Schizophrenia is associated with subtle abnormalities of brain structure on MRI, including enlargedlateral ventricles, reduced cortical gray matter volumes, reduced hippocampal volumes, as well as abnormaldiffusion properties in white matter. While it has been hypothesized that these brain abnormalities ariseduring early brain development, there has been little direct evidence to support this idea. In the first fundingperiod of this Conte Center project, we developed the magnetic resonance image (MRI) acquisition andimage analysis tools to study very early brain development in children at high risk for schizophrenia. Theseincluded a genetic high risk group - the offspring of women with schizophrenia, and a 'structural' high riskgroup - children with prenatal isolated mild ventriculomegaly. Our results to data indicate that compared tonormal controls, the offspring of mothers with schizophrenia have reduced cortical gray matter volumes onneonatal MRI. This is the first concrete evidence that early cortical development is compromised by geneticvulnerability. The perinatal and early postnatal period is one of the critical periods in the development ofcortical connectivity - a time of rapid synapse growth - one that is a focus of this Conte Center. In addition,we have developed a large cohort of normal controls. In the second funding period, we propose to continueour study of every early brain development in normal and high risk children, applying our novel imageanalysis methodologies to study gray and white matter development in an expanding cohort, and to studylongitudinal brain developmental changes as we follow our cohort into mid childhood. Specifically we willstudy longitudinal brain development in children at high risk for schizophrenia with 3T MRI (includingdiffusion tensor imaging) and neurodevelopmental assessments at ages 0, 1, 2, 4, and 6 years of age. Wewill also study early brain development in the offspring of mothers with bipolar illness as a comparison groupfor non-specific effects of medication and chronic illness. Finally, we have obtained DMA and MRIs on alarge group of normal neonates and will determine if polymorphisms of risk genes influence corticaldevelopment at this earliest stage of life.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
5P50MH064065-07
Application #
7656678
Study Section
Special Emphasis Panel (ZMH1)
Project Start
2008-08-01
Project End
2012-07-31
Budget Start
2008-08-01
Budget End
2009-07-31
Support Year
7
Fiscal Year
2008
Total Cost
$348,833
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Type
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Lyu, Ilwoo; Kim, Sun Hyung; Girault, Jessica B et al. (2018) A cortical shape-adaptive approach to local gyrification index. Med Image Anal 48:244-258
Stephens, Rebecca L; Langworthy, Benjamin; Short, Sarah J et al. (2018) Verbal and nonverbal predictors of executive function in early childhood. J Cogn Dev 19:182-200
Girault, Jessica B; Langworthy, Benjamin W; Goldman, Barbara D et al. (2018) The Predictive Value of Developmental Assessments at 1 and 2 for Intelligence Quotients at 6. Intelligence 68:58-65
Tu, Liyun; Styner, Martin; Vicory, Jared et al. (2018) Skeletal Shape Correspondence Through Entropy. IEEE Trans Med Imaging 37:1-11
Jha, Shaili C; Xia, Kai; Schmitt, James Eric et al. (2018) Genetic influences on neonatal cortical thickness and surface area. Hum Brain Mapp 39:4998-5013
Wang, Yan; Ma, Guangkai; An, Le et al. (2017) Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI. IEEE Trans Biomed Eng 64:569-579
Xia, K; Zhang, J; Ahn, M et al. (2017) Genome-wide association analysis identifies common variants influencing infant brain volumes. Transl Psychiatry 7:e1188
Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2017) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp 38:1009-1024
Wei, Lifang; Cao, Xiaohuan; Wang, Zhensong et al. (2017) Learning-based deformable registration for infant MRI by integrating random forest with auto-context model. Med Phys 44:6289-6303
Gao, Wei; Lin, Weili; Grewen, Karen et al. (2017) Functional Connectivity of the Infant Human Brain: Plastic and Modifiable. Neuroscientist 23:169-184

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