The current classification of psychiatric disorders is based on categorical clustering of signs and symptoms, without regard to underlying neurobiologic mechanisms. The reification of these categories has constrained efforts to develop an understanding of the fundamental behavioral, neural and genetic mechanisms that give rise to various forms of psychopathology. To address this disconnect between mechanism and nosology, the NIMH recently launched the RDoC project to facilitate a more """"""""bottom up"""""""" approach to psychopathology. Our proposal focuses on the """"""""negative valence"""""""" domain of the RDoC matrix and aims to characterize and validate a neural phenotype of the """"""""Anxiety"""""""" construct (""""""""response to potential threat""""""""). In a uniquely large neuroimaging resource (the MGH Genomic Superstruct Project, GSP) we have recently identified a neural measure of limbic system integrity (amygdala enlargement and medial prefrontal cortical [mPFC] thinning) that is robustly associated with dimensional measures of trait anxiety. Consistent with the goals of the RDoC framework, we now propose to validate key biological and clinical features of this anxiety dimension in three stages: 1) Clinical Characterization: we will demonstrate the relevance of this neural phenotype to clinical populations presenting with significant anxiety symptoms and its association with symptom severity, chronicity and functional impairment;2) Neural Dissection: we will use advanced Connectome imaging technology to examine the relationship between the anxiety neural phenotype and white matter connectivity between the mPFC and specific amygdala subnuclei;and 3) Genetic Dissection: using common and rare (exome array) genomewide data (N = 2078), we will conduct single variant, genome partitioning, and biological pathway analyses to identify allelic contributions to the anxiety neural phenotype and characterize the aggregate heritability and biological significance of contributing loci. Successfu completion of these aims will yield novel insights into the neural, behavioral, and genetic basis of the RDoC anxiety dimension and provide a crucial step towards the RDoC's goal of a new framework for psychiatric classification grounded in etiology and pathogenesis.
This proposal combines state-of-the-art neuroimaging, behavioral, and genomic data to characterize neural and genetic contributions to the RDoC Anxiety construct (response to potential threat). We also aim to validate the clinical relevance of our brain-based and self-report measures of anxiety by characterizing their relationship to the severity, chronicity and functional impact of anxiety symptoms. This project will inform the development of RDoC criteria for the negative valence domain and the larger goal of grounding mental disorders in underlying biological and psychological dimensions.
|Ge, Tian; Holmes, Avram J; Buckner, Randy L et al. (2017) Heritability analysis with repeat measurements and its application to resting-state functional connectivity. Proc Natl Acad Sci U S A 114:5521-5526|
|Sabuncu, Mert R; Ge, Tian; Holmes, Avram J et al. (2016) Morphometricity as a measure of the neuroanatomical signature of a trait. Proc Natl Acad Sci U S A 113:E5749-56|
|Smoller, Jordan W (2016) The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders. Neuropsychopharmacology 41:297-319|
|Lee, P H; Baker, J T; Holmes, A J et al. (2016) Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol Psychiatry 21:1680-1689|
|Ge, Tian; Nichols, Thomas E; Ghosh, Debashis et al. (2015) A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application. Neuroimage 109:505-514|
|Ge, Tian; Nichols, Thomas E; Lee, Phil H et al. (2015) Massively expedited genome-wide heritability analysis (MEGHA). Proc Natl Acad Sci U S A 112:2479-84|