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Resting-state connectivity biomarkers define neurophysiological subtypes of depression.

TitleResting-state connectivity biomarkers define neurophysiological subtypes of depression.
Publication TypeJournal Article
Year of Publication2017
AuthorsDrysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C
JournalNat Med
Volume23
Issue1
Pagination28-38
Date Published2017 Jan
ISSN1546-170X
KeywordsAdult, Brain, Cluster Analysis, Depressive Disorder, Major, Female, Frontal Lobe, Functional Neuroimaging, Humans, Limbic System, Magnetic Resonance Imaging, Male, Neural Pathways, Ventral Striatum
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.

DOI10.1038/nm.4246
Alternate JournalNat. Med.
PubMed ID27918562
PubMed Central IDPMC5624035
Grant ListR01 MH109685 / MH / NIMH NIH HHS / United States
R21 MH099196 / MH / NIMH NIH HHS / United States
R00 MH097822 / MH / NIMH NIH HHS / United States
P20 RR021938 / RR / NCRR NIH HHS / United States
P50 MH077083 / MH / NIMH NIH HHS / United States