Information processing pathway maps — A scalable framework for mapping cortical processing

Abstract

Cortical processing in the human brain is highly complex, and researchers have long faced challenges in describing and sharing formal accounts of it in an intuitive yet rigorous manner. Traditional mathematical representations, while precise, often obscure the underlying concepts, whereas narrative descriptions lack the necessary detail. Information Processing Pathway Maps (IPPMs) bridge this gap by providing a clear and flexible way to represent neural processing that maintains mathematical accuracy. These maps can be generated directly from neuroimaging data such as electroencephalography (EEG) and magnetoencephalography (MEG), making them a scalable tool for mapping brain processes. They are also theory-agnostic, making them applicable across various mathematical frameworks of neuronal processing. Recent advances in neuroimaging techniques have significantly improved the efficiency of IPPM creation and expanded their coverage. This paper presents an introductory overview of the IPPM framework, its interpretability, methods for generation, breadth of coverage, and potential applications in both research and clinical settings. We conclude with a discussion on their limitations and suggest promising avenues for future research.