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For decades, scientists have tried to understand why certain works of art move us more than others. Yet, directly connecting what is in an image to how it makes us feel has proven elusive. Since shapes and visual patterns play such a central role in both art and how we see the world, the authors explored a new angle: using advanced mathematical tools from topology to reveal hidden structures in images.

Macroscale brain activity can be captured using techniques like EEG and fMRI. However, the granular or more detailed activity of neurons and localized neural masses is inaccessible. A solution is the use of whole-brain models, as they can simulate EEG and fMRI recordings from mathematical equations and can be fit to empirical data. 

Cell type annotation in single-cell RNA sequencing is a critical bottleneck, with existing automated methods facing limitations in accuracy, interpretability, and generalization to novel cell types. Although Large Language Models (LLMs) have recently shown potential in single-cell annotation, they are prone to inherent “hallucinations”.