Section Corrected Panatzatos Identification and characterization of genomic factors involved in maintaining brain circuits and neurotransmitter systems associated with psychopathological behavior could identify new therapeutic targets and elucidate mechanisms of psychopathology. Using recently available human postmortem brain gene expression maps (Allen Brain Atlas) and methods to quantify large-scale in vivo functional connectivity (FC) from fMRI data, the proposed project will develop and validate new computational tools which relate functional network architecture to molecular neuroanatomy in order to identify and characterize genomic risk factors for major depressive disorder (MDD) and suicide. Our pilot data demonstrate correlations between gene expression and FC, suggesting sufficient inter-individual consistency in their spatial distributions to allow detection of genes whose expression predict FC. Separate streams of research have linked abnormalities in FC related to resting state and negative valence processing to MDD and suicidal behavior. Dysregulated genomic factors that are involved in these specific circuits are more likely to confer risk towards these disorders.
Aim 1 is to develop and validate computational approaches that identify genes whose expression predict FC associated with resting state and negative valence processing.
Aim 2 is to test whether these genes are also dysregulated in MDD and suicide using a distinct genomics dataset, which would suggest translational value for the approach developed in Aim 1. A Corollary Aim is to create publically available, open-source tools which can be applied to identify genomic factors related to other RDoC constructs using task and resting-state fMRI. It is anticipated this project will facilitate evidence-based identification of psychiatric biomarkers and hypothesis generation. Results would also elucidate the relationships of genes with neural circuits and psychopathology at the NIMH Research Domain Criteria (RDoC) level and further our understanding of the molecular biology of brain functional architecture, mood disorders and suicidal behavior. To carry out these aims and attain my career goal of becoming an expert in translational neuroinformatics, the PI has mapped out a course of further training in clinical and molecular neuroscience, computational genomics, computer science and informatics and advanced statistics. This training will complement and build upon the PI?s previous training in cellular physiology, cognitive neuroscience and computational neuroimaging.
Section Depression is a disease with significant public health and economic burden, for which currently available treatments are not effective in the majority of patients. This proposal investigates the neurobiological mechanisms that determine antidepressant response and treatment-resistance, with a specific focus on molecular signaling pathways that regulate neuronal network activity in the hippocampal dentate gyrus, a key brain region involved in mood and cognition. This work will help to discover new targets for improved antidepressant treatments.
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