Hahn Andreas, Reed Murray B, Milz Christian, Falb Pia, Murgaš Matej, Lanzenberger Rupert
Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
J Cereb Blood Flow Metab. 2025 Sep 8:271678X251370831. doi: 10.1177/0271678X251370831.
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 ( = 0.91-0.99). Stimulation-induced changes in metabolism ([F]FDG) and neurotransmitter dynamics (6-[F]FDOPA, [C]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.
功能正电子发射断层扫描（fPET）可识别生理过程、个体分子连接性和组水平分子协方差的刺激特异性变化。由于目前尚无适用于这些技术的一致分析方法，我们提出了一个用于统一fPET评估的工具箱。该工具箱支持对使用各种放射性示踪剂、扫描仪、实验方案、认知任务和物种获得的数据进行分析。它包括基于通用线性模型（GLM）的任务特异性效应评估、信号变化百分比和绝对定量，以及数据驱动的独立成分分析（ICA）。它允许通过PET信号的时间相关性计算分子连接性，并使用静态图像计算分子协方差作为受试者间协方差。通过与使用既定方案获得的先前结果进行比较来评估工具箱性能，结果显示出高度一致性（=0.91-0.99）。在不同的认知任务中检测到了刺激诱导的代谢变化（[F]FDG）和神经递质动力学变化（6-[F]FDOPA，[C]AMT）。分子连接性显示了网络之间的代谢相互作用，而组水平协方差突出了半球间关系。这些结果强调了该工具箱在捕捉动态分子过程方面的灵活性。该工具箱为跨各种实验设置分析fPET数据提供了一种全面、可重复、用户友好的方法。这有助于分析流程的共享以及跨中心的比较，以推进健康和疾病状态下脑代谢和神经递质动力学的研究。
