The NIH BRAIN Initiative and the DOE Office of Science seek input from stakeholders in the extramural community. Responses to the workshop questions are voluntary and, if preferred, may be submitted anonymously via email@example.com. Please do not include any information that you do not wish to make public. Proprietary, classified, confidential, or sensitive information should not be included in your response.
The input sought includes, but is not limited to, any of the following key workshop questions:
Workshop 1: Significance of mapping complete neural circuits
- What issues would you be able to address if you had detailed connectome data that cannot currently be investigated?
- Are there conceptual issues that would be approached entirely differently if detailed connectomic data were available?
- What would be the value added between having one versus many samples (e.g., individuals, conditions, species)?
Workshop 2: Sample preparation in mammalian whole-brain connectomics
- What are key issues to be resolved in sample preparation for whole mouse brain EM connectomes in conjunction with different imaging platforms?
- What are key issues to be resolved in sample preparation using complementary imaging at lower resolution in mouse brains and larger brains?
- What are the limitations, potentials and greatest opportunities in moving to larger brains?
Workshop 3: Experimental modalities for whole-brain connectivity mapping
- In your opinion, what resolution is required to attain meaningful wiring models?
- Given the wide variety of imaging technologies currently available, which technologies could be driven to scale and most easily disseminated?
- What would be the most optimal way to encode dynamics in static brain maps?
Workshop 4: Connectome generation and data pipelines
- What are the prospects and challenges you see in scaling analysis goals to volumes comprising hundreds of petavoxels of imaging data?
- What kinds of improvements in technical infrastructure (hardware and/or software) would most dramatically aid in scaling analysis?
- What new frontiers of analysis enabled by a whole mammalian connectome excite you most? What challenges do you see in pursuing those frontiers?
Workshop 5: Optimizing the use of connectomic data to drive data science and scientific discovery
- What new types of analyses (not developed yet) are needed to make progress and enable scientific breakthroughs?
- What would be the most optimal way to integrate analyses across scales? Or connectomic data with functional data?
- What is needed to make meaningful comparisons between connectomes? Or to make connectomic data and analyses widely available?