LONIR furthers this mission by developing resources to aid investigators and by facilitating collaboration between researchers around the world. By creating and sharing a diverse array of tools to analyze and visualize imaging data, LONIR fosters the interdisciplinary partnerships that are vital to the advancement of brain research.
Now in its nineteenth year, LONIR aims to continue providing innovative solutions for the investigation of imaging, genetics, behavioral and clinical data. Projects have been designed within the fields of Technology, Research and Development (TR&D).
Historically, the research community has struggled to maximize the potential of existing neuroscientific data due to its complexity. Without tools to understand the availability, characteristics and contents of these data, our capacity to map and comprehend the brain is greatly diminished.
The goal of TR&D1 is to remove these barriers to interpreting data by providing researchers with intuitive and interactive tools. Not only will LONIR resources assist with managing the underlying data, but they will provide powerful interfaces delivered via state-of-the-art web-based software.
The wide availability of novel analytical tools will expand the scope of the community that can easily access and utilize LONIR data. Whereas reuse of data has traditionally been rare due to the challenge of locating necessary elements, LONIR’s new technology will rapidly locate and extract relevant datasets on command.
Ultimately, the increased ease of accessing, identifying and managing complex neuroscientific data will render the reuse of data feasible for the broader research community. The resulting large-scale collaboration has the potential to accelerate the pace of discovery in the areas of brain structure, function and disease.
During the most recent P41 grant period, LONIR investigated automated tract extraction, methods of diffusion modeling that are more sensitive to disease, and adaptive techniques to detect changes in neural networks. The current project focuses on increasing power when analyzing brain connectivity and handling unprecedentedly large datasets.
These tools will ultimately enable the robust and accurate comparison of structural variations in development and pathology across populations.
The LONIR structure is designed to facilitate studies of dynamically changing anatomic frameworks: developmental, neurodegenerative, traumatic and metastatic. LONIR therefore targets new strategies for surface and volume parameterization that track change over time. Additional research cores include anatomic fundamentals and analyzing anatomic and cytoarchitectural attributes across multiple spaces and time.
LONIR also has a core that focuses on visualization and animation, which creates and distributes brain models that depict complex variations in brain structure and function over time. This includes developing projects for handling cortical data.
Ongoing national and international collaborations also encompass a diverse array of research foci, including Alzheimer’s disease, traumatic brain injury, epilepsy, autism, HIV, blindness, brain development and connectivity.
The approach to integrated computational neuroscience is based on a scalable, portable and distributed infrastructure, which uses object-oriented programming, Extensible Markup Language (XML), encrypted distributed computing and open-source design, implementation and tool dissemination.
Algorithms are designed to generate average models of brain anatomy and maps of growth, degeneration and their population statistics. These models are based on parametric surfaces, volumetric morphology and topology-preservation mapping.
The resulting algorithms are implemented, validated and distributed via the LONIR Pipeline environment and are applicable to a variety of computational neuroscience challenges in normal brain and disease.