PROJECT SUMMARY/ABSTRACT Acute brain injuries can lead to secondary brain damage that worsens the outcome. Reduced cerebral blood flow can induce ischemia, while excess blood flow can cause hemorrhage. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches at neurointensive care units (NICUs).
PROJECT SUMMARY Non-invasive imaging of human brain function plays an important role in advancing neuroscience research and understanding neurological diseases. This need has been met primarily by functional magnetic resonance imaging (fMRI). fMRI, though powerful, is an expensive technique that is not suitable for subjects who cannot tolerate small spaces or cannot stay still (e.g. children, psychiatric disorders), and cannot be used for tasks that require subjects to interact with a natural environment, or for tasks that conflict with the scanner noise, e.g. auditory studies.
PROJECT SUMMARY/ABSTRACT Functional MRI (fMRI) is the most widely-used tool to noninvasively measure brain function and has produced much of our current knowledge about the functional organization of the human brain. However, all fMRI methods measure neuronal activity indirectly by tracking the associated local changes in blood flow, volume and oxygenation, which limit their spatiotemporal specificity to the underlying neuronal activity.
Abstract/Project Summary Blood oxygenation level dependent (BOLD) fMRI is widely used in neuroscience studies. Technical advancement in the recently years has enabled BOLD signals to be acquired at sub-second temporal resolution, opening a new window for examining functional dynamics of the human brain, e.g. resting- state fMRI. The BOLD signal originates from the mismatch between cerebral blood flow (CBF) and metabolism changes, and is complex; it serves as an indirect measure of neural activities.
ABSTRACT Recent advances in neural recording technologies have made it possible to study increasingly large and di- verse subsets of neurons, producing a growing interest in the collective computational properties of neural pop- ulations. Ideally, causally testing these population hypotheses requires timing and selecting experimental ma- nipulations based on the current state of neural dynamics, but technical limitations have rendered this difficult in practice.
Project Summary How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This understanding and characterization will be critical towards the application of principled neurostimulation to specific brain regions to study the effect of neural circuit perturbations on behavior, and conversely towards predictions of the neural activity during perturbations in the behavior.
PROJECT SUMMARY/ ABSTRACT Advances in neurotechnologies are producing large and complex datasets at unprecedented rate. Large-scale electrophysiological and optical imaging recordings provide an urging need to develop novel theoretical and analytical approaches to analyze and interpret these multi-scale, multi-resolution brain recordings.
Project Summary Divisive normalization (DN) is a well-established theory of how interactions between neurons in a circuit modulate the activity of individual neurons. DN has been termed a canonical operation because it describes a wide range of empirical data across species and brain areas, and theory predicts that DN underlies behavioral gains of sensory integration and visual attention. Despite this progress, it has been difficult to tie DN to circuit and cellular mechanisms, and to quantify its impact on neural coding and behavior. Two main obstacles have limited progress.
ABSTRACT Much evidence suggests that the development of orientation selectivity and maps in primary visual cortex (V1) is instructed by spontaneous patterns of input activity, without the necessity of visual experience.
Abstract The goal of this research is to develop new multivariate data analysis techniques for neural recordings that reveal causal dependencies between recording sites. Delay Differential Analysis (DDA) is a robust and efficient nonlinear time-domain algorithm for time series data that complements linear spectral methods. DDA combines delay and differential embeddings in nonlinear dynamical systems to discriminate between different normal and abnormal cortical states with high temporal resolution and insensitivity to artifacts.
