The Biostatistics Core will assist the Center in its efforts to quantify the role of genetic and pharmacologic influences of seroteronergic modulation on the development of brain structure and functioning, and of behaviour. The Biostatistics Core will focus on providing resources for consultation and performing statistical analysis for all projects. Specifically, we will assist in meeting Center goals by: 1) assisting in the statistical design and power analysis of all center projects;2) performing statistical analyses on data from a large birth-cohort study to determine effects of maternal SRI exposure on development and neuropsychiatric outcomes in offspring;3) performing statistical analyses on brain imaging data as well as data on clinical endophenotypes of depression, to quantify 5htllpr genotype and other interacting genotype-specific changes in brain structure/function, as well as association of these genes with clinical endophenotypes and neurophysiologic measures in a high risk 3-generation cohort;4) performing statistical analysis to determine the effect of SSRI exposure and genetic factors on infant brain structure and neurophysiology and early life temperament;and 5) assisting in analyzing data generated from animal models to determine serotoninmediated genetic and pharmacologic influences on developing brain structure across species. In addition, investigators in the Biostatistics Core will work closely with those in the genetics and imaging cores to develop novel and sophisticated statistical methods to integrate phenotypic, genotypic, neurophysiologic, and imaging data. These activities will be accomplished by regular individual and group meetings with project and core directors, to review data and suggest statistical approaches. Students from T32 training grants, both graduate students and fellows, will also be involved in this work.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Specialized Center (P50)
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Special Emphasis Panel (ZMH1-ERB-M)
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New York State Psychiatric Institute
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Spann, Marisa N; Serino, Dana; Bansal, Ravi et al. (2015) Morphological features of the neonatal brain following exposure to regional anesthesia during labor and delivery. Magn Reson Imaging 33:213-21
Horga, Guillermo; Kaur, Tejal; Peterson, Bradley S (2014) Annual research review: Current limitations and future directions in MRI studies of child- and adult-onset developmental psychopathologies. J Child Psychol Psychiatry 55:659-80
Polo-Kantola, Paivi; Lampi, Katja M; Hinkka-Yli-Salomaki, Susanna et al. (2014) Obstetric risk factors and autism spectrum disorders in Finland. J Pediatr 164:358-65
Goodman, Jarid; Marsh, Rachel; Peterson, Bradley S et al. (2014) Annual research review: The neurobehavioral development of multiple memory systems--implications for childhood and adolescent psychiatric disorders. J Child Psychol Psychiatry 55:582-610
Spann, Marisa N; Bansal, Ravi; Rosen, Tove S et al. (2014) Morphological features of the neonatal brain support development of subsequent cognitive, language, and motor abilities. Hum Brain Mapp 35:4459-74
Yan, Xu; Zhou, Minxiong; Ying, Lingfang et al. (2014) A fast schema for parameter estimation in diffusion kurtosis imaging. Comput Med Imaging Graph 38:469-80
Gyllenberg, David; Gissler, Mika; Malm, Heli et al. (2014) Specialized service use for psychiatric and neurodevelopmental disorders by age 14 in Finland. Psychiatr Serv 65:367-73
He, Xiaofu; Liu, Wei; Li, Xuzhou et al. (2014) Automated assessment of the quality of diffusion tensor imaging data using color cast of color-encoded fractional anisotropy images. Magn Reson Imaging 32:446-56
Yan, Xu; Zhou, Minxiong; Ying, Lingfang et al. (2013) Evaluation of optimized b-value sampling schemas for diffusion kurtosis imaging with an application to stroke patient data. Comput Med Imaging Graph 37:272-80
Talati, Ardesheer; Weissman, Myrna M; Hamilton, Steven P (2013) Using the high-risk family design to identify biomarkers for major depression. Philos Trans R Soc Lond B Biol Sci 368:20120129

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