Biologically Annotated Heterogeneity of Depression Through Neuroimaging Normative Modeling

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Depression is clinically heterogeneous, complicating group-level neuroimaging biomarker discovery.
• Individual symptom presentation in depression hinders personalized treatment strategies.
• Previous research focused on group-level neuroimaging, limiting personalized interventions.

Purpose of the Study:

• To review advances in data-driven neuroimaging for understanding depression heterogeneity.
• To discuss methods for identifying precision neuromarkers in depression.
• To highlight future directions for personalized depression diagnostics and therapeutics.

Main Methods:

• Systematic review of data-driven neuroimaging analyses in depression.
• Exploration of dimensional and overlapping strategies for heterogeneity.
• Discussion of normative modeling for individual-specific abnormal patterns.
• Analysis of multiscale organizational associations in depression.

Main Results:

• Data-driven approaches are advancing the understanding of depression heterogeneity.
• Integrative multi-neuroimaging strategies enable precision neuromarker investigation.
• Individual-specific abnormal patterns can be identified using normative modeling.
• Advances facilitate a move towards personalized interventions for depression.

Conclusions:

• Understanding depression heterogeneity through advanced neuroimaging is critical.
• Precision neuromarkers hold promise for accurate diagnosis and personalized treatment.
• Future research should focus on integrating multi-neuroimaging data for individual patient profiles.