of Neuronal Morphology &Connectivity This continuing project is directed at describing neuroanatomical structure in a compact yet sufficiently complete fashion to allow the implementation of biologically plausible and quantitatively accurate computer simulations. Neuronal morphology plays a fundamental role in physiological and pathological brain function by integrating complex patterns of synaptic inputs, transmitting trains of spiking output, and subserving network connectivity. During the previous funding periods (under Generation and Description of Dendritic Morphology), informatics tools were successfully designed and deployed to reproduce the three-dimensional shape of dendritic trees in the same format used to represent experimentally reconstructed neurons. Digital arbors were also combined with computational models of membrane biophysics to investigate the cellular structure-activity relationship. The goal of this application is to expand these software resources and research approach from dendrites to all aspects of neuronal structure, including full axonal arborizations and synaptic connectivity. The general strategy is to resample in stochastic models the experimentally measured statistical distributions, and to compare the resulting simulations directly to the original data. Such comprehensive and parsimonious characterization constitutes an effective way to compress, store, exchange, and amplify extremely complex neuroanatomical information. The project has three logically related, but technically independent specific aims.
The first aim i s to enhance the power and usability of computational neuroanatomy tools for the analysis and synthesis of neuronal morphology, and to integrate them with leading bioinformatics algorithms enabling large scale knowledge mining of massive data sets. In the second aim, digital reconstruction, quantitative morphometry, and compartmental modeling of branch growth and spike propagation are applied to two distinct classes of axonal arbors, namely hippocampal CA3 interneurons and olivo-cerebellar climbing fibers.
The third aim, extending to circuitry, develops a relational database of cellular-level connectivity in the rodent hippocampus. In this framework, population statistics for each neuronal class are stochastically resampled to quantify the network structure-activity relationship. The neurobiological and technological components of this project are deeply intertwined and span a variety of scientific approaches, including microscopic imaging, computational simulations, statistical analysis and data mining. The robust development and open source distribution of the underlying neuroinformatics infrastructure for data handling and integration will continue to benefit the wider neuroscience community.

Public Health Relevance

Generation and Description of Neuronal Morphology &Connectivity Brain connectivity and the intricate tree-like shape of individual nerve cells underlie cognitive and physiological functions, and are dramatically altered in almost all known neurological disorders. Using state-of-the-art imaging, statistical analysis, and computational modeling, this project will quantify and synthesize a massive amount of complex neuroanatomical information to investigate the relationship between architecture and function in the nervous system. To maximize impact on the research community, powerful bioinformatics tools and databases will be developed, professionally documented, and freely distributed online for the long lasting benefit of scientific advancement and public health.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS039600-13
Application #
8254433
Study Section
Special Emphasis Panel (ZRG1-ETTN-F (03))
Program Officer
Liu, Yuan
Project Start
1999-08-01
Project End
2014-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
13
Fiscal Year
2012
Total Cost
$297,279
Indirect Cost
$82,904
Name
George Mason University
Department
None
Type
Organized Research Units
DUNS #
077817450
City
Fairfax
State
VA
Country
United States
Zip Code
22030
Ascoli, Giorgio A (2014) A community spring for neuroscience data sharing. Neuroinformatics 12:509-11
DeFelipe, Javier; Lopez-Cruz, Pedro L; Benavides-Piccione, Ruth et al. (2013) New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat Rev Neurosci 14:202-16
Sugihara, Izumi; Brown, Kerry M; Ascoli, Giorgio A (2013) New insights on vertebrate olivo-cerebellar climbing fibers from computerized morphological reconstructions. Bioarchitecture 3:38-41
Ferrante, Michele; Migliore, Michele; Ascoli, Giorgio A (2013) Functional impact of dendritic branch-point morphology. J Neurosci 33:2156-65
Parekh, Ruchi; Ascoli, Giorgio A (2013) Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77:1017-38
Martone, Maryann E; Ascoli, Giorgio A (2013) Connecting connectomes. Neuroinformatics 11:389-92
Ropireddy, D; Bachus, S E; Ascoli, G A (2012) Non-homogeneous stereological properties of the rat hippocampus from high-resolution 3D serial reconstruction of thin histological sections. Neuroscience 205:91-111
Baker, John L; Perez-Rosello, Tamara; Migliore, Michele et al. (2011) A computer model of unitary responses from associational/commissural and perforant path synapses in hippocampal CA3 pyramidal cells. J Comput Neurosci 31:137-58
Donohue, Duncan E; Ascoli, Giorgio A (2011) Automated reconstruction of neuronal morphology: an overview. Brain Res Rev 67:94-102
Ropireddy, Deepak; Scorcioni, Ruggero; Lasher, Bonnie et al. (2011) Axonal morphometry of hippocampal pyramidal neurons semi-automatically reconstructed after in vivo labeling in different CA3 locations. Brain Struct Funct 216:1-15

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