Bipolar disorder in the balance.

Area of Science:

• Neuroscience
• Psychiatry
• Biomedical Engineering

Background:

• Bipolar disorder (BD) lacks objective diagnostic and monitoring markers.
• Current diagnostic methods rely on subjective assessments, necessitating novel objective approaches.

Purpose of the Study:

• To explore Electrovestibulography (EVestG) for identifying neurophysiological markers in bipolar disorder.
• To assess EVestG's utility in differentiating between bipolar disorder patients and healthy controls, and in measuring symptom severity.

Main Methods:

• EVestG recorded electrical activity from the outer ear canal in response to a translation stimulus.
• Sensory vestibulo-acoustic features were extracted from EVestG data.
• Machine learning classifiers were used to distinguish between patient groups and controls.

Main Results:

• Five of six extracted EVestG features correlated significantly with the Montgomery-Åsberg Depression Rating Scale (MADRS).
• Classification accuracy for distinguishing controls from BD patients ranged from 75-79%.
• Accurate differentiation was achieved between symptomatic BD (BD-S), reduced symptomatic BD (BD-R), and controls, suggesting distinct pathophysiological differences.

Conclusions:

• EVestG offers a promising tool for objective diagnosis and severity assessment in bipolar disorder.
• Neurophysiological features derived from EVestG can discriminate between different states of bipolar disorder.
• Further research is needed to fully elucidate the impact of medication on EVestG findings.