The ability to adjust dynamically to attain stability in the face of widely ranging internal changes and external insults is a feature observed commonly in natural systems. The human brain, for example, has an amazing capacity to functionally recover from strokes that caused damages to local neuronal circuitries. Despite their scientific and social values, little is known about the principles of such highly adaptive systems. However, recent advances in imaging and computational technologies are practically ripe for visualizing and processing the small insect brain in its entirety, down to the level of individual synaptic connectivity. The objective of this project is the image-based computational modeling of how synaptic connectivity is established in vivo during brain development, a major question in neuroscience today. Using imagery acquired with state-of-the-art imaging techniques (Brainbow and MARCM) at two Drosophila neuroscience laboratories, the goal is to estimate and pattern the complete morphology, connectivity properties and structure dynamics of single neurons and neuronal circuits in the Drosophila larvae. This research effort aims at setting the principle for large-scale studies of more complex brains at single-cell resolution, and modeling adaptive responses of neuronal circuits to changes such as aging, disease and injury.
The overreaching hypothesis of this research is that the brain is a highly adaptive system defined by specific structure and dynamics, as a whole and at the single cell level. Although this is a fundamental hypothesis, it has been difficult to test using live animals, quantitatively through numerical modeling, or even qualitatively through observation. By bringing together leading-edge imaging technologies and computationally intensive image analytics, this project initiates the pursuit of what makes the brain function as a whole throughout life and continuously adapt to various changes such as aging, disease, drug treatment, and injury. The research plan includes training a number of graduate, undergraduate and K12 students, integrating cutting-edge research in Computer Science and Computational Neuroscience with education. All findings, methods, developed algorithms (open source), publications, and data will be publicly available at the project website: http://neurovision.cs.iupui.edu/