Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia; Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Victoria, Australia.
Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia.
Neuroimage. 2020 Jun;213:116720. doi: 10.1016/j.neuroimage.2020.116720. Epub 2020 Mar 8.
Functional positron emission tomography (fPET) is a neuroimaging method involving continuous infusion of 18-F-fluorodeoxyglucose (FDG) radiotracer during the course of a PET examination. Compared with the conventional bolus administration of FDG in a static PET scan, which provides an average glucose uptake into the brain over an extended period of up to 30 min, fPET offers a significantly higher temporal resolution to study the dynamics of glucose uptake. Several earlier studies have applied fPET to investigate brain FDG uptake and study its relationship with functional magnetic resonance imaging (fMRI). However, due to the unique characteristics of fPET signals, modelling of the fPET signal is a complex task and poses challenges for accurate interpretation of the results from fPET experiments. This study applied independent component analysis (ICA) to analyse resting state fPET data, and to compare the performance of ICA and the general linear model (GLM) for estimation of brain activation in response to tasks. The fPET signal characteristics were compared using GLM and ICA methods to model fPET data from a visual activation experiment. Our aim was to evaluate GLM and ICA methods for analysing task fPET datasets, and to apply ICA methods to the analysis of resting state fPET datasets. Using both simulation and in-vivo experimental datasets, we show that both ICA and GLM methods can successfully identify task related brain activation. We report fPET metabolic resting state brain networks revealed by application of the fPET ICA method to a cohort of 28 healthy subjects. Functional PET provides a unique method to map dynamic changes of glucose uptake in the resting human brain and in response to extrinsic stimulation.
功能正电子发射断层扫描（fPET）是一种神经影像学方法，在 PET 检查过程中涉及连续输注 18F-氟脱氧葡萄糖（FDG）示踪剂。与在静态 PET 扫描中常规给予 FDG 脉冲相比，fPET 提供了更高的时间分辨率，可以研究葡萄糖摄取的动力学。早期的几项研究已经将 fPET 应用于研究大脑 FDG 摄取，并研究其与功能磁共振成像（fMRI）的关系。然而，由于 fPET 信号的独特特征，fPET 信号的建模是一项复杂的任务，对 fPET 实验结果的准确解释提出了挑战。本研究应用独立成分分析（ICA）分析静息状态 fPET 数据，并比较 ICA 和广义线性模型（GLM）在估计任务相关大脑激活方面的性能。使用 GLM 和 ICA 方法比较 fPET 信号特征，以对视觉激活实验中的 fPET 数据进行建模。我们的目的是评估 GLM 和 ICA 方法用于分析任务 fPET 数据集，并将 ICA 方法应用于静息状态 fPET 数据集的分析。通过使用模拟和体内实验数据集，我们表明 ICA 和 GLM 方法都可以成功识别与任务相关的大脑激活。我们报告了通过应用 fPET ICA 方法对 28 名健康受试者的队列进行分析后揭示的 fPET 代谢静息状态脑网络。功能 PET 提供了一种独特的方法，可以绘制静息状态人类大脑和对外源性刺激的葡萄糖摄取的动态变化。
