Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study

Area of Science:

• Neuroimaging
• Neuropsychiatry
• Brain Anatomy

Background:

• Differentiating bipolar disorder (BD) and major depressive disorder (MDD) is challenging due to overlapping depressive symptoms.
• Neuroanatomical differences may offer objective markers for diagnosis.

Purpose of the Study:

• To investigate distinct cortical thickness and surface area variations between BD and MDD patients.
• To assess the utility of neuroanatomical features in discriminating between BD and MDD using machine learning.

Main Methods:

• Vertex-wise cortical imaging techniques were used to analyze cortical thickness and surface area in 16 BD patients, 19 MDD patients, and 29 healthy controls.
• Support vector machine (SVM) algorithm was employed to classify patients based on neuroanatomical features.

Main Results:

• Bipolar disorder patients exhibited significantly larger cortical surface area in specific left and right brain regions compared to major depressive disorder patients.
• Support vector machine analysis achieved 74.3% accuracy in discriminating between BD and MDD.
• Distinct patterns of cortical volume differences were observed between patients and healthy controls.

Conclusions:

• Significant differences in cortical thickness and surface area highlight potentially distinct pathophysiological mechanisms in BD and MDD.
• Neuroanatomical features show promise for developing objective classification tools to aid in diagnosing BD and MDD.