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

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 [18F]FDG as well as “tau/amyloid covariance” for [18F]flutemetamol / [18F]flortaucipir. To augment these distinctions, high-temporal resolution functional [18F]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 3rd 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. ### Competing Interest Statement This research was funded in whole, or in part, by the Austrian Science Fund (FWF) [grant DOI: 10.55776/KLI1006 and 10.55776/KLI504, PI: R. Lanzenberger; grant DOI: 10.55776/DOC33, PI/Supervisor of M. Murgas: R. Lanzenberger; grant-DOI: 10.55776/KLI1151, PI: A. Hahn], the WWTF Vienna Science and Technology Fund [grant DOI: 10.47379/CS18039, Co-PI: R. Lanzenberger], and by a grant from the Else Kröner-Fresenius-Stiftung (2014_A192) to R. Lanzenberger. L. Cocchi is supported by the NHMRC Australia (GNT2027597). For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. L. R. Silberbauer, G. Gryglewski, and M. B. Reed were recipients of DOC fellowships of the Austrian Academy of Sciences at the Department of Psychiatry and Psychotherapy, Medical University of Vienna. This scientific project was performed with the support of the Medical Imaging Cluster of the Medical University of Vienna. R. Lanzenberger received investigator-initiated research funding from Siemens Healthcare regarding clinical research using PET/MR and travel grants and/or conference speaker honoraria from Janssen-Cilag Pharma GmbH in 2023, and Bruker BioSpin, Shire, AstraZeneca, Lundbeck A/S, Dr. Willmar Schwabe GmbH, Orphan Pharmaceuticals AG, Janssen-Cilag Pharma GmbH, Heel and Roche Austria GmbH., and Janssen-Cilag Pharma GmbH in the years before 2020. He is a shareholder of the start-up company BM Health GmbH, Austria since 2019. M. Hacker received consulting fees and/or honoraria from Bayer Healthcare BMS, Eli Lilly, EZAG, GE Healthcare, Ipsen, ITM, Janssen, Roche, and Siemens Healthineers. L. Cocchi is involved in a not-for-profit clinic administering fMRI-guided brain stimulation therapy (Queensland Neurostimulation Centre). The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.