For over a century, psychiatrists have recognized that distinct disease processes can converge to produce superficially similar clinical syndromes with overlapping symptoms, but the underlying mechanisms remain poorly understood. Existing diagnostic systems have improved the reproducibility of psychiatric diagnoses but there is a weak correspondence between diagnostic labels and their neurobiological substrates. This is especially true for depression, a heterogeneous neuropsychiatric syndrome that has been linked to dysfunction and abnormal connectivity in frontostriatal and limbic brain networks. The methods and systems described herein enable the accurate diagnosis of novel biotypes of depression that transcend current diagnostic boundaries and may be useful for identifying individuals who are most likely to benefit from antidepressant treatment. Functional magnetic resonance imaging is used to characterize the architecture of functional connectivity across the whole brain to show that patients with depression can be subdivided into four neurophysiological biotypes based solely on unique patterns of abnormal connectivity in resting state brain networks. Clustering subjects on this basis reduces diagnostic heterogeneity, enabling the development of depression biotype classifiers for diagnosing biotypes of depression in individual patients. These biotypes also predict differing responses to antidepressant treatment, and abnormal connectivity patterns can be used to track changes in depression severity over time.
