We propose a theoretical-experimental program to quantitate seizure activity with neuronal resolution in the intact larval zebrafish central nervous system. Our study is made possible by recent advances in light-sheet microscopy, and theoretical and algorithmic advances in the analysis of large neural imaging datasets. Light-sheet microscopy has both excellent spatial and temporal resolution and is capable of virtually complete volumetric coverage of the larval zebrafish central nervous system. This, along with advanced statistical and computational techniques, allows us to quantify neural dynamics in the zebrafish brain with unprecedented accuracy. Because of the structure of neural circuits, inhibitory neuronal populations typically surround excited regions, protecting the brain from runaway excitatory (ictal) activity that is generated when a seizure forms. However, repeated waves of ictal activity can break down the surround inhibition, allowing a seizure to propagate. With high-resolution microscopy and state-of-the-art computational analysis and simulation methods, we will study how coherent ictal activity generated during seizures interacts with the surround inhibition (often called the 'inhibitory restraint') that is the brain's response to the seizure. A precise understanding of how coherent excitations interact with and depress inhibition in interneuron populations would provide a powerful control paradigm for spatial and temporal intervention in seizure formation and propagation. Furthermore, although computer simulations have been performed using detailed synaptic connectivity reconstructions from anatomical data, simulations derived, then validated in the same organism would be a transformative contribution to the study of seizures and more generally to neuroscience. This study combines the experimental, theoretical, and validation aspects of a neuroscience investigation into a unified whole in the study of a large, intact neuronal network for the first time. Although, for technical reasons, this approach is limited to the larval zebrafish, a small, transparent organism, it could radically improve our understanding of how protective mechanisms in meso-scale neuronal systems can fail in vertebrates. Our proposed study will provide information that could guide future seizure interventions such as neuron transplantation, electrical stimulation, surgical tissue removal or drug targeting of neuronal populations and synapses that most effectively prevent seizure formation and propagation. The education and training of the graduate students and postdocs involved in our program will be integrated with every aspect of the research. Undergraduate students will be involved in the research and mentored. The investigation is multi-institutional and builds on existing interdisciplinary collaborations in engineering, developmental neuroscience, epilepsy and mathematics.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS090645-03
Application #
9045722
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Gnadt, James W
Project Start
2014-07-15
Project End
2019-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of California Davis
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
047120084
City
Davis
State
CA
Country
United States
Zip Code
95618
Hong, SoonGweon; Lee, Philip; Baraban, Scott C et al. (2016) A Novel Long-term, Multi-Channel and Non-invasive Electrophysiology Platform for Zebrafish. Sci Rep 6:28248
Sornborger, Andrew T; Lauderdale, James D (2016) A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data. Conf Rec Asilomar Conf Signals Syst Comput 2016:1056-1060
Sornborger, Andrew T; Wang, Zhuo; Tao, Louis (2015) A mechanism for graded, dynamically routable current propagation in pulse-gated synfire chains and implications for information coding. J Comput Neurosci 39:181-95