[en] Abstract
Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-called molecular connectivity approach that captures relationships of molecular imaging measures in different brain regions. Targeting these inter-regional interactions within a neuroreceptor system may allow to better understand complex brain functions. In this article, we provide a comprehensive review of molecular connectivity studies in the field of neurotransmission. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge. A systematic search in three bibliographic databases MEDLINE, EMBASE, and Scopus on July 14, 2023 was conducted. A second search was rerun on April 4, 2024. Molecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria. Thirty-nine studies were included in the scoping review. Studies were categorised based on the primary neurotransmitter system being targeted: dopamine, serotonin, opioid, muscarinic, glutamate, and synaptic density. The most investigated system was the dopaminergic and the most investigated disease was Parkinson’s disease (PD). This review highlighted the diverse applications and methodologies in molecular connectivity research, particularly for neurodegenerative diseases and psychiatric disorders. Molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Severino, Mario ; Department of Information Engineering, University of Padua, Padua, Italy
Peretti, Débora Elisa; Laboratory of Neuroimaging and Innovative Molecular Tracers, University of Geneva, Geneva, Switzerland
Bardiau, Marjorie ; Université de Liège - ULiège > CARE "ULiège Library" > ULiège Library : Santé
Cavaliere, Carlo ; Université de Liège - ULiège > GIGA ; IRCCS SYNLAB SDN, Naples, Italy
Doyen, Matthieu; Université de Lorraine, IADI, INSERM U1254, Nancy, France
Gonzalez-Escamilla, Gabriel; Department of Neurology, Focus Program Translational Neuroscience, Rhine-Main, Neuroscience Network, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
Horowitz, Tatiana; Department of Nuclear Medicine, Timone Hospital, AP-HM, Aix Marseille Univ, CERIMED, CNRS, Centrale Marseille, Institut Fresnel, Marseille, France
Nørgaard, Martin; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark ; Molecular Imaging Branch, National Institute of Mental Health, Bethesda, MD, United States
Perez, Jhony Alejandro Mejia; Memory and Aging Centre, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
Perovnik, Matej; Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
Rullmann, Michael; Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany
Steenken, Dilara; Department of Nuclear Medicine, TUM School of Medicine and Health, Munich, Germany
Talmasov, Daniel; Departments of Neurology and Psychiatry, Columbia University Irving Medical Centre, New York, NY, United states
Tang, Chunmeng; Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
Volpi, Tommaso; Department of Radiology and Biomedical Imaging, PET Centre, Yale University, New Haven, CT, United States
Xu, Zhilei; Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
Bertoldo, Alessandra; Department of Information Engineering, University of Padua, Padua, Italy ; Padova Neuroscience Centre, University of Padua, Padua, Italy
Calhoun, Vince D.; Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TRenDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
Caminiti, Silvia Paola; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
Di, Xin; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United states
Habeck, Christian; Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Centre, New York, NY, United States
Jamadar, Sharna; School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia ; Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
Perani, Daniela; Vita-Salute San Raffaele University, Milan, Italy
Sala, Arianna ; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Coma Science Group
Sossi, Vesna; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
Yakushev, Igor; Department of Nuclear Medicine, TUM School of Medicine and Health, Munich, Germany
Pereira, Joana B.; Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
Veronese, Mattia; Department of Information Engineering, University of Padua, Padua, Italy ; Department of Neuroimaging, King’s College London, London, United Kingdom
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