Diagnosis of Alzheimer's Disease Using Dynamic High- Order Brain Networks Abstract Alzheimer's disease (AD) is the most common form of dementia with no known disease-modifying treatment. Current clinical diagnosis and monitoring of the disease are primarily based on subjective neuropsychological and neurobehavioral assessments, which are generally susceptible to large variability.
Abstract The twin study design in brain imaging offers a very effective way of determining heritability of the human brain. The difference in variability between monozygotic (MZ) and same-sex dizygotic (DZ) twins can be used in determining heritability. We propose to determine the extent of heritability of both structural and functional brain networks at the voxel-level using 200 pairs of twin (400 individuals) of fMRI/DTI and MRI.
Principal investigator/Program Director (Last, first, middle): Hanson, Stephen, José RFA-EB-15-006 Project Summary/Abstract Since the earliest days of neuroscience research, core methods have focused on matching specific functions to local brain structure and neural activity. The relationship between brain structure and function has been a key motivation for the development and application of novel methods and discovery. Despite the apparent success of this program in identifying brain areas associated with memory, attention, executive control, action- perception, language, etc..
ABSTRACT Neuronal spike-trains and various other signals in the central nervous system have a discrete, impulsive nature that is well characterized with point process statistical models. In several neuroscience applications, such impulsive signals are transformed upon interaction with biological processes or measurement artifacts, and are consequently observed as filtered point process data.
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.
