Dr. Toga’s research focuses on neuroimaging, informatics, mapping brain structure and function, and brain atlasing. He has developed multimodal imaging and data aggregation strategies and applied them in a variety of neurological diseases and psychiatric disorders, including neurodegenerative diseases, neurovascular diseases, and ocular diseases. His work in informatics includes the development and implementation of some of the largest and most widely used databases and data mining tools linking disparate data from genetics, imaging, clinical, and behavioral studies, and supporting global efforts in Alzheimer’s disease, Huntington’s Disease, and Parkinson’s Disease. His work also involves measurement of the dynamic brain during development and aging, and as a result of insult.
Learn more about Dr. Toga’s research.
Ph.D. in Neuroscience, St. Louis University
M.S. in Neuroscience, St. Louis University
B.S. in Psychology, University of Massachusetts
2016 - present: Ghada Irani Chair in Neuroscience
2013 - present: Provost Professor, Departments of Ophthalmology, Neurology, Psychiatry, and the Behavioral Sciences, Radiology and Engineering at the Keck School of Medicine of USC
2013 - present: Director, USC Mark and Mary Stevens Neuroimaging and Informatics Institute
2013 - present: Director, USC Laboratory of Neuro Imaging
2010 - 2012: Associate Vice Provost of Informatics, University of California, Los Angeles
2010 - 2013: Associate Dean, David Geffen School of Medicine, University of California, Los Angeles
1993 - 2010: Professor, Department of Neurology, University of California, Los Angeles
1993 - 2013: Associate Director, Division of Brain Mapping, Neuropsychiatric Institute, University of California, Los Angeles
1987 - 2013: Director, Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles
Dr. Ard aims to improve scientists’ understanding of neuroscientific data through novel visualizations, enabling human insight into highly complex datasets. Before joining LONI, he worked with USC’s Institute for Creative Technologies (ICT), where he mastered Virtual Reality techniques which he now applies to neuroimaging data.
2013 – Ph.D. in Neuroscience, Brown University
2006 – B.A. in Psychology, University of Colorado Boulder
Dr. Bienkowski’s research focuses on the relationship between anatomy, genetics, and function within brain regions involved in memory and emotion. His scientific goals are to identify the structural organization of neural networks, then dissect the functional role of specific network circuits to understand how the circuits cooperate to produce animal behavior.
2012 – Ph.D. in Neuroscience, University of Pittsburgh
2007 – B.S. in Neuroscience, University of Pittsburgh
Dr. Braskie’s research evaluates how genetic and environmental risk factors for Alzheimer’s Disease (AD) relate to brain structure, function, and connectivity throughout adulthood, with an emphasis on metabolic risk. She primarily studies cognitively intact adults and those with early mild cognitive impairment, studying how AD risk-related biological mechanisms and signaling pathways are associated with brain changes, using relevant blood and cerebrospinal fluid measures, as well as multimodal imaging (structural and functional MRI, diffusion tensor imaging, and PET).
2006 – Ph.D. in Neuroscience, UCLA
1994 – B.A. in Business Administration and Accounting, College of William and Mary
2019 - present: Assistant Professor of Neurology
2019 - present: Director of Education, Stevens NeuroImaging and Informatics Institute
2013- 2019: Assistant Professor of Research USC
2011 -2013: Assistant Researcher UCLA
2009 - 2011: Post doctoral fellow UCLA
2006 -2009: Post doctoral fellow UC Berkeley
2002-2006: Graduate student researcher UCLA
2003-Teaching assistant to Dr. Arnold Scheibel UCLA
Dr. Duncan’s expertise lies in the development of analytical tools to extract information from biomedical data. Both her Ph.D. and postdoctoral research used EEG data to isolate patterns indicating the onset of epileptic seizures. She has worked on developing algorithms, such as Unsupervised Diffusion Component Analysis, to identify early signs of Alzheimer's disease in patients using structural MRI data. Her recent projects include identifying biomarkers to predict epilepsy following a traumatic brain injury and using Virtual Reality to improve segmentation, a manual error-correction process required to analyze brain imaging data.
2013 - PhD in Electrical Engineering, Yale University
2007 - BS in Mathematics and Polish Literature, University of Chicago
Dr. Jahanshad is part of INI’s Imaging Genetics Center, where she researches genetic influences on brain structure using high-resolution structural and diffusion imaging. She also develops protocols for large-scale meta-analyses of brain structure and connectivity for the ENIGMA consortium. Her work with diffusion imaging involves monitoring the effects of infectious diseases, such as HIV, on the brain.
2012 – Ph.D. in Biomedical Engineering, UCLA
2006 – B.A. in Biomedical and Electrical Engineering
Dr. Jann develops analytical tools and clinical translations of functional MRI technology. He studies the relationship between functional connectivity, complexity and metabolism of brain networks. His multimodal approach involves a combination of fMRI, connectivity measures, cerebral blood flow, Transcranial Magnetic Stimulation (TMS) excitation and inhibition, and Magnetic Resonance Spectroscopy (MRS). He is also exploring the use of neuromodulation, such as TMS, in the treatment of various neuropsychiatric disorders.
2009 – Ph.D. in the Faculties of Medicine, Science and Veterinary Medicine, University of Bern (Switzerland)
2004 – Dipl. Sc. Nat. in Biology (Neuroscience focus), Swiss Federal Institute of Technology
2016, Assistant Project Scientist at the Laboratory of Functional MRI Technology, Ahmanson-Lovelace Brain Mapping Center, UCLA
2016, Postdoctoral researcher at the Laboratory of Functional MRI Technology, Ahmanson-Lovelace Brain Mapping Center, UCLA
2010 -2013, Postdoctoral researcher at the Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland
2009-2010, Research assistant at the Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland
Dr. Kim’s research spans an interdisciplinary cross-section of medical image processing, machine learning and neuroscience, covering both clinical neurology and neuropsychiatry. He studies multicontrast image registration and segmentation as well as surface modeling of both cortical and subcortical structures. One of his current projects involves the prediction of neurodevelopmental outcomes for preterm babies. He is also developing an online neuroimaging data quality control (image QC) system.
2016- Postdoctoral Fellowship in Imaging of Neurodevelopment, UC San Francisco
2012 -PhD in Biomedical Engineering (specialty: brain image analysis), McGill University / Montreal Neurological Institute experience neuroimaging, image processing, machine learning, clinical neuroimaging (preterm newborns, sleep disorders, epilepsy)
2011 – Ph.D. in Biomedical Engineering, Montreal Neurological Institute, McGill University
2003-M.S. in Biomedical Engineering, Hanyang University (Seoul, Korea)
2000-B.S. in Mechanical Engineering, Hanyang Unversity (Seoul, Korea)
Dr. Liew runs the Neural Plasticity and Neurorehabilitation Lab (NPNL) in the USC Chan Division of Occupational Science and Occupational Therapy. Her team aims to enhance neural plasticity in a wide population of individuals, especially those who have suffered severe strokes, exploring the applications of noninvasive brain stimulation, brain computer interfaces, neuroimaging, virtual reality, and behavioral techniques.
2012 – Ph.D. in Cognitive Neuroscience, USC
2008 – M.A. in Occupational Therapy, USC
2006 – B.A. in Kinesiology and English, Rice University
Dr. Neu’s research involves the management and visualization of neuroinformatics data. He develops numerical methods for image classification and feature extraction. At LONI, he is a key contributor to the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Global Alzheimer’s Association Interactive Network (GAAIN), the Parkinson’s Progression Markers Initiative (PPMI), and other projects.
1999 – M.S. in Computer Science, UCLA
1997 – Ph.D. in Physics, UCLA
1993 – M.S. in Physics, UCLA
1991 – B.S. in Physics and Mathematics, University of Wisconsin
UCLA Laboratory of Neuro-Imaging, 2002-2013
UCLA Department of Radiological Sciences, 1998-2002
Dr. Pa researches early brain dysfunction in older adults and individuals at risk for Alzheimer’s Disease with the goal of identifying biomarkers and designing effective interventions to slow or halt the onset of the disease. For example, she is working on several pilot projects combining physical and cognitive interventions in novel ways. She also analyzes neural alterations in brain function and network connectivity in individuals with mild cognitive impairment.
2007 - PhD in Psychology (specialty: Cognitive Neuroscience), UC Irvine
2002 – B.A. in Psychology, UC Irvine
Doctor of Medicine from University of California, Los Angeles
Ph.D. in Biomedical Engineering from University of California, Los Angeles
B.S. in Biomedical Engineering, Brown University
2018-2019: Fellowship, Neuroradiology, University of Southern California
2014-2018: Residency, Diagnostic Radiology, University of California, Los Angeles
2013-2014: Internship, Transitional Year, University of Hawaii
Interests involve mapping brain tissue macro and micro-structure using MRI, with the specific aim of obtaining micro-level neuroanatomical biomarkers. Also interested in studying microstructural alteration of brain tissue during development and aging.
Dr. Sepehrband uses MRI to map brain tissue macro- and micro-structure, with the specific aim of obtaining micro-level neuroanatomical biomarkers. He also studies the microstructural alteration of brain tissue during development and aging. Sepehrband also has extensive experience working with diffusion MRI and digital signal processing.
2015 – Ph.D. in Biotechnology and Neuroscience, University of Queensland (Australia)
2010 – M.S. in Digital Electronics, Sharif University of Technology (Iran)
Diffusion Magnetic Resonance Imaging
Brain tissue macro- and micro-structure mapping
Digital signal processing
My research focuses on the development of cutting-edge image analysis algorithms and their application in studying human brain structure and function.
Dr. Shi develops cutting-edge image analysis algorithms, which he applies to the study of human brain structure and function. He seeks to more accurately model the brain’s fibers in diffusion imaging using Fiber Orientation Distribution (FOD) reconstruction. He also studies anatomical shape modeling using the Laplace-Beltrami system, in order to create better brain mapping algorithms for use in researching Alzheimer’s Disease, retinopathies, and other disorders.
2005 – Ph.D. in Electrical Engineering, Boston University
1999 – M.S. in Electrical Engineering, Southeast University (Nanjing, China)
1996 – B.S. in Electrical Engineering, Southeast University (Nanjing, China)
Assistant Professor, Department of Neurology, UCLA School of Medicine, 2009-2013
PostDoctoral Fellow, Department of Neurology, UCLA School of Medicine, 2005-2009
Level set algorithms, inverse problems and regularization, statistical signal processing.
Associate Director, Mary and Mark Stevens Neuroimaging and Informatics Institute Director, Imaging Genetics Center Professor of Ophthalmology, Neurology, Psychiatry and the Behavioral Sciences, Radiology, Pediatrics and Engineering firstname.lastname@example.org
Dr. Thompson’s primary focus is imaging genetics, the study of how individual genetic differences lead to differences in brain wiring, structure, and intellectual function. He co-founded a worldwide imaging genetics consortium, ENIGMA, which studies 22 brain diseases in 37 countries, and has published the largest-ever neuroimaging studies of schizophrenia, major depression, bipolar disorder, and obsessive-compulsive disorder. Other research pursuits include developing new computational algorithms for neuroimaging, dynamic (4D) brain mapping, creation of a probabilistic atlas of the human brain and cortex, and creation of population-based atlases for Alzheimer’s Disease, Schizophrenia, and other disorders. Learn more about Dr. Thompson’s research.
1998 – Ph.D. in Neuroscience, UCLA
1993 – M.A. in Mathematics and Philosophy, Oxford University
1991 – B.A. in Greek and Latin Languages, Oxford University
2010 – 2013: Professor of Neurology, Professor of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA
2007 – 2013: Professor of Neurology, David Geffen School of Medicine, UCLA
2003 – 2007: Associate Professor of Neurology, David Geffen School of Medicine, UCLA
1998 – 2003: Assistant Professor of Neurology, David Geffen School of Medicine, UCLA
1993 – 1998: Fellow, Howard Hughes Medical Institute
1993 – 1998: Research Grantee, United States Information Agency, Washington, DC
1993 – 1998: Fulbright Scholar, U.S.-U.K. Fulbright Commission, London, England
Professor of Neurology and Radiology, Director of Imaging Technology Innovation email@example.com
Dr. Wang runs the Laboratory of Functional MRI Technology (LOFT), where he develops techniques and clinical translations of arterial spin labeling (ASL) and performs cross-scale and modality complexity analysis of resting state fMRI and electrophysiology data. Dr. Wang leads projects to develop and validate imaging biomarkers of cerebral small vessel diseases (cSVD) and vascular cognitive impairment and dementia (VCID). He also researches electric current mapping of transcranial direct current stimulation (tDCS). Another current project involves the development of low-dose CT perfusion technology, which may help significantly reduce radiation exposure without compromising image quality.
2010 – M.S.C.E. (Master of Science in Clinical Epidemiology), University of Pennsylvania School of Medicine
1998 -Ph.D. University of Science and Technology of China, Beijing, China (Biophysics)
1993- B.S. Fu Dan University, Shanghai, China (Biophysics)
2016-present Professor, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA
2016-present Director of Imaging Technology Innovation, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
2016-present Professor (secondary), Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA
2012-2016 Executive Director, UCLA-Beijing Joint Center for Advanced Brain Imaging.
2015-2016 Professor-in-Residence (secondary), Department of Radiology, UCLA David Geffen School of Medicine.
2015-2016 Professor-in-Residence, Department of Neurology, UCLA David Geffen School of Medicine.
2011-2015 :Associate Professor-in-Residence (secondary), Department of Radiology, UCLA David Geffen School of Medicine.
2010-2015: Associate Professor-in-Residence, Department of Neurology, UCLA David Geffen School of Medicine.
2011-2013: Adjunct Associate Professor, Department of Radiology, University of Pennsylvania School of Medicine.
2006-2010 :Research Assistant Professor, Department of Neurology, University of Pennsylvania School of Medicine.
2003-2010 : Research Assistant Professor, Department of Radiology, University of Pennsylvania School of Medicine.
Dr. Yan’s work involves functional MRI data acquisition and post processing, as well as MRI sequence development, including novel arterial spin labeling (ASL) technical development and non-contrast 4D dynamic Magnetic Resonance Angiography. One of her primary research projects is the assessment of intracranial vascular compliance (VC) using ASL MRI techniques, and the study of how intracranial VC changes in the aging brain.
2010 – Ph.D. in Biophysics, Institute of Biophysics, Chinese Academy of Sciences
2005 – B.S. in Biomedical Engineering, Huazhong University of Science and Technology
2013-2016:Assistant Researcher, Department of Neurology, UCLA
2010-2013:Postdoctoral Scholar, Department of Neurology, UCLA
Dr. Cabeen works on the development and evaluation of computational tools for modeling, visualizing, and analyzing neuroimaging datasets. His current research projects aim to improve our understanding of brain microstructure and connectivity using diffusion MR imaging. He is the creator and developer of the Quantitative Imaging Toolkit (QIT), a software platform for visualizing and analyzing neuroimaging datasets. Learn more at http://cabeen.io.
PhD in Computer Science from Brown University
MSc in Computer Science from Brown University
BSc in Engineering and Applied Science from the California Institute of Technology
Machine Learning, Image Synthesis, Brain Aging, and Alzheimer’s Disease.
B.S. in Automation and Electrical Engineering, Beijing University of Aeronautics and Astronautics
M.S. in Image Informatics, Chinese Academy of Sciences
Ph.D. - Biomedical Imaging, University of Southern California
Multimodal neuroimaging of neurovascular diseases.
Fast Heritability Estimation Method using Linear Mixed Model
Statistical Analysis of Brain Images
University of Southern California, PhD, Computational Biology and Bioinformatics, 2016 - present
Tsinghua University, BS, Mathematics, Probability and Statistics Track, 2012 - 2016
Research Assistant in Imaging Genetics Center, USC Stevens Neuroimaging and Informatics Institute
Research Assistant in Scientific Computing and Applied Statistics group, Department of Mathematical Sciences, Tsinghua University
Research Assistant in Brain Imaging lab, Department of Psychology, Tsinghua University
My research focuses are:
1. MRI pulse sequence design and programming: arterial spin labeling (ASL), simultaneous multi-slice (SMS), time-dependent 2D CAIPI acquisition.
2. Non-invasive measurement of cerebral blood flow and perfusion in human body organs (e.g. placenta).
3. Mapping water exchange across the blood-brain barrier (BBB) using diffusion weighted perfusion technique. Evaluation of water permeability change in early dementia and subjects at risk of small vessel disease.
4. Model-free modeling of arterial blood flow from dynamic MR angiography.
5. Ultra-high field: layer-dependent measurement of cerebral blood flow with submillimeter spatial resolution.
2019- Ph.D. in Biomedical Engineering, University of Sothern California (USC), Los Angeles, CA, USA
2016- M.S. in Bioengineering, University of California Los Angeles (UCLA), Los Angeles, CA, USA
2014- B.S in Engineering Physics, Tsinghua University, Beijing, China
Computational neuroimaging analysis and the application to studies of neuroanatomy and brain connectivity networks and their relationship to development, aging and pathological conditions.
PhD, Signal Processing, Tampere University of Technology, Finland, 2010
MSc, Signal Processing and Communication, University of Edinburgh, UK, 2005
BSc, Electronics and Information Engineering, Harbin University of Science and Technology, China, 2004
Postdoctoral Research Fellow, Montreal Neurological Institute, McGill University, Canada, 2010-2015