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Resting-state connectivity biomarkers define neurophysiological subtypes of depression

An Erratum to this article was published on 07 February 2017

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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.

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Figure 1: Canonical correlation analysis (CCA) and hierarchical clustering define four connectivity-based biotypes of depression.
Figure 2: Connectivity biomarkers define depression biotypes with distinct clinical profiles.
Figure 3: Functional connectivity biomarkers for diagnosing neurophysiological biotypes of depression.
Figure 4: Connectivity biomarkers predict differential antidepressant response to rTMS.
Figure 5: Connectivity biomarkers of depression biotypes transcend diagnostic boundaries.

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  • 19 December 2016

    In the version of this article initially published online, the abstract contained two typos reading, “Like to other neuropsychiatric disorders,…” and “transcranial-magnetic-stimulation therapy…” . These errors have been corrected in the print, PDF and HTML versions of this article.

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Acknowledgements

We wish to thank all investigators who volunteered to share MRI data via the 1000 Functional Connectomes Project (http://fcon_1000.projects.nitrc.org/index.html), which was supported by grants from the NIMH, NIDA, Autism Speaks, NINDS and HHMI. Principal investigators from sites that provided data used here include: R.L. Buckner (Harvard–MGH), F.X. Castellanos (NYU), A.C. Evans (ICBM), B. Leventhal (Nathan Kline Institute), S.J. Li (Medical College of Wisconsin), M.J. Lowe (Cleveland Clinic), H.M. Mayberg (Emory), M.P. Milham (Nathan Kline Institute), V. Riedl (Munchen), C. Sorg (Munchen), A. Villringer (Leipzig) and Y.F. Zang (Beijing Normal University). We also thank the following investigators at the University of New Mexico who provided public access to MRI data from patients diagnosed with schizophrenia through the Center of Biomedical Research Excellence in Brain Function and Mental Illness (COBRE): C. Aine, V. Calhoun, J. Canive, F. Hanlon, R. Jung, K. Kiehl, A. Mayer, N. Perrone-Bizzozero, J. Stephen and C. Tesche, who were supported by NIH COBRE grant 1P20RR021938-01A2. We also thank D. Fair (OHSU) and J. Power (NIMH, Weill Cornell) for providing comments on the data analysis, as well as members of the Liston Lab and Sackler Institute, for their helpful comments on the manuscript. H.S.M. was supported by a grant from the NIMH (P50 MH077083). C.L. was supported by grants from the Dana Foundation, Hartwell Foundation, International Mental Health Research Organization, Klingenstein-Simons Foundations, NARSAD and NIMH (R00 MH097822, R01 MH109685).

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Authors

Contributions

J.D., K.D., F.M., D.J.O., A.E., A.F.S., K.S., J.K., H.S.M., F.M.G., G.S.A., M.D.F., A.P.-L., H.U.V., B.J.C., M.J.D. and C.L. collected the data. L.G. consulted on all statistical analyses. C.L. designed the protocol for analyzing data pooled across multiple sites and identifying clusters. A.T.D., R.F. and C.L. designed and implemented the preprocessing pipeline and methods for validating clusters and optimizing classifiers, and C.L. developed and implemented the method for clustering and classification in a low-dimensional connectivity-feature space by using canonical correlation analysis (Figs. 1,2,3). J.D., K.D. and F.M. collected the TMS data. C.L. analyzed the TMS response data and other clinical data (Figs. 2 and 4) and tested the subtype classifiers on subjects with other diagnoses (Fig. 5). A.T.D., Y.M. and C.L. implemented the permutation testing. A.T.D., B.Z. and C.L. created the figures and wrote the manuscript. All authors discussed the results and conclusions and edited the manuscript.

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Correspondence to Conor Liston.

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Competing interests

C.L. and A.T.D. have no competing interests. A.E. has received a research grant from Brain Resource. A.F.S. has served as a consultant to BrainCells, CeNeRx, BioPharma, CNS Response, Corcept Therapeutics, Eli Lilly, Forest Laboratories, GlaxoSmithKline, InnoPharma, Lundbeck, Merck, Neuronetics, Novartis, Pathway Diagnostics, Pfizer, PharmaNeuroBoost, Quintiles, Sanofi, Synosis, Takeda, and Xytis; has equity in Amnestix, BrainCells, CeNeRx, Corcept Therapeutics, Forest Laboratories, Merck, Neurocrine Biosciences, Pfizer, PharmaNeuroBoost, Somaxon Pharmaceuticals and Synosis; has pharmacogenetic-use patents on the prediction of antidepressant response; and has received speaking fees from GlaxoSmithKline and Roche. G.S.A. has received grant support from Forest Pharmaceuticals; has consulted for Hoffman–LaRoche, Lilly, Pfizer and Otsuka; and has served at the speakers′ bureaus of AstraZeneca, Avanir, Forest, Merck, Novartis and Sunovion. M.J.D. has received research grants from Neuronetics. All other authors report no biomedical financial interests or potential conflicts of interest.

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Drysdale, A., Grosenick, L., Downar, J. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23, 28–38 (2017). https://doi.org/10.1038/nm.4246

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