A unified approach for identifying PET-based neuronal activation and molecular connectivity with the functional PET toolbox.

Abstract

Functional PET (fPET) identifies stimulation-specific changes of physiological processes, individual molecular connectivity and group-level molecular covariance. Since there is currently no consistent analysis approach available for these techniques, we present a toolbox for unified fPET assessment. The toolbox supports analysis of data obtained with a variety of radiotracers, scanners, experimental protocols, cognitive tasks and species. It includes general linear model (GLM)-based assessment of task-specific effects, percent signal change and absolute quantification, and data-driven independent component analysis (ICA). It allows computation of molecular connectivity via temporal correlations of PET signals and molecular covariance as between-subject covariance using static images. Toolbox performance was evaluated by comparison to previous results obtained using established protocols, demonstrating strong agreement (r = 0.91-0.99). Stimulation-induced changes in metabolism ([¹⁸F]FDG) and neurotransmitter dynamics (6-[¹⁸F]FDOPA, [¹¹C]AMT) were detected across different cognitive tasks. Molecular connectivity demonstrated metabolic interactions between networks, whereas group-level covariance highlighted interhemispheric relationships. These results underscore the toolbox's flexibility in capturing dynamic molecular processes. The toolbox offers a comprehensive, reproducible, user-friendly approach for analyzing fPET data across various experimental settings. This facilitates sharing of analyses pipelines and comparison across centres to advance the study of brain metabolism and neurotransmitter dynamics in health and disease.