Resting‐state functional connectivity and nicotine addiction: prospects for biomarker development

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.

Closed-loop brain training: the science of neurofeedback

“…An example of one such model of network-based pathology is the 'triple network model' of neuropsychiatric disorders 178 . Dysfunction in three large-scale brain networks (the default mode network (DMN), the executive control network (ECN) and the salience network (SN)) seems to be crucial for the development and maintenance of a series of neuropsychiatric disorders, including schizophrenia 178 , Alzheimer disease 182 and addiction 183,184 . The DMN includes a set of midline (the medial prefrontal cortex and the posterior cingulate cortex) and lateral (parahippocampal gyrus) brain networks that have been implicate d in internal self-referential processes (rumination, and episodic and prospective memory).…”

“…Activity in the DMN and ECN seems to be anti-correlated and may serve to respond to information-processing demands from internal (DMN) and external (ECN) environments. The brain system that accounts for allocation of attentional resources and facilitation of switching between the DMN and the ECN is the SN, which consists of the anterior insula, the dorsal anterior cingulate and, occasionally, the central reward or motivation system, including the ventral tegmental area, the nucleus accumbens-ventral striatum, the amygdala and the ventromedial prefrontal cortex 183 .…”

Insula Demonstrates a Non-Linear Response to Varying Demand for Cognitive Control and Weaker Resting Connectivity With the Executive Control Network in Smokers

“…Identified GROUP differences in task activation were used as seeds for rsFC analysis. Analyses were a priori constrained to voxels falling within each of the three anatomical masks of large-scale brain networks consistently shown relevant to nicotine addiction (Fedota and Stein, 2015;Sutherland et al, 2012): DMN, ECN, and SN. These resting networks were identified via group-level independent component analysis (gICA) (Beckmann et al, 2005) within the FSL software package MELODIC (http:// www.fmrib.ox.ac.uk/fsl/melodic/index.html).…”

“…Behavioral and neurobiological deficits of inhibitory processing are often dissociated in smokers (Buzzell et al, 2014;de Ruiter et al, 2012;Evans et al, 2009), and consistent effects on performance monitoring have also proven elusive (Franken et al, 2010;Luijten et al, 2011;Rass et al, 2014). These disparate task-based results are in contrast to resting network connectivity, which is more consistently dysregulated in smokers (for a review, see Fedota and Stein, 2015;Sutherland et al, 2012). Resting functional connectivity results show that connectivity within and between the ECN, SN, and DMN is disrupted, with SN-ECN connectivity reduced and SN-DMN connectivity increased in smokers, especially during abstinence (Lerman et al, 2014;Sutherland et al, 2013).…”

“…This is based in part on recent successes integrating task-based activation with resting network analyses to provide a more holistic understanding of addiction-related dysfunction (Janes et al, 2015;Sutherland et al, 2013). Predicated on the known nicotine-dependent alterations between and among preestablished, large-scale brain networks (Fedota and Stein, 2015;Sutherland et al, 2012), our strategy was to use PFT-based differences in activation between smokers and non-smokers as seeds for resting-state functional connectivity (rsFC) analysis. Thus the current study sought to more directly relate tasked-based dysfunction in cognitive control to large-scale network connectivity by identifying differences in network connections between task-modified, cognitive control regions and resting-state networks relevant to attentional salience attribution.…”