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Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning

Shabestari, Payam S. ; Behjat, Harry H. LU ; Ville, Dimitri Van De ; Cederroth, Christopher R. ; Edvall, Niklas K. ; Naas, Adrian ; Kleinjung, Tobias and Neff, Patrick (2026) In IEEE Open Journal of Engineering in Medicine and Biology 7. p.203-213
Abstract

Tinnitus, the perception of sound without an external source, affects many individuals, yet its impact on the brain's functional connectome remains underexplored. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index, and coherence, rely on pairwise comparisons between activity of macro-scale brain regions, limiting holistic characterization. We used an approach that estimates the entire connectivity structure by analyzing all time-courses simultaneously, robust even for short recordings and suitable for real-time applications. Using resting-state MEG from tinnitus patients and controls, learned connectomes outperformed correlation-based ones in fingerprinting individuals across test/retest.... (More)

Tinnitus, the perception of sound without an external source, affects many individuals, yet its impact on the brain's functional connectome remains underexplored. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index, and coherence, rely on pairwise comparisons between activity of macro-scale brain regions, limiting holistic characterization. We used an approach that estimates the entire connectivity structure by analyzing all time-courses simultaneously, robust even for short recordings and suitable for real-time applications. Using resting-state MEG from tinnitus patients and controls, learned connectomes outperformed correlation-based ones in fingerprinting individuals across test/retest. Group analyses revealed altered FC across multiple frequency bands, impacting default mode, auditory, visual, and salience networks, indicating large-scale reorganization. Tinnitus exhibited highly individualized whole-brain FC profiles, highlighting the importance of individual variability and paving the way for personalized models to optimize patient-specific interventions.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain functional connectivity, fingerprinting, graph learning, magnetoencephalography (MEG), tinnitus
in
IEEE Open Journal of Engineering in Medicine and Biology
volume
7
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:42328494
  • scopus:105038682387
ISSN
2644-1276
DOI
10.1109/OJEMB.2026.3690604
language
English
LU publication?
yes
id
8c528246-d58e-4b38-a544-2efbb06e4425
date added to LUP
2026-08-17 09:45:15
date last changed
2026-08-31 10:40:23
@article{8c528246-d58e-4b38-a544-2efbb06e4425,
  abstract     = {{<p>Tinnitus, the perception of sound without an external source, affects many individuals, yet its impact on the brain's functional connectome remains underexplored. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index, and coherence, rely on pairwise comparisons between activity of macro-scale brain regions, limiting holistic characterization. We used an approach that estimates the entire connectivity structure by analyzing all time-courses simultaneously, robust even for short recordings and suitable for real-time applications. Using resting-state MEG from tinnitus patients and controls, learned connectomes outperformed correlation-based ones in fingerprinting individuals across test/retest. Group analyses revealed altered FC across multiple frequency bands, impacting default mode, auditory, visual, and salience networks, indicating large-scale reorganization. Tinnitus exhibited highly individualized whole-brain FC profiles, highlighting the importance of individual variability and paving the way for personalized models to optimize patient-specific interventions.</p>}},
  author       = {{Shabestari, Payam S. and Behjat, Harry H. and Ville, Dimitri Van De and Cederroth, Christopher R. and Edvall, Niklas K. and Naas, Adrian and Kleinjung, Tobias and Neff, Patrick}},
  issn         = {{2644-1276}},
  keywords     = {{Brain functional connectivity; fingerprinting; graph learning; magnetoencephalography (MEG); tinnitus}},
  language     = {{eng}},
  pages        = {{203--213}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Open Journal of Engineering in Medicine and Biology}},
  title        = {{Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning}},
  url          = {{http://dx.doi.org/10.1109/OJEMB.2026.3690604}},
  doi          = {{10.1109/OJEMB.2026.3690604}},
  volume       = {{7}},
  year         = {{2026}},
}