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Abstract Details

Activity Number: 621
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Statistics in Mental Health Research
Abstract - #305437
Title: Network Models in High-Dimensional Psychiatric Data
Author(s): Vivian H Shih*+ and Catherine A Sugar
Companies: University of California at Los Angeles and University of California at Los Angeles
Address: Department of Biostatistics, , CA, , United States
Keywords: covariance estimation ; network models ; high dimensionality ; phenomics ; neurocognition ; psychiatric disorders

The emerging discipline of phenomics, the study of patterns of observable traits characterizing deficits in specific disorders, promotes the pace of discoveries in neuropsychiatric research. Correlated neurocognitive phenotypes within specific illnesses as well as across multiple disorders can substantially improve current diagnostic approaches and therapeutic options. Complexities in psychiatric data such as high dimensionality, hierarchical structures, and correlated measures require advanced statistical tools for analysis. We present case-studies with appropriately developed methods, tailored to accommodate the underlying superstructure of the data. One such method is the use of graphical network models, a sparse covariance estimation technique constructively searching for sparse relational structures of possible phenotypes within and across groups. However, due to the highly correlated nature of our data, we expect a common base relationship among all measures as opposed to a sparse structure with few nonzero associations. We modify conventional covariance estimation to reflect this difference in order to extract key neurocognitive phenotypes of psychiatric disorders.

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