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<p>Positron emission tomography (PET)-based connectivity analysis provides a molecular perspective that complements fMRI-derived functional connectivity. However, lack of standardized terminology and diverse methodologies in PET connectivity studies has resulted in inconsistencies, complicating the interpretation and comparison of results across studies. A standardized nomenclature is thus needed to reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers, imaging modalities and studies. Here, we define and differentiate the terms “molecular connectivity” and “molecular covariance”. Drawing parallels from other imaging modalities, we propose “molecular connectivity” as an umbrella term to characterize statistical dependencies between the measured PET signal across brain regions at a <i>within-subject</i> level. Like fMRI resting-state functional connectivity, “molecular connectivity” leverages spatio-temporal associations in the PET signal to derive brain network associations. Conversely, “molecular covariance” denotes group-level computations of covariance matrices <i>between-subjects</i>. Further specification of the terminology can be achieved by including the target of the employed radioligand, such as “metabolic connectivity/covariance” for [<sup>18</sup>F]FDG or “amyloid covariance” for [<sup>18</sup>F]flutemetamol and “tau covariance” for [<sup>18</sup>F]flortaucipir. While this approach to standardization aims to clarify terminology, open questions remain about the neurobiological underpinnings of these connectivity metrics. Future research should focus on elucidating these mechanisms and developing advanced computational methodologies that evaluate diverse feature relationships and improve the robustness of PET-based connectivity metrics.</p>

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Connecting the dots: approaching a standardized nomenclature for molecular connectivity in positron emission tomography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月2日

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  • Murray B. Reed,
  • Luca Cocchi,
  • Christin Y. Sander,
  • Jingyuan Chen,
  • Granville J. Matheson,
  • Patrick Fisher,
  • Tommaso Volpi,
  • Nikkita Khattar,
  • Christine DeLorenzo,
  • Gregor Gryglewski,
  • Leo R. Silberbauer,
  • Matej Murgaš,
  • Godber M. Godbersen,
  • Lukas Nics,
  • Martin Walter,
  • Marcus Hacker,
  • Alessandra Bertoldo,
  • Mark Lubberink,
  • Mark Silfstein,
  • R. Todd Ogden,
  • J. John Mann,
  • Tetsuya Suhara,
  • Andrea Varrone,
  • Ronald Boellaard,
  • Roger N. Gunn,
  • Alexander Hammers,
  • Bharat Biswal,
  • Bruce Rosen,
  • Gitte M. Knudsen,
  • Richard Carson,
  • Julie Price,
  • Rupert Lanzenberger,
  • Andreas Hahn

摘要

Positron emission tomography (PET)-based connectivity analysis provides a molecular perspective that complements fMRI-derived functional connectivity. However, lack of standardized terminology and diverse methodologies in PET connectivity studies has resulted in inconsistencies, complicating the interpretation and comparison of results across studies. A standardized nomenclature is thus needed to reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers, imaging modalities and studies. Here, we define and differentiate the terms “molecular connectivity” and “molecular covariance”. Drawing parallels from other imaging modalities, we propose “molecular connectivity” as an umbrella term to characterize statistical dependencies between the measured PET signal across brain regions at a within-subject level. Like fMRI resting-state functional connectivity, “molecular connectivity” leverages spatio-temporal associations in the PET signal to derive brain network associations. Conversely, “molecular covariance” denotes group-level computations of covariance matrices between-subjects. Further specification of the terminology can be achieved by including the target of the employed radioligand, such as “metabolic connectivity/covariance” for [18F]FDG or “amyloid covariance” for [18F]flutemetamol and “tau covariance” for [18F]flortaucipir. While this approach to standardization aims to clarify terminology, open questions remain about the neurobiological underpinnings of these connectivity metrics. Future research should focus on elucidating these mechanisms and developing advanced computational methodologies that evaluate diverse feature relationships and improve the robustness of PET-based connectivity metrics.