Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes

Area of Science:

• Neuroscience
• Psychiatry
• Computational Biology

Background:

• Bipolar disorder (BD) is characterized by mood fluctuations.
• Identifying biomarkers for mood states in BD is crucial for effective treatment.

Purpose of the Study:

• To identify brain functional connectomes predicting depressed and elevated mood in individuals with bipolar disorder (BD).
• To utilize Connectome-based Predictive Modeling (CPM) for predicting mood symptomatology.

Main Methods:

• Functional magnetic resonance imaging (fMRI) data from 81 adults with BD were analyzed.
• Connectome-based Predictive Modeling (CPM) with cross-validation identified predictive functional connectomes.
• The predictive models were validated in an independent sample of 43 adults with BD.

Main Results:

• CPM successfully predicted the severity of both depressed (r = 0.23, p=0.031) and elevated (r = 0.27, p=0.01) mood.
• Specific functional connectomes involving prefrontal cortex, motor areas, and visual association areas were associated with mood severity.
• These identified networks demonstrated predictive accuracy in an independent sample (r >= 0.45, p=0.002).

Conclusions:

• Distributed functional connectomes predict mood severity in bipolar disorder.
• Networks involved in emotional, cognitive, and perceptual functions are linked to mood states.
• These findings may guide the development of targeted therapies for mood symptoms in BD.