Identification of depression subtypes and relevant brain regions using a data-driven approach
Open accessAbstract
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.
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Several studies have explored the association between onset age and HDRS-17 score in patients with MDD, but the results were inconsistent.
“Several studies have explored the association between the onset age and HDRS-17 score in patient with MDD, but the results were inconsistent [71-74].”
From Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome (2023) · cites this paper as [73]
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References 48 · Cited by 136
Research works printed but not linked: 2
- [33]Tokuda, T. & Yoshimoto, J. Multiple co-clustering based on nonparametric mixture models (Github), https://github.com/tomokitokuda/Multiple-Co-clustering (2018). PMC5648298 29049392
- [37]Cohen, J. Statistical power analysis for the behavioral sciences (second edition) (Lawrence Earlbaum Associates, 1988).
- Authors
- Tokuda, T., Yoshimoto, J., Shimizu, Y., Okada, G., Takamura, M., Okamoto, Y., Yamawaki, S., & Doya, K.
- Affiliations
- Okinawa Institute of Science and Technology Graduate University (JP)
- Hiroshima University (JP)
- Corresponding author
- Tokuda, Tomoki
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-09-20
- Volume
- 8
- Issue
- 1
- Pages
- 14082-14082
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6148252
- OpenAlex
- W2891129629
- Cited by
- 136
- References
- 48
- Citations by year
- 2026: 16 · 2025: 13 · 2024: 21 · 2023: 13 · 2022: 18 · 2021: 24 · 2020: 18 · 2019: 12 · 2018: 1
- FWCI
- 8.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- NAIST Digital Library (Nara Institute of Science and Technology) (submittedVersion, cc-by-nc-nd)
- Okinawa Institute of Science and Technology Graduate University (Okinawa Institute of Science and Technology Graduate University) (submittedVersion, cc-by-nc-nd)
- Funders
- Japan Agency for Medical Research and Development
- Okinawa Institute of Science and Technology Graduate University
- Japan Society for the Promotion of Science (16H06563)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- depression subtypes, resting-state functional connectivity, angular gyrus, default mode network, treatment response prediction, biclustering, biomarkers
- MeSH terms
- Adult, Antidepressive Agents, Brain, Depressive Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Severity of Illness Index, Databases, Factual
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