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Journal of Cerebral Blood Flow & MetabolismNeurosciencePeer-reviewed journal article

A Unified Approach for Identifying PET-based Neuronal Activation and Molecular Connectivity with the functional PET toolbox

Andreas HahniD, Murray Bruce ReediD, Christian MilziD, Pia FalbiD, Matej MurgašiD, Rupert R. LanzenbergeriD

PDFDOIPreprint
Citations
10
FWCI
5.78
Field percentile
top 3%

Abstract

PurposeFunctional 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. MethodsThe 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. ResultsToolbox 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. ConclusionsThe 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.

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