Distinct and Shared Molecular Mechanisms Underlie Morphological-Functional Overcoupling and Undercoupling in Major...

Area of Science:

• Neuroimaging
• Machine Learning
• Molecular Psychiatry

Background:

• Major depressive disorder (MDD) presents heterogeneous brain alterations, with underlying neurobiological mechanisms unclear.
• Understanding MDD heterogeneity is crucial for targeted treatments.
• Morphological-functional coupling (MFC) offers a potential framework for classifying MDD subtypes.

Purpose of the Study:

• To identify and characterize MDD subtypes using MFC.
• To investigate the clinical and molecular correlates of these subtypes.
• To explore treatment response in relation to identified subtypes.

Main Methods:

• Employed semi-supervised machine learning on multi-center neuroimaging data (N=1604 discovery, N=322 validation).
• Identified subtypes based on MFC patterns.
• Linked subtypes to gene expression, neurotransmitter maps, cell densities, and clinical symptoms. Assessed treatment response in a longitudinal cohort (N=33).

Main Results:

• Identified two replicable MDD subtypes: overcoupling (Subtype I) and undercoupling (Subtype II).
• Subtype I showed elevated MFC in association cortices, linked to synaptic pathways and severe symptoms. Subtype II showed reduced MFC in primary cortices, linked to cell cycle pathways.
• Both subtypes exhibited glial gene enrichment and dysregulation in serotonergic/GABAergic systems. Undercoupling subtype showed better escitalopram response.

Conclusions:

• MDD subtypes identified via MFC correlate with distinct glial and synaptic molecular signatures.
• This MFC-based classification links macroscale brain organization to microscale features.
• Provides a neurobiologically informed framework for understanding MDD heterogeneity.