Our team has been focused on the development and analysis of quantitative neuroimaging features in human fetuses and patients with genetic brain malformations and psychiatric/neurological brain disorders. Our research goal is to provide unique and biologically relevant imaging biomarkers that not only help us to better understand normal and abnormal brain development but also aid in the detection and diagnosis of disease. We have developed advanced methodologies for quantifying and investigating cortical sulcal pits and patterns and white matter structural connectivity/network using a cortical surface model.

Identifying functional and genetic implications in the cortical sulcal pits/patterns and collaborating with biology/genetic researchers, we have shown its normal individual variability and relationships with human intelligence, asymmetric language function, genetics, malformation of cortical development, developmental dyslexia, 16p11.2 deletion syndrome, and congenital heart disease. For the last few years, we have been focusing on the challenging problem of characterizing early cortical surface growth and sulcal pattern formation in the developing fetal brain. For automatic and quantitative fetal brain MRI analysis, we also aim to develop a fully automatic pipeline for fetal brain MRI processing including automatic brain segmentation; fetal MRI quality assessment; motion correction; fetal brain tissue segmentation; cortical surface extraction; and volume- and surface-based feature extraction. We are using state-of-the-art medical image processing and deep learning techniques for this pipeline development.

We are also working on brain structural connectivity analysis projects for various developmental brain disorders (polymicrogyria, tuberous sclerosis complex, 16p11.2 deletion syndrome, cerebral palsy, TUBB3 E410K syndrome, and autism spectrum disorder) using advanced analysis techniques such as individual gyral topology-based connectome analysis, rich-club subnetwork analysis, heat kernel-based network energy transport analysis.

Our research group pursues an innovative study toward practical clinical use of MRI quantification, which has the potential for early identification of children at risk for the disorders, allowing the opportunity for early remediation and improved clinical and surgical interventions and further minimizing public health costs.