Resting-State Networks in Depression vs Social Anxiety

Speaker: Matthew P. Gunn, PhD, UIC Department of Psychiatry

Overview
Major depressive disorder and social anxiety disorder often overlap clinically, but it is unclear whether they share the same brain network signatures. In this talk, Gunn will present resting-state fMRI findings from 150 adults (major depressive disorder, social anxiety disorder and healthy controls) using a harmonized group information–guided ICA pipeline.

We show that:

• Major depressive disorder is characterized by reduced slow-frequency connectivity in the salience network and superior temporal gyrus.

• Social anxiety disorder shows enhanced fusiform/parahippocampal connectivity linked to higher social anxiety and reduced STG connectivity.

• Machine-learning sensitivity analyses confirm that these frequency-specific features reliably distinguish social anxiety disorder from major depressive disorder with high accuracy.

Learning objectives
By the end of this event, participants will be able to:

1. Describe how intrinsic salience, visual–limbic and superior temporal networks differ between major depressive disorder and social anxiety disorder.

2. Explain why frequency-resolved resting-state measures may provide more precise biomarkers for internalizing disorders.

3. Discuss how multivariate and machine-learning approaches can validate candidate biomarkers for precision psychiatry.

Intended audience
Faculty, trainees and students in psychiatry, psychology, neuroscience and related fields; clinicians interested in translational neuroimaging and biomarker development.