Theory & Data Analysis Tools

PROJECT SUMMARY / ABSTRACT Project Summary A key goal of computational neuroscience is to discover how the brain’s structural organization produces its functional behavior, and how impairment of the former causes dysfunction and disease. Rapid advances in neural measurement technologies are finally beginning to enable in vivo measurements of large-scale functional organization (via EEG, MEG, fMRI, PET, optical imaging) and the underlying structural connectivity architecture (via diffusion MRI, tractography).

Project Summary/Abstract: The broad, long-term objective of this grant is to advance a graph theoretical framework to identify core-nodes in a Brain Network of Networks to develop a software tool that will allow end-users from the broad neuroscience community to identify and analyze the most influential nodes in the brain in various disease states. Specific Aim #1: Develop Network of Networks (NoN) graph theoretical tools to identify “core nodes” for network vulnerability, which can be used as a tool to analyze disorders of the brain.

Project Summary The ability to quantitatively characterize incipient Alzheimer's disease (AD) pathology in its preclinical stage is a critical step for early interventions involving disease modifying therapy and for designing efficient clinical trials to test therapy efficacy. This project focuses on deriving statistical image analysis methods for identifying the relationship of morphometric changes in this early stage with direct indicators of AD pathology (such as amyloid deposition) and various risk factors such as family history in late midlife adults who are cognitively healthy.

Summary The human brain is a large, well-connected, and dynamic network. Using functional MRI data, modeling how this network processes the stimulus information has yielded insight on some of the mechanisms of the brain. However, the past efforts, including ours, on using small-scale models yielded limited understanding of how the complete and dynamic neural system functions in task-related experiments. Such understanding cannot be recovered from the data without substantial and collaborative efforts on model development.

A central goal of neuroscience is to understand how learning is implemented by the nervous system. However, despite years of studies in animals and humans, our understanding of both the computational basis of learning and its implementation by the brain is still rudimentary. A critical gap therefore exists between the large amount of behavioral and neural data that has been collected during learning and a mathematical and biological understanding of the rules governing motor plasticity.

Project Summary Calcium imaging methods allow us to record the simultaneous activity of many neurons with single-cell resolution; these methods are therefore a critical enabling tool for the BRAIN initiative and in neuroscience more broadly.

Project Summary Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by repetitive and stereotyped be- havior and difficulties in communication and social interaction. It is now one of the most prevalent psychiatric disorders in childhood, but it is also a lifelong condition, adversely affecting an individual's social relationships, independence and employment well into adult.

Abstract The overarching goal of this proposal is to learn how large groups of neurons interact in a network to perform computations that go beyond the individual ability of each cell. Our working hypothesis is that emergent behavior in neural networks results from their organization into a hierarchy of modular sub-networks or motifs, each performing simpler computations than the network as a whole.

In this U01 grant we propose a 5 year engineering development effort to advance Magnetic Particle Imaging (MPI) to replace MRI as the next-generation functional brain imaging tool for human neuroscience. MPI is a young but extremely promising technology that uses the non-linear magnetic response of ironoxide nanoparticles to localize their presence in the body. MPI directly detects the nanoparticle's magnetization rather than using secondary effects on the Magnetic Resonance relaxation times.

ABSTRACT: The strategic plan of the