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
- mental health
- healthcare
- electronic health record
- cerebral ischemia
- single cell
- functional connectivity
- big data
- deep learning
- high throughput
- small molecule
- machine learning
- stem cells
- quality improvement
- single molecule
- rna seq
- social media
- climate change
- data analysis
- mesenchymal stem cells
- brain injury
- multiple sclerosis
- optical coherence tomography
- blood brain barrier
- subarachnoid hemorrhage
- health information
- artificial intelligence
- high density
- fluorescence imaging
- photodynamic therapy