RESEARCH GOALS:
The research goal of our laboratory is to study cutting-edge computational and informatics methods, and turn them into practical tools that can help computers better understand digital data (e.g., 1-D sequences, 2-D images, and 3-D shapes) in practical applications. In particular, we are recently working on various neuroimaging and bioinformatics projects. In these projects, we apply, extend or develop state-of-art software tools for analyzing structural and functional neuroimaging data as well as genomic and other related biomarker data. The ultimate goal is to improve early diagnosis and mechanistic understanding of disease processes and treatment response for brain disorders such as Alzheimer's disease and schizophrenia, as well as the cognitive effects of cancer chemotherapy. We are also involved in two Fetal Alcohol Spectrum Disorders (FASD) studies, where we develop facial imaging methods to identify features important in FASD as well as examine a parallel mouse model to examine how timing of alcohol exposure influences the pattern of facial dysmorphology. The computational methods we study in these biomedical applications include (1) image & vision computing, (2) data mining & pattern recognition, and (3) geometric modeling & graphics.

MULTIDISCIPLINARY RESEARCH TEAM:
This research program is intensively multidisciplinary. We have assembled an excellent set of critical resources, including (1) experts from neuroscience, neuroimaging, computer science, genetics, informatics, statistics, and related domains, (2) state-of-the-art imaging facilities, and (3) state-of-the-art computer systems (access to supercomputers at IU, powerful workstations, file & web servers) and software tools. These resources greatly enable our capability to not only collect large volumes of imaging and other biomarker data but also study major computational and analytic challenges limiting the effective application of these data and tools towards solving complex biomedical problems.

RESEARCH AFFILIATIONS:
Center for Neuroimaging, Indiana Institute of Biomedical Imaging Sciences
Center for Computational Biology and Bioinformatics
Stark Neurosciences Research Institute
The Indiana Alzheimer Disease Center
Personalized Therapeutics Group
TRIP Translating Research into Practice

SAMPLE STUDIES:

	SPHARM-MAT (under development): SPHARM-MAT is a matlab-based 3D shape modeling and analysis toolkit for neuroanatomical studies, and is designed to aid statistical shape analysis for identifying morphometric changes in brain structures related to neuropsychiatric disorders.
	IG-Browser (under development): IG-Browser is an imaging genomic browsing system designed for genome wide analysis of neuroimaging phenotypes. The system is still under development. Some results created by our prototype tool are available here.
	Fourier method for large scale surface modeling and registration: We present a simple and efficient Fourier expansion method on the sphere that enables large scale modeling, and propose a new SPHARM registration method that aims to preserve the important homological properties between 3D models.
	Parametric surface modeling and registration for comparison of manual and automated segmentation of the hippocampus: We compare an automated method with a manual protocol on the determination of hippocampal boundaries from MRI scans. To perform the comparison, we develop an enhanced SPHARM processing framework to model and register these hippocampal traces.
	Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort: We examined baseline 1.5T MRI scans from 693 participants in the ADNI cohort to (1) characterize initial differences between the AD, MCI, and HC groups and (2) detect anatomic features associated with imminent conversion from MCI to probable AD within one year (MCI-Converters).
	Modeling 3-dimensional morphological structures using spherical harmonics: We build virtual 3D models of male cerci and female thoraces using spherical harmonics to understand how male and female damselflies make mating decisions. The spherical harmonic model provides a quantitiative description of a 3D shape in a multidimensional phenotypic space. See here for more details.
	Efficient registration of 3D SPHARM surfaces: SHREC is an efficient algorithm for registration of 3D SPHARM surfaces. SHREC follows the iterative closest point (ICP) registration strategy, and alternately improves the surface correspondence and adjusts the object pose. SHREC overcomes the limitation of the conventional FOE method, and has the potential to be used in general cases.