NCS-FO: Empowering Data-Driven Hypothesis Generation for Scalable Connectomics Analysis

The field of connectomics aims to reconstruct the wiring diagram of neurons and synapses at nanometer resolution to enable new insights into the workings of the brain. Recent advances in image acquisition and machine learning methods have yielded complete reconstructions of neural connectivity of large tissue samples. The investigators have one such dataset from a human brain tissue consisting of two petabytes of raw image data from electron microscopy.

In collaboration with Google, they have spent the past two years reconstructing the complete 3D shape of about 50,000 cells, including 18,000 neurons, and identifying about 133 million synapses. This data will enable them to examine the prototypes of various neuron shapes, the correlations between these neuron types and their internal structures, and how they are connected to each other.

This will be done in a dataset that is orders of magnitude larger than previous brain samples. These dense brain reconstruction results come with complex spatial and network structures, posing new challenges for scientists who wish to explore and analyze such data. The proposed program will develop a scalable visual analytics system that allows researchers to generate novel data-driven hypotheses from the petabyte-scale connectomics data.

This three-year project aims to build novel visual analytics tools and efficient deep learning methods to advance the field of connectomics. Project deliverables will empower neuroscientists to analyze large brain networks in a one cubic millimeter volume containing tens of thousands of neurons and hundreds of millions of synaptic connections. The project aims to analyze the brain at the neuron level and network level.

It will investigate scalable visual analytics methods for the comparison of morphological features and analysis of spatial distributions and proximity of cell organelles. The network-level analysis will be supported, from local synaptic network motifs to larger-scale connectivity patterns of different cortical layers. A tightly integrated targeted proofreading/analysis loop will be developed, using techniques from machine learning for automatic error suggestion and guidance of the proofreading process to obtain high-quality data with minimal user interaction.

To support intuitive hypothesis generation based on the data-driven visual analysis, an intuitive domain-specific query framework and investigate methods for automatic user guidance and hypothesis suggestion will be designed. Ultimately, this project will provide data and analysis tools to develop new theories of how the brain works.

This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.