Physiopy: a Python suite for handling physiological data recorded in neuroimaging settings

COEX 

Functional Magnetic Resonance Imaging (fMRI), a pivotal tool for neuroscientific research, leverages blood oxygenation levels to infer neural activity. However, its reliance on hemodynamic responses also renders it sensitive to various physiological processes affecting blood oxygenation. This dual nature presents both challenges and opportunities: while these physiological factors can introduce confounds in interpreting neural signals [1], they simultaneously offer valuable insights into essential human functions encompassing cognition, emotion, and health [2-4]. To this end, we underscore the necessity of acquiring concurrent physiological data such as cardiac and respiratory activity, gas exchange metrics (O2/CO2 levels), and skin conductance. Adoption of concurrent physiological signals is growing within the neuroimaging community, reflecting a broader appreciation of physiological dynamics in brain imaging studies. Emphasizing the critical role of physiological monitoring in fMRI data quality, physiopy is a dynamic, collaborative initiative designed to streamline the integration of physiological data with fMRI research. The foundation of physiopy rests on four key pillars: (1) Accessible Software Suite: Offering a range of user-friendly software tools specifically tailored for efficient physiological data processing, (2) Comprehensive Documentation: Ensuring clarity and ease of use through detailed guides and instructional materials, (3) Community-Driven Practices: Fostering a culture of shared knowledge and collaborative development of best practices, and (4) Engaged Community: Cultivating an active network of users, developers, and researchers, all united by a shared interest in the integration of physiology within neuroimaging research.