The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists.
Mariah L KenneyIaroslavna VasylievaGreg HoodIvan Cao-BergLuke TuiteRozita LaghaeiMegan C SmithAlan M WatsonAlexander J RopelewskiPublished in: bioRxiv : the preprint server for biology (2024)
Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.
Keyphrases
- resting state
- white matter
- high resolution
- healthcare
- electronic health record
- single cell
- mental health
- functional connectivity
- cerebral ischemia
- big data
- deep learning
- cell therapy
- high throughput
- stem cells
- small molecule
- single molecule
- machine learning
- multiple sclerosis
- emergency department
- optical coherence tomography
- data analysis
- high speed
- photodynamic therapy
- wastewater treatment
- quality improvement
- social media
- adverse drug
- label free