A5061381325- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Photoreceptor and optogenetics research
- Blind Source Separation Techniques
- Neuroscience and Neural Engineering
- Neuroscience and Neuropharmacology Research
- Mental Health Research Topics
- Sparse and Compressive Sensing Techniques
- Memory and Neural Mechanisms
- EEG and Brain-Computer Interfaces
- Advanced Fluorescence Microscopy Techniques
- Neurobiology and Insect Physiology Research
- Insect and Arachnid Ecology and Behavior
- Plant and animal studies
- Receptor Mechanisms and Signaling
- Cell Image Analysis Techniques
- Neuroendocrine regulation and behavior
- Stress Responses and Cortisol
- Circadian rhythm and melatonin
- Neural Networks and Applications
- Treatment of Major Depression
- Transcranial Magnetic Stimulation Studies
- Bioinformatics and Genomic Networks
- Digital Holography and Microscopy
- Advanced Neuroimaging Techniques and Applications
Cornell University
2018-2024
Weill Cornell Medicine
2018-2024
MIND Research Institute
2018-2023
New York Proton Center
2023
Columbia University
2018-2020
Simons Foundation
2018-2020
Stanford University
2008-2019
Columbia University Irving Medical Center
2018
Bioengineering Center
2015-2016
Ventura College
2007-2009
The neurobiological mechanisms underlying the induction and remission of depressive episodes over time are not well understood. Through repeated longitudinal imaging medial prefrontal microcircuits in living brain, we found that spinogenesis plays a critical role sustaining specific antidepressant behavioral effects maintaining long-term remission. Depression-related behavior was associated with targeted, branch-specific elimination postsynaptic dendritic spines on projection neurons....
Motivation for reward drives adaptive behaviors, whereas impairment of perception and experience (anhedonia) can contribute to psychiatric diseases, including depression schizophrenia. We sought test the hypothesis that medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions govern hedonic responses. By using optogenetic functional magnetic resonance imaging locally manipulate but globally visualize neural activity in rats, we found dopamine neuron...
Light field microscopy is a new technique for high-speed volumetric imaging of weakly scattering or fluorescent specimens.It employs an array microlenses to trade off spatial resolution against angular resolution, thereby allowing 4-D light be captured using single photographic exposure without the need scanning.The recorded can then used computationally reconstruct full volume.In this paper, we present optical model based on wave optics, instead previously reported ray optics models.We also...
Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional "mass univariate" techniques that fit data one voxel at a time. In the functional magnetic resonance imaging (fMRI) literature, this has led broad application of "off-the-shelf" classification and regression methods. These generic approaches allow investigators use ready-made algorithms accurately decode perceptual, cognitive, or behavioral states from distributed...
Whole-brain recordings give us a global perspective of the brain in action. In this study, we describe method using light field microscopy to record near-whole calcium and voltage activity at high speed behaving adult flies. We first obtained maps for various stimuli behaviors. Notably, found that increased on scale when fly walked but not it groomed. This increase with walking was particularly strong dopamine neurons. Second, extracted spatially distinct sources as well their time series...
Light field microscopy has been proposed as a new high-speed volumetric computational imaging method that enables reconstruction of 3-D volumes from captured projections the 4-D light field. Recently, detailed physical optics model microscope derived, which led to development deconvolution algorithm reconstructs with high spatial resolution. However, resolution reconstructions shown be non-uniform across depth, some z planes showing and others, particularly at center imaged volume, very low...
Variables in many big-data settings are structured, arising, for example, from measurements on a regular grid as imaging and time series or spatial-temporal climate studies. Classical multivariate techniques ignore these structural relationships often resulting poor performance. We propose generalization of principal components analysis (PCA) that is appropriate massive datasets with structured variables known two-way dependencies. By finding the best low-rank approximation data respect to...
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Despite growing interest in applying machine learning to neuroimaging analyses, few studies have gone beyond classifying sensory input directly predicting behavioral output. With spatial resolution on the order of millimeters and temporal seconds, functional magnetic resonance imaging (fMRI) is a promising technology for such applications. However, fMRI data's low signal-to-noise ratio, high...
In compressed sensing, one takes samples of an N -dimensional vector using matrix A , obtaining undersampled measurements . For random matrices with independent standard Gaussian entries, it is known that, when k -sparse, there a precisely determined phase transition: for certain region in the ( )-phase diagram, convex optimization typically finds sparsest solution, whereas outside that region, fails. It has been shown empirically same property—with transition location—holds wide range...