Stable brain PET metabolic networks using a multiple sampling scheme

Interregional communication within the human brain is essential for maintaining functional integrity. A promising approach for investigating how brain regions communicate relies on the assumption that the brain operates as a complex network. In this context, positron emission tomography (PET) images have been suggested as a valuable source for understanding brain networks. However, such networks are typically assembled through direct computation without accounting for outliers, impacting the reliability of group representative networks. In this study, we used brain [¹⁸F]fluoro-2-deoxyglucose PET data from 1,227 individuals in the Alzheimer's disease (AD) continuum from the Alzheimer's Disease Neuroimaging Initiative cohort to develop a novel method for constructing stable metabolic brain networks that are resilient to spurious data points. Our multiple sampling scheme generates brain networks with greater stability compared with conventional approaches. The proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional method. We further validated the approach in an independent AD cohort (n = 114) from São Paulo, Brazil (Faculdade de Medicina da Universidade de São Paulo). This innovative method is flexible and improves the robustness of metabolic brain network analyses, supporting better insights into brain connectivity and resilience to data variability across multiple radiotracers for both health and disease.