Resting-state connectivity biomarkers define neurophysiological subtypes of depression

Andrew T. Drysdale, Logan Grosenick, Jonathan Downar, Katharine Dunlop, Farrokh Mansouri, Yue Meng, Robert N. Fetcho, Benjamin Zebley, Desmond J. Oathes, Amit Etkin, Alan F. Schatzberg, Keith Sudheimer, Jennifer Keller, Helen S. Mayberg, Faith M. Gunning, George S. Alexopoulos, Michael D. Fox, Alvaro Pascual-Leone, Henning U. Voss, B. J. CaseyMarc J. Dubin, Conor Liston

Research output: Contribution to journalArticlepeer-review

1039 Scopus citations

Abstract

Biomarkers have transformed modern medicine but remain largely elusive in psychiatry, partly because there is a weak correspondence between diagnostic labels and their neurobiological substrates. Like other neuropsychiatric disorders, depression is not a unitary disease, but rather a heterogeneous syndrome that encompasses varied, co-occurring symptoms and divergent responses to treatment. By using functional magnetic resonance imaging (fMRI) in a large multisite sample (n = 1, 188), we show here that patients with depression can be subdivided into four neurophysiological subtypes ('biotypes') defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. Clustering patients on this basis enabled the development of diagnostic classifiers (biomarkers) with high (82-93%) sensitivity and specificity for depression subtypes in multisite validation (n = 711) and out-of-sample replication (n = 477) data sets. These biotypes cannot be differentiated solely on the basis of clinical features, but they are associated with differing clinical-symptom profiles. They also predict responsiveness to transcranial magnetic stimulation therapy (n = 154). Our results define novel subtypes of depression that transcend current diagnostic boundaries and may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.

Original languageEnglish
Pages (from-to)28-38
Number of pages11
JournalNature Medicine
Volume23
Issue number1
DOIs
StatePublished - Jan 2017
Externally publishedYes

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