Schizophrenia is associated with subtle abnormalities of brain structure on MRI, including enlarged lateral ventricles, reduced cortical gray matter volumes, reduced hippocampal volumes, as well as abnormal diffusion properties in white matter. While it has been hypothesized that these brain abnormalities arise during early brain development, there has been little direct evidence to support this idea. In the first funding period of this Conte Center project, we developed the magnetic resonance image (MRI) acquisition and image analysis tools to study very early brain development in children at high risk for schizophrenia. These included a genetic high risk group - the offspring of women with schizophrenia, and a """"""""structural"""""""" high risk group - children with prenatal isolated mild ventriculomegaly. Our results to data indicate that compared to normal controls, the offspring of mothers with schizophrenia have reduced cortical gray matter volumes on neonatal MRI. This is the first concrete evidence that early cortical development is compromised by genetic vulnerability. The perinatal and early postnatal period is one of the critical periods in the development of cortical 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 continue our study of every early brain development in normal and high risk children, applying our novel image analysis methodologies to study gray and white matter development in an expanding cohort, and to study longitudinal brain developmental changes as we follow our cohort into mid childhood. Specifically we will study longitudinal brain development in children at high risk for schizophrenia with 3T MRI (including diffusion tensor imaging) and neurodevelopmental assessments at ages 0, 1, 2, 4, and 6 years of age. We will also study early brain development in the offspring of mothers with bipolar illness as a comparison group for non-specific effects of medication and chronic illness. Finally, we have obtained DNA and MRIs on a large group of normal neonates and will determine if polymorphisms of risk genes influence cortical development at this earliest stage of life. ? ? ?
| 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 |
Showing the most recent 10 out of 187 publications
