Purpose: Functional PET (fPET) enables the identification of stimulation-specific changes of various physiological processes (e.g., glucose metabolism, neurotransmitter synthesis) as well as computation of individual molecular connectivity and group-level molecular covariance. However, currently no consistent analysis approach is available for these techniques. We present a versatile, freely available toolbox designed for the analysis of fPET data, thereby filling a gap in the assessment of neuroimaging data. Methods: The fPET toolbox supports analyses for 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, as well as independent component analysis (ICA) for data-driven analyses. Furthermore, it allows computation of molecular connectivity via temporal correlations of PET signals between regions and molecular covariance as between-subject covariance using static images. Results: Toolbox performance was validated by analysis protocols established in previous work. Stimulation-induced changes in [18F]FDG metabolic demands and neurotransmitter dynamics obtained with 6-[18F]FDOPA and [11C]AMT were robustly detected across different cognitive tasks. Molecular connectivity analysis demonstrated metabolic interactions between different networks, whereas group-level covariance analysis highlighted interhemispheric relationships. These results underscore the flexibility of fPET in capturing dynamic molecular processes. Conclusions: The toolbox offers a comprehensive, unified and user-friendly platform for analyzing fPET data across a variety of experimental settings. It provides a reproducible analysis approach, which in turn facilitates sharing of analyses pipelines and comparison across centers to advance the study of brain metabolism and neurotransmitter dynamics in health and disease.
