CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution

TL;DR

CytoNet is a foundation model trained on extensive histological data that encodes cellular patterns in the human cerebral cortex, enabling scalable analysis and linking microarchitecture to brain function.

Contribution

Introduces CytoNet, a novel foundation model for analyzing human cortical microarchitecture using self-supervised learning on large-scale histological images.

Findings

Supports cortical area classification and laminar segmentation

Quantifies microarchitectural variation across brains

Links cellular architecture to functional network organization

Abstract

Studying the cellular architecture of the human cerebral cortex is critical for understanding brain organization and function. It requires investigating complex texture patterns in histological images, yet automatic methods that scale across whole brains are still lacking. Here we introduce CytoNet, a foundation model trained on 1 million unlabeled microscopic image patches from over 4,000 histological sections spanning ten postmortem human brains. Using co-localization in the cortical sheet for self-supervision, CytoNet encodes complex cellular patterns into expressive and anatomically meaningful feature representations. CytoNet supports multiple downstream applications, including area classification, laminar segmentation, quantification of microarchitectural variation, and data-driven mapping of previously uncharted areas. In addition, CytoNet captures microarchitectural signatures of macroscale functional organization, enabling decoding of functional network parcellations from cytoarchitectonic features. Together, these results establish CytoNet as a unified framework for scalable analysis of cortical microarchitecture and for linking cellular architecture to structure-function organization in the human cerebral cortex.