Topic: light sheet microscopy

The Allen Institute has launched a new moonshot initiative on human brain health and disease called the Brain Health accelerator. This initiative aims to dramatically accelerate our understanding of human brain structure and function, identify the molecular, cellular and circuit basis of disease progression, and pioneer new therapeutic strategies targeting vulnerable and affected cell types. Our mission focuses on taking a human-centric approach to understanding and treating disease, combining a large-scale open science discovery approach across multiple diseases, AI-based disease modeling, an

Automated axon tracing via fully supervised learning requires annotated 3D brain imagery, which necessitates expertise and labor to produce. Reducing the amount of annotated training data needed for supervised algorithms for axon tracing would expedite critical brain research. In this work, we developed and integrated a self-supervised learning (SSL) algorithm trained on large unannotated data volumes into NeuroTrALE (pictured), a custom AI-driven pipeline for automatic annotation and visualization of 3D brain imagery. Our light sheet microscopy dataset consisted of stained Parvalbumin positiv