Resting-state connectivity biomarkers define neurophysiological subtypes of depression

By Andrew T. Drysdale, Logan Grosenick, J. Downar, K. Dunlop, Farrokh Mansouri, Y. Meng, R. Fetcho, Benjamin Zebley, D. Oathes, Amit Etkin and 12 more. Nature Medicine, 2016

1,971 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/247ae085e9d6fc03305560777a9dcb9cce6b4d1b

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Citations in the corpus

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, background): “Fourth, if FD at any point in time exceeded 0·3 mm, then that time point was scrubbed (Drysdale et al., 2017; Power et al., 2015).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “As mentioned above, patterns of FC and TVFC are sensitive to individual differences in health and disease, and emerging work suggests they may have powerful utility as clinical biomarkers (e.g., as predictors of treatment response; Drysdale et al., 2017; Etkin et al., 2019; Reggente et al., 2018).”
The promise of machine learning in predicting treatment outcomes in psychiatry (2021, methodology): “Drysdale et al 44 used clustering to identify four “subtypes”, or groups, amongst 1,188 depressed patients based on patterns of dysfunctional connectivity in lim bic and frontostriatal networks.”
Major depressive disorder: hypothesis, mechanism, prevention and treatment (2024, methodology): “…a signi ﬁ cant role in MDD research and is a superior technique for researching MDD pathogenesis and identifying neuroimaging markers for MDD. 271 Thus, indicators such as amplitude of low-frequency ﬂ uctuation (ALFF), fractional amplitude of low-frequency ﬂ uctuation (fALFF), regional…”
Reduced default mode network functional connectivity in patients with recurrent major depressive disorder (2019, neutral): “Future independent efforts could include generating neural biotypes of MDD (37), performing dynamic FC analysis, and data mining with machine learning algorithms.”
Mental Activity as the Bridge between Neural Biomarkers and Symptoms of Psychiatric Illness (2022, methodology): “In our exper-iment, we ran a dimension-discovery analysis on the individual items of these assessments using prior research for validation: independent groups have found separable symptom dimensions within the HAM-D and the BDI-II.”