Identification of depression subtypes and relevant brain regions using a data-driven approach

It is well known that depressive disorder is heterogeneous, yet little is known about its neurophysiological subtypes. In the present study, we identified neurophysiological subtypes of depression related to specific neural substrates. We performed cluster analysis for 134 subjects (67 depressive su...

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Bibliographic Details
Published in:Scientific reports 2018-09, Vol.8 (1), p.14082-13, Article 14082
Main Authors: Tokuda, Tomoki, Yoshimoto, Junichiro, Shimizu, Yu, Okada, Go, Takamura, Masahiro, Okamoto, Yasumasa, Yamawaki, Shigeto, Doya, Kenji
Format: Article
Language:English
Subjects:
631/378/1689/1414
631/378/3920
Adult
Antidepressants
Antidepressive Agents - therapeutic use
Brain - diagnostic imaging
Child abuse & neglect
Databases, Factual
Depressive Disorder - diagnostic imaging
Depressive Disorder - drug therapy
Female
Functional magnetic resonance imaging
Humanities and Social Sciences
Humans
Magnetic Resonance Imaging
Male
Mental depression
Middle Aged
multidisciplinary
Nerve Net - diagnostic imaging
Neural networks
Science
Science (multidisciplinary)
Serotonin uptake inhibitors
Severity of Illness Index
Trauma
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Summary:It is well known that depressive disorder is heterogeneous, yet little is known about its neurophysiological subtypes. In the present study, we identified neurophysiological subtypes of depression related to specific neural substrates. We performed cluster analysis for 134 subjects (67 depressive subjects and 67 controls) using a high-dimensional dataset consisting of resting state functional connectivity measured by functional MRI, clinical questionnaire scores, and various biomarkers. Applying a newly developed, multiple co-clustering method to this dataset, we identified three subtypes of depression that are characterized by functional connectivity between the right Angular Gyrus (AG) and other brain areas in default mode networks, and Child Abuse Trauma Scale (CATS) scores. These subtypes are also related to Selective Serotonin-Reuptake Inhibitor (SSRI) treatment outcomes, which implies that we may be able to predict effectiveness of treatment based on AG-related functional connectivity and CATS.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-32521-z