With ever-improving imaging technologies and ever-increasing
high-performance computational power, the complexity and scale of
acquired brain imaging data have continued to grow at an explosive
pace. Rapid advances in multimodality imaging technologies have
significantly accelerated brain disorder studies by providing
complementary information on many aspects of the human brain in the
normal and diseased states. Capitalizing on the availability of
large-scale data, we are now able to computationally integrate,
index and model the brain functions across a large population for
discovering more detailed understanding and more profound knowledge
about complex biological interactions in the human brain. Based on
our continuous research effort along this direction, this NSF
project is developing a novel, rigorous theoretical framework based
on Riemannian geometry, multivariate simplex splines, and
statistical learning, which provides a basis for multimodality
information integration and understanding across populations.
Specifically, our research team will design a fundamental framework
for advanced and integrated analysis of brain imaging data. It is
expected that the developed, advanced informatics tools will allow
the quantitative and integrative analysis of a variety of functional
patterns and the relationships between anatomical and functional
features in different datasets. The proposed computational framework
has the potential to be applied across multiple areas of brain
research as well as in clinical diagnosis.
The above figure shows the conformal brain surface model (Figure A
and Figure B) facilitates accurate matching and registration among
subjects in the canonical, spherical domain, hence supporting
integrated cross-subject analysis of Positron Emission Tomography
(PET) (molecular-level brain activity analysis) (Figure C),
Diffusion Tensor Imaging (DTI) (neural fiber connectivity analysis)
(Figure D), and Electroencephalography (EEG) (time-varying signal
analysis) (Figure E) in computer-aided diagnosis of brain disorders.
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