Systems Neuroscience

Summary Progress from description to quantification is essential as a science matures. Yet numerical analysis of the elementary unit of brain circuitry—the individual neuron—continues to pose methodological challenges. Even the definition of a measurement yardstick (a metric) for the tree shape of a neuron remains an open research problem. Without such metrics, researchers cannot accurately classify neurons into cell types, an essential step toward understanding the circuit components and how they work together.

Project Summary Even in the absence of changing sensory inputs, many networks in the brain exhibit emer- gent dynamics: that is, they display patterns of neural activity that are shaped by the intrinsic structure of the network, rather than modulated by an external input. Such dynamics are be- lieved to underlie central pattern generators (CPGs) for locomotion, oscillatory activity in cortex and hippocampus, and the complex interplay between sensory-driven responses and ongoing spontaneous activity.

PROJECT SUMMARY/ABSTRACT The Weber-Fechner law is perhaps the oldest quantitative relationship in psychology. Detailed neurophysiology of the visual system demonstrates that neural representations of extrafoveal retinal position obey Weber-Fechner scaling such that the width of the receptive field centered at x goes up like x, creating a neural basis for the behavioral Weber-Fechner law.

Abstract We are developing a novel embedded-ensemble encoding (EEE) theory for mammalian neocortex to unify data from cell and network experiments, and to infer general principles of how information is processed in the brain. Our combination of investigators includes a theorist/modeler, an experimentalist/modeler and a modeler/ neuroinformatician. Our theory is based on the observation that cortical pyramidal neurons produce synaptically-induced dendritic plateau potentials that place an individual neuron into an activated state.

Project Summary/Abstract Network methods have emerged as some of the most useful approaches for analyzing functional MRI data. While great advancements have been made in these methods, limitations hamper the progress fMRI researchers can make in better understanding brain processes. In particular, researchers are typically limited to looking at properties within a network, such as how regions relate across time, and cannot simultaneously look at relations between known networks.

Project Summary/Abstract Understanding how the human brain produces cognition ultimately depends on precise quantitative characterization of context-dependent dynamic functional networks (DFN) that transiently link distributed brain regions. Progress in achieving this goal has been limited due to a lack of theoretical frameworks for characterizing DFNs and appropriate computational methods to test them. Devising and validating computational methods for investigating DFNs in the human brain is thus of great significance.

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.