Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence

Smoking withdrawal leads to both whole-brain and network-specific decreases in brain dynamics.

Featured Paper of the Month – April 2021

Published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging by John Fedota, Ph.D. and Thomas Ross, Ph.D., et al. in the NIDA IRP Cognitive and Affective Neuroscience of Addiction Section.

Summary

Quitting smoking is hard. This is largely due to the nicotine withdrawal syndrome. Withdrawal from cigarettes is characterized by craving, bad feelings and losses of attention. Importantly, these negative symptoms are known to fluctuate wildly in time. Inspired by this, NIDA scientists applied a technique that allowed us to look at how communication across the entire brain fluctuated over time during smoking and withdrawal. Twenty-five smokers came to NIDA twice each. During the first visit they smoked a cigarette and were not in withdrawal. Before the second visit, they did not smoke for 2 days and so were in peak withdrawal. At each visit they underwent a function magnetic resonance imaging (MRI) scan. This scan measures brain activity. NIDA scientists analyzed these data to examine which brain regions were varying together and how that changed over the duration of the scan. They found that nicotine withdrawal led to brain-wide decreases in the frequency of interactions between brain networks. In addition, within a subset of these networks, the variability of these interactions also decreased. Finally, within 2 of these networks, the decrease in variability was related to withdrawal symptoms. So, withdrawal leads to decrease in brain dynamics, and this may be part of why quitting is so hard.

Publication Information

Fedota, John R; Ross, Thomas J; Castillo, Juan; McKenna, Michael R; Matous, Allison L; Salmeron, Betty Jo; Menon, Vinod; Stein, Elliot A

Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence Journal Article

In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2020, ISSN: 2451-9022.

Abstract | Links

@article{FEDOTA2020b,

title = {Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence},

author = {John R Fedota and Thomas J Ross and Juan Castillo and Michael R McKenna and Allison L Matous and Betty Jo Salmeron and Vinod Menon and Elliot A Stein},

url = {https://pubmed.ncbi.nlm.nih.gov/33436331/},

doi = {https://doi.org/10.1016/j.bpsc.2020.10.004},

issn = {2451-9022},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},

abstract = {\textbf{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. 



\textbf{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. 



\textbf{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. 



\textbf{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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}