Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level

• Published in: Journal of visualized experiments no. 215
• Main Authors: Lajtos, Melissa, Raedt, Robrecht, Neyt, Sara, De Herdt, Veerle, Vanhove, Christian
• Format: Journal Article
• Language: English
• Published: United States 24.01.2025
• Subjects: Animals; Brain - diagnostic imaging; Brain - metabolism; Fluorodeoxyglucose F18 - administration & dosage; Fluorodeoxyglucose F18 - chemistry; Fluorodeoxyglucose F18 - pharmacokinetics; Male; Positron-Emission Tomography - methods; Radiopharmaceuticals - pharmacokinetics; Rats
• ISSN: 1940-087X
• DOI: 10.3791/67458

To this day, metabolic brain connectivity is mostly studied on a group level through the acquisition of static positron emission tomography (PET) data of multiple subjects. Our research groups are currently studying changes in metabolic connectivity across multiple time points following an intracerebral hemorrhage on an intrasubject level in rats. To investigate intrasubject metabolic brain connectivity, temporal information of the tracer uptake in different brain regions is required, which can be achieved through dynamic PET. In this publication, we give a detailed description of our data acquisition and analysis protocol. Dynamic PET data of the rat brain are acquired on a dedicated preclinical PET system using 2-deoxy-2-[ F]fluoro-D-glucose ([ F]FDG) as tracer. The tracer is injected intravenously as a bolus at the start of the PET scan. During the 60 min acquisition, animals are sedated with medetomidine. After acquisition, the PET data are reconstructed into thirty 2 min time frames using an iterative reconstruction algorithm (Maximum-Likelihood Expectation-Maximization). A parcellated atlas consisting of multiple volumes of interest (VOIs) is used to extract time-activity curves of each VOI, which are then used to calculate the Pearson correlation coefficient between each pair of VOIs. This dynamic PET protocol enables the assessment of metabolic connectivity differences between two single scans, rather than between groups of scans. This approach allows for the study of changes in metabolic connectivity within a single subject across different time points, or for the comparison of an individual's metabolic connectivity to a normal database. Such comparisons could be useful for tracking disease progression or aiding in the diagnosis of neurological disorders characterized by disrupted communication between brain regions, such as epilepsy or dementia.