LAUSR

Depression is a heterogeneous clinical syndrome that is diagnosed when a patient reports at least five of nine symptoms. This allows for several hundred unique combinations of changes in mood, appetite, sleep, energy, cognition and motor activity. Such remarkable heterogeneity reflects the consensus view that there are multiple forms of depression, but their neurobiological basis remains poorly understood1,2. So far, most efforts to characterize depression subtypes and develop diagnostic biomarkers have begun by identifying clusters of symptoms that tend to co-occur, and by then testing for neurophysiological correlates. These pioneering studies have defined atypical, melancholic, seasonal and agitated subtypes of depression associated with characteristic changes in neuroendocrine activity, circadian rhythms and other potential biomarkers3–5. Still, the association between clinical subtypes and their biological substrates is inconsistent and variable at the individual level, and unlike diagnostic biomarkers in other areas of medicine, they have not yet proven useful for differentiating individual patients from healthy controls or for reliably predicting treatment response at the individual level. An alternative to subtyping patients on the basis of co-occurring clinical symptoms is to identify neurophysiological subtypes, or biotypes, by clustering subjects according to shared signatures of brain dysfunction6. This type of approach has already begun to yield insights into how differing biological mechanisms may give rise to overlapping, heterogeneous clinical presentations of psychotic disorders6,7. Neuroimaging biomarkers of abnormal brain function have proven utility in the assessment of pain8 and have also shown promise for depression, for both the prediction of treatment response9–13 and treatment selection14. Resting-state fMRI (rsfMRI) is an especially useful modality because it can be used easily in diverse patient populations to quantify functional network connectivity in terms of correlated, spontaneous MR signal fluctuations. Depression is associated with dysfunction and abnormal functional connectivity in frontostriatal and limbic brain networks15–20, in accordance with morphological and synaptic changes in chronic stress models in rodents21–24. These studies raise the intriguing possibility that fMRI measures of connectivity could be leveraged to identify