Abstract PET-based connectivity computation is a molecular approach that complements fMRI-derived functional connectivity. However, the diversity of methodologies and terms employed in PET connectivity analysis has resulted in ambiguities and confounded interpretations, highlighting the need for a standardized nomenclature. Drawing parallels from other imaging modalities, we propose “molecular connectivity” as an umbrella term to characterize statistical dependencies between PET signals across brain regions at the individual level (within-subject). Like fMRI resting-state functional connectivity, “molecular connectivity” leverages temporal associations in the PET signal to derive brain network associations. Another within-subject approach evaluates regional similarities of tracer kinetics, which are unique in PET imaging, thus referred to as “kinetic connectivity”. On the other hand, “molecular covariance” denotes group-level computations of covariance matrices across-subject. Further specification of the terminology can be achieved by including the employed radioligand, such as “metabolic connectivity/covariance” for [ 18 F]FDG as well as “tau/amyloid covariance” for [ 18 F]flutemetamol / [ 18 F]flortaucipir. To augment these distinctions, high-temporal resolution functional [ 18 F]FDG PET scans from 17 healthy participants were analysed with common techniques of molecular connectivity and covariance, allowing for a data-driven support of the above terminology. Our findings demonstrate that temporal band-pass filtering yields structured network organization, whereas other techniques like 3 rd order polynomial fitting, spatio-temporal filtering and baseline normalization require further methodological refinement for high-temporal resolution data. Conversely, molecular covariance from across-subject data provided a simple means to estimate brain region interactions through regularized or sparse inverse covariance estimation. A standardized nomenclature in PET-based connectivity research can reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers and imaging modalities. Via a data-driven approach, this work provides a transparent framework for categorizing and comparing PET-derived connectivity and covariance metrics, laying the foundation for future investigations in the field.

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature