Recent years have witnessed a paradigm shift in the investigation of the pathophysiology of major depressive disorder (MDD), as theoretical models increasingly take a systems-neuroscience view of the disorder as being rooted in imbalanced interactions within and between large-scale neural networks. Indeed, the NIH Blueprint for Neuroscience Research (://www.neuroscienceblueprint.nih.gov/connectome/) emphasizes the study of anatomical and functional connectivity as a critical frontier in psychiatric research. However, significant gaps exist in this research, particularly in (1) our understanding of temporal dimensions of network dysfunction in MDD, and (2) the relationship between network dysfunction and individual differences in cognitive-affective deficits that characterize the disorder. An F32 postdoctoral fellowship would allow the candidate to investigate these novel dimensions of network functioning in MDD. Specifically, activity and connectivity changes between the frontoparietal control network (FPN) involved in executive control, the default mode network (DMN) involved in internal mentation, and the dorsal attention network (DAN) that supports attention to the external world, may be critically related to MDD and to the ruminative and negatively- biased cognitive style that is a hallmark of the disorder. In light of previous findings, the proposed study aims to test the predictions that adults with MDD will exhibit (1) reduced frequency of switching between DMN and DAN (spontaneous changes in activity within each network); (2a) hyperconnectivity within DMN and between FPN-DMN, and hypoconnectivity within DAN and between FPN-DAN; and (2b) excessive stationarity (stability in the magnitude and directionality of correlated activity between brain systems) between FPN-DMN and FPN-DAN, representing reduced flexibility in the regulatory relationships between these systems. In addition, the proposed study aims to explicitly test associations between network dysfunction and putative mechanisms of rumination and negative cognitive bias, predicting that (3a) reduced network switching and excessive stationarity will be associated with poorer executive functioning with emotional information; and that (3b) such executive deficits will be associated with a negatively biased and ruminative thinking style. With the mentorship of Dr. Diego Pizzagalli (primary mentor), a leader in the field of clinical neuroscience of depression, and the consultation of Dr. Jutta Joormann (expert in cognitive-affective deficits in depression) and Dr. Blaise Frederick (head magnetic resonance imaging (MRI) physicist at McLean Hospital and MRI methods expert) the candidate will receive training in designing and implementing her first independent functional MRI (fMRI) study. In addition, Dr. Susan Whitfield-Gabrieli (secondary mentor), a pioneer in network-based neuroimaging methods, will provide mentorship in advanced fMRI analytic techniques and programming. Together, the proposed training plan will support the candidate in investigating neural network substrates of MDD, sharpening new skills in advanced neuroimaging research, and building a program of independent research.
Dysfunction in the coordinated activity of large-scale neural networks is increasingly recognized to be central to the pathophysiology of major depression, but little is known about the temporal dynamics of such dysfunction or their relationship to the cognitive-affective deficits that characterize the disorder. The proposed study aims to investigate the temporal dynamics and behavioral correlates of neural network dysfunction in major depressive disorder. Developing a precise understanding of imbalanced network interactions over time may provide critical insight into how and why deficits in regulating attention and emotion arise in depression, and thus suggest targets for future research that investigates mechanisms of risk or treatment.
