In the biomedical research community, there exists a need for novel tools and strategies to more precisely model the brain’s structure and function. New techniques can enhance the current understanding of the developing brain and healthy adult brain, as well as various disease states, transcending traditional boundaries such as age, sex and species.

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).

Research Areas

Data Science

Data Science focuses on the methodological developments for the analysis of brain imagery. Specifically, this project will design and distribute new methods for robust image segmentation and registration, quality assurance and evaluation of image processing results, and processing of structural and diffusion brain data.

Diffusion MRI and Connectomics

Diffusion MRI and Connectomics will advance the study of brain connectivity using diffusion imaging and its powerful extensions. This project will use Deep Learning to develop tract-based statistical analysis tools and adaptive connectivity mapping approaches. It will enable analysis of large diffusion imaging datasets totaling over 10,000 subjects.

Intrinsic Surface Mapping

Intrinsic Surface Mapping develops novel algorithms for surface reconstruction, modeling and analysis. By removing unnecessary metric distortion, it will provide greatly improved accuracy and power in detecting alterations to brain anatomy and function in disease studies.

Current Research

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.