Journal article · Vestibular← The news desk

✦ The Dispatch

Data-driven vestibular fMRI in PPPD and residual dizziness after BPPV: a narrative methodological review of AI-based phenotyping and longitudinal modeling

A dispatch from PubMed — filed

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....

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✦ The floor

Discussion

Signed responses from readers of the wire.

✦ Clinical Takeaway ✦

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.

✦ Why It Matters ✦

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.

✦ Key Points ✦
  1. 01Reviews AI-based phenotyping and longitudinal modeling using vestibular fMRI in PPPD and post-BPPV residual dizziness.
  2. 02PPPD (persistent postural-perceptual dizziness) is a chronic functional dizziness disorder with no current objective biomarker.
  3. 03Residual dizziness after BPPV treatment affects a significant subset of patients and lacks clear mechanistic explanation.
  4. 04The narrative review format limits evidence strength; no new experimental data are presented.
  5. 05Published in Frontiers in Neurology (DOI: 10.3389/fneur.2026.1915520).
✦ Claims & Evidence ✦

AI-based phenotyping and longitudinal fMRI modeling can improve understanding of PPPD and residual dizziness after BPPV.

opinionpartially supported
✦ Research metadata ✦
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

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