CytoNet: A Foundation Model for the Human Cerebral Cortex
This provides a consistent framework for neuroscientists to study cortical microarchitecture, enabling diverse investigations into brain organization and function.
The paper tackles the problem of analyzing the human cerebral cortex's cellular architecture by introducing CytoNet, a foundation model that uses self-supervised learning on high-resolution microscopic images to generate expressive feature representations, achieving top-tier performance in tasks like cortical area classification and layer segmentation.
To study how the human brain works, we need to explore the organization of the cerebral cortex and its detailed cellular architecture. We introduce CytoNet, a foundation model that encodes high-resolution microscopic image patches of the cerebral cortex into highly expressive feature representations, enabling comprehensive brain analyses. CytoNet employs self-supervised learning using spatial proximity as a powerful training signal, without requiring manual labelling. The resulting features are anatomically sound and biologically relevant. They encode general aspects of cortical architecture and unique brain-specific traits. We demonstrate top-tier performance in tasks such as cortical area classification, cortical layer segmentation, cell morphology estimation, and unsupervised brain region mapping. As a foundation model, CytoNet offers a consistent framework for studying cortical microarchitecture, supporting analyses of its relationship with other structural and functional brain features, and paving the way for diverse neuroscientific investigations.