In recent years, an explosion of imaging technology has brought us tantalizingly close to achieving the goal of imaging brain function with single neuron resolution: Using labeling techniques such as expressed reporters and bulk dye loading combined with imaging methods such as fast frame CCD, photodiode array, and traditional as well as multiphoton confocal imaging, we can now visualize complicated neural interactions at the level of cell culture, the brain slice preparation and even the intact animal, with single cell resolution. In our own work, we have found that this same technology paired with modern computers has resulted in the ability to collect masses of information far more quickly than we can process and understand it using standard workflow models. Additionally, there is a pressing need to adopt the systems biology strategy of automated database storage and classification of analysis results. We propose to address these and other issues with a focused technology development proposal which adreses the problem/need based criteria addressed by the Neurotechnology Research, Development and Enhancement Program. The PI, with an experimental and computational modeling background, and the co-PI, with a neural imaging data analysis background, bring complementary skills to this project. We will develop a common framework for data collection, meta-tagging and storage of image based time series. We will develop and implement algorithms that will allow for the on-line calculation of approximate or exact principal components and multivariate spectral characteristics during experimental manipulations. We will implement Canonical Correlation Analysis (CCA) and other techniques for the comparison of multiple datasets. By implementing multivariate methods for the analysis of ratiometric imaging data, we will lay the groundwork for extending these multivariate techniques and tools to full optical spectrum based-data collection. .
Increasingly, physiological imaging is helping us to understand how the brain works in health and disease. This work is relevant to public health because it will provide necessary tools for making physiological imaging more efficient and sensitive. In addition, it wil pave the way for using physiological imaging as a high-throughput tool.
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