Neuroimaging studies of vertigo and chronic dizziness have identified abnormalities across vestibular, visual, somatosensory, and cognitive-emotional networks, but conventional group-level analyses are limited in capturing individual heterogeneity and longitudinal change....
✦ The floor
Discussion
Signed responses from readers of the wire.
No actionable clinical change — this is a methodological review proposing AI-assisted fMRI frameworks for vestibular research; findings are not yet ready to influence routine clinical practice.
Establishing rigorous AI-driven neuroimaging methods for vestibular disorders like PPPD could accelerate the development of objective biomarkers, addressing a major diagnostic gap in dizziness medicine.
- 01Reviews AI-based phenotyping and longitudinal modeling using vestibular fMRI in PPPD and post-BPPV residual dizziness.
- 02PPPD (persistent postural-perceptual dizziness) is a chronic functional dizziness disorder with no current objective biomarker.
- 03Residual dizziness after BPPV treatment affects a significant subset of patients and lacks clear mechanistic explanation.
- 04The narrative review format limits evidence strength; no new experimental data are presented.
- 05Published in Frontiers in Neurology (DOI: 10.3389/fneur.2026.1915520).
AI-based phenotyping and longitudinal fMRI modeling can improve understanding of PPPD and residual dizziness after BPPV.
opinionpartially supported- PMID
- 42846342
- DOI
- 10.3389/fneur.2026.1915520.
- Journal
- Frontiers in Neurology
- Publication type
- review
- Evidence level
- 5
- Population
- Patients with PPPD and residual dizziness following BPPV treatment
- Intervention
- Data-driven vestibular fMRI with AI-based phenotyping and longitudinal modeling
Primary outcomes
Methodological frameworks for AI-based vestibular fMRI phenotyping; Longitudinal modeling approaches for dizziness disorder characterization