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Identification of depression subtypes and relevant brain regions using a data-driven approach
Tomoki Tokuda1, Junichiro Yoshimoto2,3, Yu Shimizu2
1Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Okinawa, 904-0495, Japan. tomoki.tokuda@oist.jp.
Scientific Reports
|September 22, 2018
Summary
Researchers identified three neurophysiological subtypes of depression using functional MRI and clinical data. These subtypes, linked to the right Angular Gyrus (AG) and trauma scores, may predict treatment response to Selective Serotonin-Reuptake Inhibitors (SSRIs).
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Depressive disorder is known to be heterogeneous.
- However, its neurophysiological subtypes and related neural substrates remain poorly understood.
Purpose of the Study:
- To identify neurophysiological subtypes of depression.
- To investigate the relationship between these subtypes, neural connectivity, clinical factors, and treatment outcomes.
Main Methods:
- Cluster analysis was performed on 134 subjects (67 with depression, 67 controls).
- A high-dimensional dataset including resting-state functional connectivity (fMRI), clinical questionnaire scores, and biomarkers was used.
- A novel multiple co-clustering method was applied.
Main Results:
- Three distinct subtypes of depression were identified.
- These subtypes are characterized by functional connectivity involving the right Angular Gyrus (AG) and default mode networks.
- Subtypes are also associated with Child Abuse Trauma Scale (CATS) scores and Selective Serotonin-Reuptake Inhibitor (SSRI) treatment outcomes.
Conclusions:
- Neurophysiological subtypes of depression can be identified using multimodal data.
- Right AG functional connectivity and CATS scores may serve as predictors for SSRI treatment effectiveness.
- This finding opens avenues for personalized depression treatment strategies.
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