Resting‐state functional connectivity and nicotine addiction: prospects for biomarker development. (September 2015)

• Record Type: Journal Article
• Authors: Fedota, John R.; Stein, Elliot A.
• Other Names: Uhl George R. guestEditor.
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1349(2015)
• Page Start: 64
• Page End: 82
• Publication Date: 2015-09
• Subjects: addiction -- nicotine -- resting‐state functional connectivity -- biomarkers; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.12882
• Languages: English
• ISSNs: 0077-8923

Abstract

Abstract : Given conceptual frameworks of addiction as a disease of intercommunicating brain networks, examinations of network interactions may provide a holistic characterization of addiction‐related dysfunction. One such methodological approach is the examination of resting‐state functional connectivity, which quantifies correlations in low‐frequency fluctuations of the blood oxygen level–dependent magnetic resonance imaging signal between disparate brain regions in the absence of task performance. Here, evidence of differentiated effects of chronic nicotine exposure, which reduces the efficiency of network communication across the brain, and acute nicotine exposure, which increases connectivity within specific limbic circuits, is discussed. Several large‐scale resting networks, including the salience, default, and executive control networks, have also been implicated in nicotine addiction. The dynamics of connectivity changes among and between these large‐scale networks during nicotine withdrawal and satiety provide a heuristic framework with which to characterize the neurobiological mechanism of addiction. The ability to simultaneously quantify effects of both chronic (trait) and acute (state) nicotine exposure provides a platform to develop a neuroimaging‐based addiction biomarker. While such development remains in its early stages, evidence of coherent modulations in resting‐state functional connectivity at various stages of nicotine addiction suggests potential network interactions on which to focus future addiction biomarker development.