For incoming students, and anyone else who's interested,
here's a summary of some of the projects I'm currently working on, with my
at the UCLA Laboratory of Neuro Imaging.
I'm including some exciting areas where students coming to the
lab briefly for a lab rotation may be interested in helping out. Some short videos
describe some of the projects (see symbols below).
Contact me if you have any questions!
If you are a mathematician or an engineer, you
may prefer to look at these projects.
[Updated - June 2007]
My research focuses on developing new mathematical and computational
approaches for analyzing human 3D brain image data. We use these approaches
to investigate the major diseases of the human brain, to better understand
brain structure and function in health and disease. Our
research team consists of
neuroscientists, medical doctors, mathematicians and engineers. We
around 50 labs worldwide (see here) and
publish extremely actively.
BRAIN MAPPING IN
Patient populations being studied include
large numbers of subjects with Alzheimer's Disease, with mild cognitive
impairment, and people at genetic risk for Alzheimer's Disease. Another project studies
how HIV/AIDS damages the brain.
Other active projects
focus on understanding brain changes and drug effects in
schizophrenia and bipolar disorder.
We are studying chronically-medicated populations, first-episode patients, twins discordant for
schizophrenia, and children and adolescents with early onset schizophrenia. Our interest in how schizophrenia
develops has also led us to expand our studies of
. We also study bipolar disorder (in adolescents and adults) and drug effects on the brain (lithium, antipsychotics,
We are creating new approaches to map
how the brain grows in childhood and
in the teenage years. Finally, we are especially interested in mapping
genetic influences on brain structure. Even in normal individuals, it is intriguing to understand
how our genes (and other factors) affect our brain structure and
It can also help us investigate
the genetic causes and
inherited risks for disease.
Part of this work
population-based brain atlases
to encode and represent
patterns of anatomic variation, and to
detect structural differences
in health and disease. These approaches often use some very interesting
mathematics as well as
high-performance computing techniques.
We also have active research projects on
Williams syndrome, fragile X syndrome,
IQ (cognitive ability).
In one project, we are creating the first time-lapse maps of Alzheimer's disease spreading in the living brain.
We are building a population-based atlas of the
brain in Alzheimer's Disease. This is a
framework for analyzing brain
data in Alzheimer's patients. It combines images and 3D anatomical models with
MRI, PET, SPECT and histologic data from large numbers of patients with dementia.
Our goal is to determine
3D maps of structural
differences, patterns of gray matter loss, anatomic variability, brain asymmetry,
and regional atrophy
in populations of normal elderly subjects, patients with mild cognitive impairment (MCI),
and patients diagnosed with, or at genetic risk for, Alzheimer's Disease. In these projects,
new mathematical and computational
approaches are being developed, tested and used for analyzing MRI-derived
structural models of the cortex, deep sulci, ventricular system, basal ganglia,
corpus callosum, amygdala and hippocampal formation.
We are especially interested in
understanding the patterns of degenerative rates over time. In an MCI clinical trial, we are investigating
how these profiles of degeneration
can be detected early and slowed down by therapy. We are also developing imaging strategies for
early detection and mapping of degenerative change. A new project is linking cortical thinning in Alzheimer's with amyloid burden
measured in living patients using FDDNP-positron emission tomography.
Several exciting studies of schizophrenia are underway. Using novel brain mapping
approaches that we developed for tracking subtle changes in the brain over time,
we are analyzing chronically medicated patients, neuroleptic naive patients, twins discordant for schizophrenia,
and children and adolescents with early-onset schizophrenia. We recently charted
a dynamic wave of gray matter
loss that occurs as schizophrenia develops.
We are interested to see if this implicates some
initiating the disease.
In a clinical trial, we are investigating how antipsychotic drugs
such as haloperidol and olanzapine decelerate these brain changes. Using functional MRI (fMRI), we are also studying
patterns of brain activation in
and how they relate to genetic polymorphisms and underlying changes in cortical structure.
In another project, we are analyzing growth patterns during normal and
abnormal brain development. There are some exciting findings, which we reported recently
in the journal Nature, exploring
extremely complex dynamics of
brain growth in children ranging from
6 to 15 years of age. Some very nice articles were written about these
findings in the news media, and these can be found here. Detailed maps
of growth patterns in young children, who were scanned repeatedly with MRI,
can be found here. Current work is creating
time-lapse movies of brain development based on
repeated MRI scans of children as they grow up. We are studying how these brain changes are
accelerated or derailed in children and teenagers
who develop schizophrenia,
or bipolar disorder. We are also developing new brain mapping techniques to help study brain changes in
developmental disorders. We are mapping cortical and subcortical structural differences in
autistic children and Fragile X syndrome. Another project maps subtle cortical
abnormalities in a
genetic disorder of brain development: Williams syndrome, where we
recently mapped the profile of cortical abnormalities.
This project studies the damage caused by the HIV virus in the brain. We are creating maps of the regions affected, and assessing
changes relate to cognitive and immune changes in people who are HIV-positive or have AIDS. We are examining how
anti-retroviral treatment influences the rate of brain degeneration in HIV patients. Specific projects focus on the
cerebellum and examining cognitive correlates of progressive brain changes.
EFFECTS ON THE
A new project maps the
damaging effects of methamphetamine use on human brain structure. Chronic drug abuse induces dramatic changes in
the basal ganglia, hippocampus, ventricular system, and cortex. We are beginning to link these changes with metabolic changes, to
how addictive drugs impact the human brain. We are also mapping the pattern of progressive white matter deterioration in
meth users and its relation to depression.
We are currently conducting a broad range of projects analyzing tumor growth and therapeutic
Using a variety of brain mapping approaches, our goal is to understand
how drug treatment, radiotherapy, and surgical resection modulate the rates of
tumor growth, as well as other imaging indices of
tumor progression, over time.
We are also developing new types of brain mapping tools that combine
maps of local growth rates, spectroscopic maps,
parametric MRI imaging,
and pathologic and genetic measures of
tumor changes. We are also investigating patients' response to therapy, to understand which therapies are best, and how
they affect tumor progression and recurrence in different patients.
We are creating detailed maps of dynamic response to the
chemotherapeutic agent Temodar (temozolamide).
We are also uncovering the inter-relationships among the major imaging measures as tumors evolve.
These projects involve a significant amount of
modeling. Finally, we are beginning a new intraoperative imaging project linking detailed maps of tumor growth rates with
differential expression of pathological and genetic markers in tissue biopsied during surgery. This key project will be
the first to link genetic and molecular cascades implicated in tumor growth and response with 3-dimensional changes in imaging markers observable at
high-resolution in individual patients. Since tumor growth is heterogeneous in individual patients, this project will
clarify the relationships between the molecular and cellular targets of therapy and fine-scale changes detectable with
imaging through repeated short-interval scanning.
We have a major funded effort to determine
genes affect brain structure, function and fiber connectivity.
Genetic brain maps, in particular,
can show whether we inherit patterns of brain structure from our parents, and if so, to what degree. We especially
want to understand
which parts of the brain are most strongly determined by our genes. We are scanning 700 twins with
high-angular resolution diffusion imaging (HARDI) at 4 Tesla to examine which genes influence fiber
integrity and connectivity in the brain. (Read more about HARDI on the math page.) Parallel studies are designed
to help understand
risk for diseases that affect the brain, as well as their genetic transmission.
The remaining projects concentrate on the mathematical and engineering aspects of brain imaging.
One important on-going project focuses on developing complex
and 3D warping
algorithms for brain data. Differences in brain structure
make it hard to compare data from different subjects, and to distinguish
abnormal structure from normal anatomic variations. Transforming 3D brain
data into the shape of a single target anatomy, or onto a neuroanatomic atlas,
removes subject-specific shape variations, and allows us to compare and integrate
3D functional brain imaging data
across subjects and groups. Applications of highly non-linear
image warping algorithms include the
transfer of multi-subject 3D functional,
vascular and histologic maps onto a single anatomic template, and the mapping of 3D
atlases onto the scans of new subjects. Local shape changes can also be detected
in 3D medical images in disease, and during normal and
abnormal growth and development. To tackle this problem, I have
been developing and testing algorithms for calculating
biologically-driven flow field transformations of
extremely high spatial dimension, which warp one anatomic scan into structural
correspondence with another. These warping transforms measure the shape differences between
the anatomies of the different subjects. We are also developing approaches to
brain mapping algorithms, and to
automatically find structures in brain images, based on the mathematics of
deformable templates in diffused potential fields. Our newest work in this area uses
level sets (implicit functions),
2D and 3D harmonic maps,
and fluid PDEs for nonlinear image registration and cortical surface mapping.
As a more technical project, I am developing strategies and algorithms
to create a
comprehensive probabilistic atlas
of the human brain based on high-dimensional
random tensor field transformations. This new type of probabilistic, digital brain atlas
is designed to detect and measure structural abnormalities throughout
new subjects' 3D MRI scans. The ultimate goal is to determine (1) whether
the detected anatomic variants are characteristic of certain disease states
(e.g., Alzheimer's), (2) how they relate to genetic, therapeutic, and demographic risk factors,
and (3) how we can distinguish them mathematically from normal patterns of brain
variation. More information on probabilistic brain atlases, and the many thousands of
subjects that underlie them, can be found
Often disease-specific patterns that are hard to see in an individual brain scan begin to emerge
after averaging brain data from large numbers of subjects. Construction of a
population-based brain atlas requires the warping of large numbers of brain
images into structural correspondence, prior to the estimation of an
anisotropic Gaussian random field, to represent the calculated structural variations,
on a high-resolution image lattice.
Given a 3D brain image of a
new subject, the algorithms calculate a set of high-dimensional volumetric maps,
typically with 384x384x256x3 (~0.1 billion) degrees of freedom, fluidly
scan into structural correspondence with other normal scans, selected one by
one from an anatomic image archive. The family of volumetric warps so
constructed encodes statistical properties of local anatomical variation
throughout the architecture of the brain. Probability maps
can then be generated, which quantify the severity of structural
abnormalities in the anatomy of the new subject. These new techniques for brain image analysis
are reviewed here, and less mathematically
here. Our development of supercomputing approaches to help solve these problems is reviewed
Results of these projects, as well as other exciting new projects
on brain development, brain atlases, and analysis of functional, neurochemical and
in Alzheimer's Disease, may be found in the form of over 600
summaries on the Internet
If any of these papers look interesting to you, please
send me a message by e-mail,
and I'll be glad to send you some free reprints of any papers you would like!