John R Fedota1, Thomas J Ross2, Juan Castillo2, Michael R McKenna3, Allison L Matous4, Betty Jo Salmeron2, Vinod Menon5, Elliot A Stein6. 1. Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland. Electronic address: john.fedota@nih.gov. 2. Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland. 3. Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland; Department of Psychology, Ohio State University, Columbus, Ohio. 4. Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland; Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire. 5. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California; Stanford Neuroscience Institute, Stanford, California. 6. Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland. Electronic address: Estein@nih.gov.
Abstract
BACKGROUND: The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS. METHODS: To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain. RESULTS: During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms. CONCLUSIONS: Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence. Published by Elsevier Inc.
BACKGROUND: The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS. METHODS: To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain. RESULTS: During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms. CONCLUSIONS: Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence. Published by Elsevier Inc.
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