Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression.
Luigi A Maglanoc1, Nils Inge Landrø2, Rune Jonassen3
1Clinical Neuroscience Research Group, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Summary
This study identified five distinct subgroups of depression and anxiety symptoms using brain imaging. These subgroups show unique patterns of brain connectivity, supporting personalized treatment approaches.
Area of Science:
- Neuroscience
- Psychiatry
- Data Science
Background:
- Depression and anxiety disorders exhibit significant interindividual variability in symptoms.
- A dimensional, symptom-based approach is crucial for refining diagnostic characterization and identifying biomarkers.
- Resting-state functional magnetic resonance imaging (rs-fMRI) offers insights into brain functional connectivity.
Purpose of the Study:
- To characterize depressive and anxiety disorders using a symptom-based, dimensional approach.
- To identify distinct subgroups of individuals based on symptom profiles.
- To investigate the brain functional connectivity correlates of these symptom-based subgroups.
Main Methods:
- Utilized Beck Depression and Beck Anxiety Inventories to assess symptoms in 1084 individuals.
- Employed high-dimensional data clustering to form subgroups based on symptom profiles.
- Compared static and dynamic functional connectivity in a subset of 252 individuals.
Main Results:
- Identified five distinct subgroups with varying symptom severity and patterns, cutting across diagnostic boundaries.
- Observed differential static functional connectivity patterns, particularly within a frontotemporal network, among subgroups.
- Found no significant associations between subgroups and clinical sum scores, dynamic functional connectivity, or global connectivity.
Conclusions:
- Subtyping depression and anxiety disorders based on dimensional symptom constellations is supported by distinct brain connectivity patterns.
- These findings advocate for personalized treatment strategies informed by neurobiological subtypes.
- Distinct static functional connectivity patterns provide a potential basis for developing robust biomarkers.
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