2004 — 2008 |
Stepanyants, Armen |
K25Activity Code Description: Undocumented code - click on the grant title for more information. |
Searching For Connectivity Principles in the Brain @ Northeastern University
DESCRIPTION (provided by applicant): Understanding synaptic connectivity principles in the local circuits of mammalian brains is one of the oldest and most important problems in neuroscience. Without clear knowledge of connectivity principles it is virtually impossible to understand how the brain functions or to understand neural circuitry changes, which underlie neurological disorders. The morphology of neuronal arbors holds valuable information about the principles of synaptic connectivity in the brain. Quantitative analysis of neuronal morphology can shed light on a number of important questions: Are there precise wiring mechanisms during development that lead to specific connectivity patterns in the adult brain? What are the structural plasticity potentials for connectivity between different classes of neurons? What are the strategies used by different neuronal classes to find their post-synaptic partners? We are developing a consistent methodology, which will put us in a position to approach these questions in a quantitative manner. We are building a new method for detecting significant spatial correlations between pairs of neuronal arbors reconstructed in three dimensions (3D). This method has potential for identifying specific features of synaptic connectivity that are introduced by precise widening mechanisms during development. In addition, we are introducing a quantitative description of the potential for structural synaptic plasticity for connectivity between different classes of neurons. I propose to study connectivity principles in local circuits of the mammalian brain under the mentorship of Drs. Chklovskii (sponsor) and Svoboda (co-sponsor) at Cold Spring Harbor Laboratory (CSHL). Their laboratories have track record in theoretical and experimental studies of synaptic connectivity. I will work with neuronal pairs and triplets, reconstructed in 3D with Neurolucida in Dr. Chklovskii's laboratory, which will provide a unique opportunity to get insight into the difficult questions of connectivity. This award will give me a chance to focus all my efforts on understanding a very interesting and important biological problem. It will help me enhance my knowledge of experimental and computational neuroscience through hands-on experimental training in CSHL facilities, participation in relevant courses, meetings and conferences, and interaction with leading neuroscience laboratories in the field such as the Chklovskii, Svoboda and Huang laboratories. By the time of the completion of the award I should have the skills and knowledge of experimental techniques necessary to conduct independent research.
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2006 — 2007 |
Menon, Latika [⬀] Stepanyants, Armen O'malley, Donald (co-PI) [⬀] Sridhar, Srinivas (co-PI) [⬀] Dokmeci, Mehmet |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ner: Nano-Biodevices For Reliable, Long-Term Stimulation and Recording of Neural Activity @ Northeastern University
0608892 Menon The goal of this NER application is to develop nano-biodevices for recording and stimulating nerves that are reliable and long lasting. This exploratory proposal addresses some of the critical issues entailed. These issues include gold nano-wire size, cell growth and motion artifacts, and cell viability at the electrode-cell interface. The research plan calls for fabrication of an array of nano-wires in three sizes ranging of 50, 100, and 200 nm. Measurements will be performed by culturing two types of neural (rat hippocampal and human neuroblastoma) cells onto the surface of Au and peptide conjugated Au nano-wires.
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2008 — 2012 |
Stepanyants, Armen |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Automated Reconstruction of Neurites From 3d Microscopy Image Stacks @ Northeastern University
DESCRIPTION (provided by applicant): Currently, accurate methods of analysis of neuron morphology are based on manual or semi-automated tracing systems. Such tracings can be time consuming and/or are prone to errors in situations where faint or beaded neurites diffusely cover large volumes. This is usually the case with tracing axons of cortical pyramidal neurons, e.g. long range horizontal projections. With this proposal we aim to develop a tool which will automate the reconstruction process of neurites from 3D microscopy stacks of images. The existence of such a tool is critical for advancing neural circuits research. As axons of many neuron types can span the entire brain of an animal (e.g. cortical pyramidal cell axons) or the entire animal itself (e.g. C. elegans), our ultimate goal is to perform reconstructions on a large scale to recover axonal and dendritic arbors of sparsely labeled populations of neurons in their entirety. Our algorithm consists of two main parts. First, a 3D stack of images is segmented into regions based on a local watershed type segmentation procedure. For this, preferred orientations are calculated in each voxel of the thresholded stack of images by applying a bank of steerable 3D Gabor filters. Regions are grown by stepping down in intensity and placing edges between adjacent voxels with dissimilar orientations. Second, created regions are merged into larger structures using global optimization criteria. Here, optimal connecting paths are determined for every pair of regions by maximizing the intensity along the path and, at the same time, keeping the path length to a minimum. Regions are merged depending on the intensity and curvature along their optimally connecting paths. The specific aims of this proposal are as follows. Specific Aim 1: We will develop a graphic user interface (GUI) and optimization based algorithm for the semi-automated tracing of neurites from 3D microscopy stacks of images. The algorithm will be based on the gradient ascent method for finding optimal paths which connect user specified seed points. The GUI will provide the user fast and flexible control over the details of the procedure. We will develop this semi-automated reconstruction tool to function as an autonomous unit, but the GUI and the tracing algorithm are also essential parts of the Specific Aim 2. Specific Aim 2: We will develop a segmentation based algorithm for a fully-automated tracing of neurites from 3D microscopy stacks of images. The algorithm will use watershed type segmentation combined with global optimization based criteria for merging the segmented regions. The fully-automated algorithm will be implemented in the GUI and will utilize methods developed as part of the Specific Aim 1. Specific Aim 3: We will complete the reconstruction process by automatically detecting neuron cell bodies, branching structure, axonal boutons, and dendritic spines. The GUI will provide an opportunity to correct possible errors by connecting and disconnecting branches, removing and adding branches, spines, and boutons. Simple morphometric functions, such as the calculation of length and numbers of boutons and spines, will be implemented as well. Currently, accurate methods of quantitative analysis of neuron morphology and synaptic connectivity are based on manual or semi-automated tracing tools which are time consuming and can be prone to errors. With this proposal we aim to develop a tool that will fully-automate the reconstruction process of neurites from 3D microscopy stacks of images. The existence of such a tool is critical for advancing basic neural circuits research and understanding changes in the central nervous system which underlie its disease state.
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2015 — 2018 |
Stepanyants, Armen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ri: Small: Theory of Robust Learning Based On the Structure and Function of the Cortical Column @ Northeastern University
How the brain learns, and in the process modifies its synaptic connectivity, remains one of the greatest mysteries of modern science. The objective of this project is to uncover the effects of robust associative learning and long-term memory storage on synaptic connectivity, thus creating the basis for quantitative analyses of these fundamental brain functions. The investigator proposes to develop a biologically realistic model of robust associative learning by cortical circuits. The model will be derived from a single hypothesis, according to which synaptic connectivity in a given circuit of adult cortex is functioning in a steady-state. In such a state the associative memory storage capacity of the circuit is maximal, and learning new associations is accompanied with forgetting some of the old ones.
The model will integrate current knowledge of excitatory and inhibitory neuron classes, with structural connectivity constraints imposed by the morphologies of axonal and dendritic arbors of cortical neurons, with homeostatic constraints on numbers and strengths of synaptic connections. It is proposed to simulate steady-state learning based on one of the best studied networks in the mammalian neocortex - the barrel-centered column of rodent somatosensory cortex. The simulations will be imbedded in the structural connectivity of the column, built from the morphologies of neurons reconstructed in three-dimensions from various cortical depths. Salient features of steady-state circuits will be validated against a large dataset of experimental studies reporting probabilities of connections between neurons, probabilities of specific higher-order connectivity motifs, distributions of unitary postsynaptic potentials, as well as relative strengths of laminar and inter-laminar projections in rodent barrel cortex. The dataset will be created as part of the project and will encompass connectivity of major excitatory and inhibitory cell classes present in all cortical layers. The proposed research is rooted in the basic principles of statistical learning and will advance the state of the art in theoretical and computational modeling of cognitive functions with basic neuroscience and computational intelligence applications.
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2015 — 2019 |
Stepanyants, Armen |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Software For Automated Reconstruction of Structure & Dynamics of Neural Circuits @ Northeastern University
? DESCRIPTION (provided by applicant): Our understanding of brain functions is hindered by the lack of detailed knowledge of synaptic connectivity in the underlying neural network. While synaptic connectivity of small neural circuits can be determined with electron microscopy, studies of connectivity on a larger scale, e.g. whole mouse brain, must be based on light microscopy imaging. It is now possible to fluorescently label subsets of neurons in vivo and image their axonal and dendritic arbors in 3D from multiple brain tissue sections. The overwhelming remaining challenge is neurite tracing, which must be done automatically due to the high-throughput nature of the problem. Currently, there are no automated tools that have the capacity to perform tracing tasks on the scale of mammalian neural circuits. Needless to say, the existence of such a tool is critical both for basic mapping of synaptic connectivity in normal brains, as well as for describing the changes in the nervous system which underlie neurological disorders. With this proposal we plan to continue the development of Neural Circuit Tracer - software for accurate, automated reconstruction of the structure and dynamics of neurites from 3D light microscopy stacks of images. Our goal is to revolutionize the existing functionalities of the software, making it possible to: (i) automatically reconstruct axonal and dendritic arbors of sparsely labeled populations of neurons from multiple stacks of images and (ii) automatically track and quantify changes in the structures of presynaptic boutons and dendritic spines imaged over time. We propose to utilize the latest machine learning and image processing techniques to develop multi-stack tracing, feature detection, and computer-guided trace editing capabilities of the software. All tools and datasets created as part of this proposal will be made available to the research community.
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