Dendritic and axonal morphologies play fundamental roles in physiological brain function and pathological dysfunction by affecting synaptic integration, spike train transmission, and circuit connectivity. Incorporating existing and forthcoming experimental data into accurate, full-scale, and biologically plausible neural network simulations is important for quantitatively bridging the sub-cellular and systems-levels. We successfully designed, implemented, and freely distributed to the community computer software and databases to reconstruct, analyze, visualize, simulate, and share the 3D tree-like shape of neurons from many labeling and visualization techniques, developmental stages, and experimental conditions. We imaged by light microscopy, digitally traced, and shared new data, and we provided our peers with the electronic means of freely doing the same. Moreover, we combined those data with computational models of membrane biophysics to investigate the neuronal structure-activity relationship. We propose to expand this research approach with two specific aims. The first is to augment the power, scope, and usability of the NeuroMorpho.Org repository of digital tracings. We plan to triple the number of shared reconstructions, adding new species, brain regions, and neuron types. Moreover, we will enhance the search functionality with a "semantic" engine using state-of-the-art ontologies. We will also extend the domain and format of distributed data to include circuitry, multi-channel information, and temporal sequences.
The second aim i s to develop a new knowledge base of neuron types in the hippocampus and entorhinal cortex by quantifying their morphological, physiological, and molecular properties from published reports. The hippocampal formation is one of the most studied brain regions, underlies autobiographic memory storage and spatial representation, and is prominently involved in devastating neurological disease, including epilepsy and Alzheimer's. Yet, our conceptual understanding of how the hippocampus works is limited compared to the wealth of available knowledge about its neurons, because it is difficult to find and integrate all relevant data scattered in thousands of papers. We will identify all published information and annotate it with specific pointers to the source documents in the peer- reviewed literature. The resulting open-source portal (Hippocampome.Org) will enable the derivation of potential circuit connectivity and the predictive simulation of network-wide spiking activity. We will make this application especially relevant to neuropathology by linking specific neuron types to diseases involving the hippocampus, and demonstrate its potential with a new model of learning disabilities based on impaired structural plasticity.

Public Health Relevance

Generation and Description of Neuronal Morphology &Connectivity Brain connectivity and the intricate tree-like shape of individual nerve cells underli cognitive and physiological functions, and are dramatically altered in almost all known neurological disorders. Using state-of-the-art information technology, statistical analysis, and computational modeling, this project will quantify, synthesize, and freely share a massive amount of complex neuroscience information on (1) neuronal morphology in general and (2) all major properties of every known neuron types in an exemplary brain region. These developments will allow us to create detailed, biologically plausible, full-scale quantitatively predictive models of cognitive function and neurological disease. The impact on the research community will be multiplied by public distribution of both simulation and program codes as well as all underlying data collected for the long lasting benefit of scientific advancement and public health.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (NOIT)
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Liu, Yuan
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George Mason University
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United States
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Ascoli, Giorgio A (2016) On the Data-Driven Road from Neurology to Neuronomy. Neuroinformatics 14:251-2
Ascoli, Giorgio A; Wheeler, Diek W (2016) In search of a periodic table of the neurons: Axonal-dendritic circuitry as the organizing principle: Patterns of axons and dendrites within distinct anatomical parcels provide the blueprint for circuit-based neuronal classification. Bioessays 38:969-76
Gillette, Todd A; Hosseini, Parsa; Ascoli, Giorgio A (2015) Topological characterization of neuronal arbor morphology via sequence representation: II--global alignment. BMC Bioinformatics 16:209
Parekh, Ruchi; Armañanzas, Rubén; Ascoli, Giorgio A (2015) The importance of metadata to assess information content in digital reconstructions of neuronal morphology. Cell Tissue Res 360:121-7
Ascoli, Giorgio A (2015) Sharing Neuron Data: Carrots, Sticks, and Digital Records. PLoS Biol 13:e1002275
Nanda, Sumit; Allaham, M Mowafak; Bergamino, Maurizio et al. (2015) Doubling up on the fly: NeuroMorpho.Org Meets Big Data. Neuroinformatics 13:127-9
Armañanzas, Rubén; Ascoli, Giorgio A (2015) Towards the automatic classification of neurons. Trends Neurosci 38:307-18
Mainetti, Matteo; Ascoli, Giorgio A (2015) A neural mechanism for background information-gated learning based on axonal-dendritic overlaps. PLoS Comput Biol 11:e1004155
Wheeler, Diek W; White, Charise M; Rees, Christopher L et al. (2015) a knowledge base of neuron types in the rodent hippocampus. Elife 4:
Peng, Hanchuan; Meijering, Erik; Ascoli, Giorgio A (2015) From DIADEM to BigNeuron. Neuroinformatics 13:259-60

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