ISSN: ISSN: 2157-7412
Assistant Professor, Department of Radiology, Behavioral Sciences, Department of Neuroscience
Yeshiva University, Albert Einstein College of Medicine, USA
Dr. Xiaobo Li is Section Leader for Image Analysis at Gruss Magnetic Resonance Research Center (MRRC), Albert Einstein College of Medicine, Yeshiva University. Assistant Professor at Departments of Radiology, Neuroscience, Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Yeshiva University, NY, USA. She has received a Ph.D in Computer-Aided Geometric Design at University of Birmingham, UK, 2001-2004. Her research has a strong focus on MR brain imaging and application problems. She has extensive theoretical and practical experience in developing and translating mathematical techniques to quantitatively evaluate the structural and functional organization in the human brain using structural MRI, DTI, and fMRI.
1. Functional MRI analysis â€“ developing and translating refined quantitative mathematical and statistical methodologies for functional organization analyses to elucidate the pathophysiological mechanisms underlying normal brain development, and disorders such as Schizophrenia, ADHD, Depression, etc.. 2. Computational Neuroanatomy â€“ developing refined quantitative mathematical measures of cortical shape development and geometric shape modeling of brain structures using structural MRI and DTI 3.Integrated analyses of brain structure and function applying structural MRI, fMRI, DTI, PET, EEG/MEG measures and psychological and psychopharmacological factors. 4. Clinical Applications: I. Functional and anatomical developments of language processing circuit and default-mode network system during normal brain maturation and onset of Schizophrenia. II. Functional and anatomical impairments in visual- and auditory-language processing circuit in patients with Schizophrenia, ADHD, Depression, other inheritable mental disorders, and the genetic high-risk children and adolescents III. Pharmacological stimulant effects to the efficiency of default-mode network system and cognition in ADHD and Depression