Current NIC projects:
Brain segmentation is the problem of creating a 3d map of tissue types (e.g. flesh,skull, cerebrospinal fluid, white matter, grey matter) from input image data. Work at the NeuroInformatics center has focused on the development of a set of software tools (called BrainK) for extracting and integrating head tissue geometries from CT and / or MR images.
Our Electrical Head Modeling research focuses on developing high-performance computational methods for solving the forward electrical head problem with high resolution, individual head tissue geometries.
Electrical Impedance Tomography (EIT) involves injecting low levels of current into the body and measuring induced surface voltages. Our research involves combining high resolution tissue geometries from MR imaging with EIT measurements to infer tissue conductivities.
The connectome graph based source modeling research seeks to exploit knowledge of anatomical connectivity in order to improve EEG source localization. In particular we are studying how to build prior models for cortical sources that are informed by white matter tractography from non-invasive diffusion weighted MRI imaging.
NEMO : an NIH funded project to study the design and implementation of ontologies to aid development of tools to support representation, storage, and sharing of brain electromagnetic data.
The Application Server provides a Representation State Transfer (REST) interface to the NIC data and workflow toolset. These toolsets enable the integratation of multiple neuro-imaging research projects taking place at the NIC and its partner labs.
HiPerSAT, a C++ library and associated tools, processes large EEG data sets with statistical data whitening and ICA (Independent Component Analysis) methods. The library uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits available parallel hardware.
Older NIC projects:
The goal of the GEMINI project is to research methods for delivering grid-enabled research tools to neuroinformatic researchers. We are currently developing optimized, multi-processor enabled EEG signal cleaners that integrate with popular tools. This work will transition into fully grid enabled services that can be easily accessed by researchers anywhere in the world.