The Neuroimaging core will acquire and process multimodal imaging data to enhance significantly our understanding of the effects of serotonergic signaling in the brain. It will provide the extensive expertise of its faculty and staff for conducting brain imaging research in mice, newborn babies, children, and adolescents using structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), perfusion MRI, MR spectroscopy (MRS), and high density EEG. By applying validated and reliable imaging and statistical methods, the core will ensure uniformity, consistency, and economy of scale across all projects of the center. The Core will contribute significantly to accelerate our understanding of the disturbances in the brain caused by serotonin availability at various stages during early brain development. To quantify disturbances in various cortical and subcortical brain circuits, research projects will acquire and analyze multimodal MR data, and compute various brain measures, including EEG power and coherence, local volumes of brain regions, and between-region connectivity. These measures will be correlated with genotype data to discern the gene-brain-behavior correlates across species, and age ranges from infancy to adulthood. Furthermore, the core will ensure data integrity by (1) quantifying identical brain measures using the same imaging and statistical methods across all projects;(2) rigorously evaluating methods developed to address better the challenges in analyzing data from a wide age range of participants;and (3) exploiting economy of scale by applying common components of image and statistical analyses across all projects, thereby greatly enhancing the efficiency and productivity of the center.

Public Health Relevance

The Neuroimaging core will support the integrative and translational research of the center by ensuring uniformity, consistency, and economy of scale across all projects, thereby accelerating the study of the effects of serotonergic signaling in the brain via various manipulations, including animal models and genotype, behavioral, and imaging data across several populations and species.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Specialized Center (P50)
Project #
Application #
Study Section
Special Emphasis Panel (ZMH1-ERB-M)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
New York State Psychiatric Institute
New York
United States
Zip Code
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

Showing the most recent 10 out of 19 publications