The goal of this project is to map the maturation of cortical circuits and higher-order cognitive functions in children and adolescents at genetic risk for schizophrenia. Executive function and social-affective processing deficits are present in both individuals with schizophrenia and in those at genetic-high-risk (GHR) for the illness, and as such they represent cognitive endophenotypes of schizophrenia. Nevertheless, their characteristics in younger GHR individuals, the timing of their onset, and the specific neurodevelopmental mechanisms that accompany them are not known. Puberty is a critical period both for the maturation of these functions, and for the onset of psychosis. The proposed study will probe functional and structural change that accompany peripubertal brain maturation in genetic high risk individuals, and will investigate executive control and social-affective processes in GHR children and adolescents. We'will probe attention and executive and affective processing in fronto-striate-limbic regions in 60 GHR and 60 healthy subjects aged 9-18 using. We will use a multimodal assessment protocol, including (a) neurocognitive testing (b) functional magnetic resonance imaging (fMRI), (c) electrophysiological recordings (ERPs), and (d) structural and diffusion imaging (sMR| and DTI). In Specifc Aim 1, we will compare the neurocognitive profile of genetic high risk (GHR) children and adolescents to healthy subjects cross-sectionally, and further assess group differences in their maturational trajectory with longitudinal follow-ups.
Specific Aim 2 will characterize the functional profile of fronto-striate-limbic regions in GHR children and adolescents, using functional magnetic resonance imaging and electrophysiological recordings. The structural profile of fronto-striate-limbic regions in GHR adolescents, including both gray matter and white matter properties, will be assessed in Specific AIMS. Finally, Specific AIM 4 will focus on network level integrative functioning of fronto-striate and fronto- limbic circuitry in GHR children and adolescents by exploring associations between functional connectivity measures, white matter properties and neurocognitive measures. By bringing together a strong clinical high- risk research program and a well established and diverse research infrastructure, this project promises to unveil critical knowledge about the neurodevelopmental changes associated with genetic risk for schizophrenia. The proposed experiments are novel, timely and highly significant for they focus on a unique population and probe a unique stage of cortical development that is critical for understanding the pathophysiology of core neurocognitive deficits in schizophrenia.
| 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
