Mozak: Creating an Expert Community to accelerate neuronal reconstruction at scale

项目总结

Project Summary This project aims to leverage the best of both computational and human expertise in neuronal reconstruction towards the goal of accelerating global neuroscience discovery from internationally-sourced imaging data. We propose to create a cloud-based unified platform for converging 3-dimensional images of neurons onto a single analysis platform to (1) train and grow a new expert community of global reconstructors to work across the data from these groups, to (2) generate a community-sourced neuronal reconstruction database of open imaging data that can be incorporated into a 3-dimensional map of neuronal interconnectivity - onto which (3) novel annotations and more complex functional and molecular data can be overlaid. Our approach will evolve with the growing needs of the neuroscience community over time. To do this, in Aim One (Neuronal Reconstruction at Scale), we will test if the newly developed crowd-sourced game-based platform Mozak can develop a collective of new human experts at scale, capable of accelerating the rate of current reconstruction by at least an order of magnitude, at the same time as increasing the robustness, quality and unbiasedness of the final reconstructions. In Aim Two (Robust Multi-Purpose Annotation), we will enhance basic neuronal reconstruction by adding specific semantic annotation— including soma volume and morphological quantification, volumetric analysis, and ongoing features (e.g. dendritic spines, axonal varicosities) requested from the neuroscience community. Experienced and high-ranking members will be given the opportunity to advance through increasingly complex neurons into full arbor brain-wide neuronal projections and multiple clustered groups of neurons in localized circuits. Finally, in Aim 3 (Creation of a Research-Adaptive Data Repository), we aim to develop a database of neuronal images reconstructed using the Mozak interface that will directly serve the general and specific needs of different research groups. Our goal is to make this database dynamically adaptive — as new research questions will invariably bring new needs for additional annotations and cross-referencing with other data modalities. This highquality unbiased processing repository will also be perfectly suited for training sets for automated algorithms, and the generation of a 3-dimensional maps such as Allen Institute for Brain Science (AIBS) common coordinate framework. We expect that the computational reconstruction methods will further improve with the new large corpus of “gold standard” reconstructions. Collectively, the completion of these three aims will create an analysis suite as well as an online community of experts capable of performing in depth analysis of large-scale datasets that will significantly accelerate neuroscience research, enhance machine learning for reconstruction analysis, and create a common platform of baseline neuronal morphology data against which aberrantly functioning neurons can be analyzed.