Neuroimaging studies of vestibular migraine (VM) have revealed abnormal functional connectivity in the central vestibular system. However, it remains to be determined whether effective connectivity (EC) of the vestibular cortex is specifically disrupted in VM and whether this brain measure can aid in the differential diagnosis of VM patients.
No actionable change — findings are preliminary and require validation in larger, independent cohorts before MRI-based connectivity signatures can be used clinically to diagnose vestibular migraine.
If validated, AI-assisted MRI connectivity mapping could provide an objective biomarker to distinguish vestibular migraine from other dizziness disorders, a long-standing diagnostic challenge.
- 01MRI and machine learning identified distinct vestibular cortex connectivity patterns in vestibular migraine patients vs. controls.
- 02Effective connectivity (directional brain communication) was the primary measure characterised.
- 03Study advances the search for objective neuroimaging biomarkers for vestibular migraine.
- 04Machine learning classification potential noted, but clinical translation is not yet established.
- 05Findings may eventually reduce reliance on symptom-based diagnosis alone.
MRI-based effective connectivity signatures can differentiate vestibular migraine patients from healthy controls.
studypartially supportedMachine learning can characterise vestibular cortex connectivity patterns specific to vestibular migraine.
studypartially supported- PMID
- 42337425
- DOI
- 10.1186/s10194-026-02430-y.
- Journal
- The Journal of Headache and Pain
- Publication type
- research_article
- Evidence level
- 4
- Population
- Vestibular migraine patients and healthy controls
- Intervention
- MRI-based effective connectivity analysis combined with machine learning classification
- Comparator
- Healthy controls
Primary outcomes
Characterisation of vestibular cortex effective connectivity signatures; Differentiation accuracy of vestibular migraine patients from controls