly distributed on a sphere
# (open at the bottom towards theta=90° by 100-percentage)
# This is separated out to reconstruct those coordinates later if needed
dphi=numpy.pi/circle_divisions
for theta in numpy.arange(dphi,numpy.pi*percentage/100,dphi):
rdphi=dphi/numpy.sin(theta)
# Correct this angle to avoid irregular (too close) distances between first and last point
rdphi=2*numpy.pi/numpy.round(2*numpy.pi/rdphi)
for phi in numpy.arange(0,2*numpy.pi,rdphi):
yield (theta,phi)
def make_SphereSourceSpace(sphereHM, radius=80, circle_divisions=25, percentage=85):
'''sphereHM is a fitted spherical head model (mne.make_sphere_model)
circle_divisions defines the number of sources on a full circle
percentage is the coverage of the full sphere, leaving out part of its bottom
'''
# Try to approximate sources on a sphere surface by using "exclude"
# Actually mne.setup_source_space() is for distributing sources on a surface
# but requires FreeSurfer files
# Source in /usr/lib/python3.11/site-packages/mne/source_space/_source_space.py
#src1 = mne.setup_volume_source_space(sphere=sphere, pos=10.0, exclude=76) # 10mm grid
# src is a 1-element list of dict, 'rr' contains the coordinates of *all* originally created sources
# 'inuse' defines which are actually used
r=radius/1000
rr=numpy.array([
(r*numpy.sin(theta)*numpy.sin(phi),r*numpy.sin(theta)*numpy.cos(phi),r*numpy.cos(theta))
for theta,phi in SphereSourceSpace_coords(circle_divisions, percentage)
])
np=rr.shape[0]
ssdict={
'type': 'discrete',
'coord_frame': mne._fiff.constants.FIFF.FIFFV_COORD_MRI,
'id': mne._fiff.constants.FIFF.FIFFV_MNE_SURF_UNKNOWN,
'np': np,
'nuse': np,
'rr': rr+sphereHM['r0'],
'inuse': numpy.full((np,),True),
'nn': numpy.array([[0,0,1]]).repeat(np,axis=0),
'vertno': numpy.arange(np),
'neighbor_vert': numpy.array([[-1]*26]).repeat(np,axis=0),
'ntri': 0,
'use_tris': None,
'nearest': None,
'dist': None,
#'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)),
}
import os
return mne.SourceSpaces([ssdict], dict(working_dir=os.getcwd(), command_line="None"))
def VolSourceToEvoked(stc, src, r0, ev):
info=mne.create_info(stc.shape[0], stc.sfreq, ch_types='eeg')
source_ev=mne.EvokedArray(
stc.data,
info,
tmin=stc.tmin,
comment=ev.comment,
nave=ev.nave,
kind='average')
montage=ev.get_montage()
ch_pos={ch_name: src[0]['rr'][int(ch_name)]-r0 for ch_name in source_ev.ch_names}
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')
source_ev.set_montage(dig)
return source_ev

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

Overview

Authors: Andrea Schlump^1,2, Bernd Feige¹, Swantje Matthies¹, Katharina von Zedtwitz¹, Isabelle Matteit¹, Kathrin Nickel¹, Katharina Domschke^1,3, Marco Reisert^4,5, Thomas Lange⁴, Markus Heinrichs², Dominique Endres¹, Ludger Tebartz van Elst¹, Simon Maier¹

ORCID iDs: Kathrin Nickel

Institutions: University of Freiburg (Germany); University Medical Center Freiburg (Germany); Deutsches Zentrum für Psychische Gesundheit (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

Documentation/Create_Graphs.R, R, 36 lines
Documentation/plot_fftfilter.R, R, 38 lines
TestSuite/t_test_check.R, R, 36 lines
avg_q.c, C, 327 lines
avg_q_ui/avg_q_ui.c, C, 2,226 lines
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python/avg_q/Expyriment.py, Python, 90 lines
python/avg_q/ICA_ArtControl.py, Python, 282 lines
python/avg_q/Measure.py, Python, 96 lines
python/avg_q/Paradigm_Script.py, Python, 86 lines
python/avg_q/Presentation.py, Python, 92 lines
python/avg_q/Ranges.py, Python, 104 lines
python/avg_q/Sagura.py, Python, 108 lines
python/avg_q/Sleep/Embla_slfile.py, Python, 53 lines
python/avg_q/Sleep/Slow_Waves.py, Python, 83 lines
python/avg_q/Sleep/Spindles.py, Python, 99 lines
python/avg_q/Sleep/__init__.py, Python, 5 lines
python/avg_q/Sleep/bookno.py, Python, 31 lines
python/avg_q/Sleep/csv_slfile.py, Python, 51 lines
python/avg_q/Sleep/delphi_table.py, Python, 42 lines
python/avg_q/Sleep/freiburg_setup.py, Python, 99 lines
python/avg_q/Sleep/sleep_eeg.py, Python, 332 lines
python/avg_q/Sleep/sleep_file.py, Python, 137 lines
python/avg_q/Sleep/slfile.py, Python, 425 lines
python/avg_q/Sleep/slplot.py, Python, 134 lines
python/avg_q/Unisens.py, Python, 123 lines
python/avg_q/__init__.py, Python, 21 lines
python/avg_q/avg_q.py, Python, 467 lines
python/avg_q/avg_q_file.py, Python, 233 lines
python/avg_q/channelnames2channelpos.py, Python, 17 lines
python/avg_q/eeglab.py, Python, 53 lines
python/avg_q/fMRI_Correct.py, Python, 705 lines
python/avg_q/idircache.py, Python, 57 lines
python/avg_q/mne.py, Python, 102 lines, 1 match
python/avg_q/nirs.py, Python, 75 lines
python/avg_q/numpy_Script.py, Python, 318 lines
python/avg_q/paradigm.py, Python, 295 lines
python/avg_q/regressors.py, Python, 145 lines
python/avg_q/t_detector.py, Python, 142 lines
python/avg_q/trgfile.py, Python, 193 lines
python/avg_q/unittests.py, Python, 51 lines
python/avg_q_unittests.py, Python, 204 lines
python/voglps.py, Python, 60 lines
python/voglps2svg.py, Python, 48 lines
python/voglps2svg_cairo.py, Python, 45 lines
readymade/ascaverage.c, C, 372 lines
readymade/asccalc.c, C, 179 lines
readymade/distance_correct.c, C, 226 lines
readymade/spline_gridder.c, C, 371 lines
readymade/swap_xz.c, C, 125 lines
vogl/drivers/NeXT.c, C, 571 lines
vogl/drivers/X11.c, C, 1,277 lines
vogl/drivers/apollo.c, C, 583 lines
vogl/drivers/decX11.c, C, 582 lines
vogl/drivers/grx.c, C, 655 lines
vogl/drivers/hpdxy.c, C, 442 lines
vogl/drivers/ibmpc/cga.c, C, 176 lines
vogl/drivers/ibmpc/ega.c, C, 125 lines
vogl/drivers/ibmpc/hgc.c, C, 159 lines
vogl/drivers/ibmpc/mswin.c, C, 1,062 lines
vogl/drivers/ibmpc/mswin.h, C/C++, 7 lines
vogl/drivers/ibmpc/pc_fill.c, C, 165 lines
vogl/drivers/ibmpc/pc_getke.c, C, 14 lines
vogl/drivers/ibmpc/pc_locat.c, C, 31 lines
vogl/drivers/ibmpc/pc_text.c, C, 139 lines
vogl/drivers/ibmpc/setmode.c, C, 33 lines
vogl/drivers/ibmpc/sigma.c, C, 151 lines
vogl/drivers/ibmpc/vega_col.c, C, 31 lines
vogl/drivers/ibmpc/vega_dra.c, C, 19 lines
vogl/drivers/ibmpc/vega_tex.c, C, 64 lines
vogl/drivers/ibmpc/vga.c, C, 127 lines
vogl/drivers/ps.c, C, 664 lines
vogl/drivers/sun.c, C, 908 lines
vogl/drivers/tek.c, C, 449 lines
vogl/drivers/wxwin.cpp, C++, 370 lines
vogl/examples/balls.c, C, 169 lines
vogl/examples/circtxt.c, C, 164 lines
vogl/examples/cube.c, C, 165 lines
vogl/examples/curves.c, C, 194 lines
vogl/examples/lcube.c, C, 186 lines
vogl/examples/loc.c, C, 85 lines
vogl/examples/moretxt.c, C, 217 lines
vogl/examples/moretxt2.c, C, 176 lines
vogl/examples/mswin/balls.c, C, 170 lines
vogl/examples/mswin/circtxt.c, C, 165 lines
vogl/examples/mswin/cube.c, C, 166 lines
vogl/examples/mswin/curves.c, C, 195 lines
vogl/examples/mswin/lcube.c, C, 202 lines
vogl/examples/mswin/loc.c, C, 86 lines
vogl/examples/mswin/moretxt.c, C, 218 lines
vogl/examples/mswin/moretxt2.c, C, 177 lines
vogl/examples/mswin/objvws.c, C, 197 lines
vogl/examples/mswin/patches.c, C, 285 lines
vogl/examples/mswin/poly.c, C, 196 lines
vogl/examples/mswin/shapes.c, C, 159 lines
vogl/examples/mswin/simple.c, C, 93 lines
vogl/examples/mswin/sinewave.c, C, 81 lines
vogl/examples/mswin/teapot.c, C, 499 lines
vogl/examples/mswin/tetra.c, C, 181 lines
vogl/examples/mswin/tmesh.c, C, 165 lines
vogl/examples/mswin/trivial.c, C, 55 lines
vogl/examples/mswin/views.c, C, 190 lines
vogl/examples/mswin/voglwin.h, C/C++, 7 lines
vogl/examples/mswin/world.c, C, 166 lines
vogl/examples/objvws.c, C, 196 lines
vogl/examples/patches.c, C, 284 lines
vogl/examples/poly.c, C, 195 lines
vogl/examples/shapes.c, C, 158 lines
vogl/examples/simple.c, C, 92 lines
vogl/examples/sinewave.c, C, 80 lines
vogl/examples/stetra.c, C, 220 lines
vogl/examples/sunview/balls.c, C, 141 lines
vogl/examples/sunview/lcube.c, C, 91 lines
vogl/examples/sunview/svballs.c, C, 73 lines
vogl/examples/sunview/svlcube.c, C, 255 lines
vogl/examples/teapot.c, C, 498 lines
vogl/examples/tetra.c, C, 180 lines
vogl/examples/tmesh.c, C, 165 lines
vogl/examples/trivial.c, C, 54 lines
vogl/examples/views.c, C, 189 lines
vogl/examples/world.c, C, 165 lines
vogl/examples/xt/balls.c, C, 144 lines
vogl/examples/xt/lcube.c, C, 91 lines
vogl/examples/xt/xtballs.c, C, 110 lines
vogl/examples/xt/xtlcube.c, C, 271 lines
vogl/examples/xview/balls.c, C, 141 lines
vogl/examples/xview/lcube.c, C, 91 lines
vogl/examples/xview/xvballs.c, C, 120 lines
vogl/examples/xview/xvlcube.c, C, 397 lines
vogl/hershey/src/check.c, C, 18 lines
vogl/hershey/src/fdisp.c, C, 66 lines
vogl/hershey/src/fhtext.c, C, 451 lines
vogl/hershey/src/futura.m, MATLAB, not shown here
vogl/hershey/src/getchar.c, C, 58 lines
vogl/hershey/src/h2v.c, C, 196 lines
vogl/hershey/src/h2v.h, C/C++, 243 lines
vogl/hershey/src/halloc.c, C, 23 lines
vogl/hershey/src/hdisp.c, C, 133 lines
vogl/hershey/src/htext.c, C, 718 lines
vogl/hershey/src/times.r, R, not shown here
vogl/src/arcs.c, C, 379 lines
vogl/src/attr.c, C, 95 lines
vogl/src/buffer.c, C, 137 lines
vogl/src/clip.c, C, 143 lines
vogl/src/curves.c, C, 279 lines
vogl/src/draw.c, C, 296 lines
vogl/src/drivers.c, C, 703 lines
vogl/src/fvodevic.h, C/C++, 57 lines
vogl/src/fvodevice.h, C/C++, 57 lines
vogl/src/fvogl.h, C/C++, 20 lines
vogl/src/getgp.c, C, 33 lines
vogl/src/lines.c, C, 122 lines
vogl/src/mapping.c, C, 43 lines
vogl/src/matrix.c, C, 298 lines
vogl/src/minv4.c, C, 60 lines
vogl/src/move.c, C, 195 lines
vogl/src/msfort/zap_c.c, C, 26 lines
vogl/src/newtoken.c, C, 43 lines
vogl/src/newtokens.c, C, 44 lines
vogl/src/objects.c, C, 466 lines
vogl/src/patches.c, C, 472 lines
vogl/src/points.c, C, 109 lines
vogl/src/polygons.c, C, 1,114 lines
vogl/src/pref.c, C, 66 lines
vogl/src/queue.c, C, 413 lines
vogl/src/rect.c, C, 99 lines
vogl/src/scale.c, C, 48 lines
vogl/src/sunfort/farcs.c, C, 166 lines
vogl/src/sunfort/fattr.c, C, 37 lines
vogl/src/sunfort/fbgnend.c, C, 211 lines
vogl/src/sunfort/fbuffer.c, C, 96 lines
vogl/src/sunfort/fcurves.c, C, 102 lines
vogl/src/sunfort/fdraw.c, C, 121 lines
vogl/src/sunfort/fdrivers.c, C, 429 lines
vogl/src/sunfort/fgetgp.c, C, 31 lines
vogl/src/sunfort/flines.c, C, 51 lines
vogl/src/sunfort/fmatrix.c, C, 97 lines
vogl/src/sunfort/fmove.c, C, 120 lines
vogl/src/sunfort/fobjects.c, C, 106 lines
vogl/src/sunfort/fpatches.c, C, 103 lines
vogl/src/sunfort/fpoints.c, C, 62 lines
vogl/src/sunfort/fpoly.c, C, 442 lines
vogl/src/sunfort/fpref.c, C, 41 lines
vogl/src/sunfort/fqueue.c, C, 103 lines
vogl/src/sunfort/frect.c, C, 55 lines
vogl/src/sunfort/ftext.c, C, 205 lines
vogl/src/sunfort/ftrans.c, C, 66 lines
vogl/src/sunfort/fvcalls.c, C, 177 lines
vogl/src/sunfort/fviewing.c, C, 78 lines
vogl/src/sunfort/fviewp.c, C, 70 lines
vogl/src/sunfort/fyobbarays.c, C, 11 lines
vogl/src/tensor.c, C, 85 lines
vogl/src/text.c, C, 273 lines
vogl/src/trans.c, C, 185 lines
vogl/src/valloc.c, C, 21 lines
vogl/src/vcalls.c, C, 296 lines
vogl/src/vgui_hook.c, C, 31 lines
vogl/src/viewing.c, C, 258 lines
vogl/src/viewp.c, C, 177 lines
vogl/src/vodevice.h, C/C++, 104 lines
vogl/src/vogl.h, C/C++, 694 lines
vogl/src/yobbarays.c, C, 29 lines
COPYING, License, 674 lines
README.md, Text, 48 lines

At the source: github.com/berndf/avg_q

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.

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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

{ "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", "author": [ { "family": "Schlump", "given": "Andrea" }, { "family": "Feige", "given": "Bernd" }, { "family": "Matthies", "given": "Swantje" }, { "family": "von Zedtwitz", "given": "Katharina" }, { "family": "Matteit", "given": "Isabelle" }, { "family": "Nickel", "given": "Kathrin" }, { "family": "Domschke", "given": "Katharina" }, { "family": "Reisert", "given": "Marco" }, { "family": "Lange", "given": "Thomas" }, { "family": "Heinrichs", "given": "Markus" }, { "family": "Endres", "given": "Dominique" }, { "family": "Tebartz van Elst", "given": "Ludger" }, { "family": "Maier", "given": "Simon" } ], "container-title-short": "Front Psychiatry", "volume": "17", "page": "1708563", "DOI": "10.3389/fpsyt.2026.1708563", "PMID": "41908118", "PMCID": "PMC13022906", "ISSN": "1664-0640", "publisher": "Frontiers Media SA", "URL": "https://doi.org/10.3389/fpsyt.2026.1708563", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 13 ] ] } }

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