Tinnitus, the perception of sound without an external source, affects many individuals, yet its impact on the brains 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....
No actionable change for current clinical practice; these neuroimaging findings advance understanding of tinnitus brain mechanisms but are not yet translatable to diagnosis or treatment decisions.
Identifying individual-level brain network signatures of tinnitus could eventually inform personalized diagnostic or therapeutic targeting, moving beyond one-size-fits-all tinnitus management.
- 01Holistic graph learning applied to resting-state MEG data to profile tinnitus brain networks.
- 02Study identifies both shared (group-level) and individual-specific functional network signatures.
- 03Published in an IEEE engineering/biomedical journal, reflecting cross-disciplinary tinnitus research.
- 04Method could improve understanding of why tinnitus presentations vary so widely between patients.
- 05Results are exploratory; clinical translation will require validation in larger, diverse cohorts.
Resting-state MEG combined with holistic graph learning can identify shared and individual brain network signatures associated with tinnitus.
studypartially supported- PMID
- 42328494
- DOI
- 10.1109/OJEMB.2026.3690604.
- Journal
- IEEE Open Journal of Engineering in Medicine and Biology
- Publication type
- research_article
- Evidence level
- 4
- Population
- Adults with tinnitus
- Intervention
- Holistic graph learning analysis of resting-state MEG brain data
Primary outcomes
Shared functional brain network signatures of tinnitus; Individual-specific resting-state MEG network signatures of tinnitus