Abstract
Major depressive disorder (MDD) is a highly prevalent neuropsychiatric disorder characterized by depressed mood, feelings of sadness, loss of interest, and reduced pleasure related to daily activities. The clinical etiology of depression has been extensively studied, with research indicating biological, social, and psychological factors related to onset of depressive symptoms. Despite increased knowledge related to MDD, there is no tangible biomarker developed for MDD. Neuroimaging modalities such as single photon emission computed tomography (SPECT) have been utilized to characterize regional cerebral perfusion (rCBF). Functional dysconnectivity in depressed patients have been examined, with depressed individuals showing elevated depression scores and decreased rCBF in cognition and executive functioning networks. While SPECT can be utilized to monitor rCBF changes with respect to symptom severity, it alone cannot be utilized to develop a potent biomarker. Advanced multivariate methods such as independent component analysis (ICA) have been used to visualize disconnected functional patterns across disorders including depression and schizophrenia. Given no current SPECT studies examine transdiagnostic clinical profiles, the current study aims to bridge this gap. We utilized the 68 NeuroMark SPECT template across six patient groups. Factor scores investigating three key symptoms of depression: worry/rumination, moodiness, and social disinterest, and measured the loading parameter strength (i.e. component expression for each NeuroMark domain/subdomain) across the 68 components were examined. We identified significant relationships between symptoms and frontal, triple network, sensorimotor, and visual components across the three symptom profiles. Future studies should examine these trends across larger sample sizes, and increased clinical samples.