Individual Differences in Attentional Bias Associated with Cocaine Dependence Are Related to Varying Engagement of Neural Processing Networks

Abstract

Cocaine and other drug dependencies are associated with significant attentional bias for drug use stimuli that represents a candidate cognitive marker of drug dependence and treatment outcomes. We explored, using fMRI, the role of discrete neural processing networks in the representation of individual differences in the drug attentional bias effect associated with cocaine dependence (AB-coc) using a word counting Stroop task with personalized cocaine use stimuli (cocStroop). The cocStroop behavioral and neural responses were further compared with those associated with a negative emotional word Stroop task (eStroop) and a neutral word counting Stroop task (cStroop). Brain-behavior correlations were explored using both network-level correlation analysis following independent component analysis (ICA) and voxel-level, brain-wide univariate correlation analysis. Variation in the attentional bias effect for cocaine use stimuli among cocaine-dependent men and women was related to the recruitment of two separate neural processing networks related to stimulus attention and salience attribution (inferior frontal-parietal-ventral insula), and the processing of the negative affective properties of cocaine stimuli (frontal-temporal-cingulate). Recruitment of a sensory-motor-dorsal insula network was negatively correlated with AB-coc and suggested a regulatory role related to the sensorimotor processing of cocaine stimuli. The attentional bias effect for cocaine stimuli and for negative affective word stimuli were significantly correlated across individuals, and both were correlated with the activity of the frontal-temporal-cingulate network. Functional connectivity for a single prefrontal-striatal-occipital network correlated with variation in general cognitive control (cStroop) that was unrelated to behavioral or neural network correlates of cocStroop-or eStroop-related attentional bias. A brain-wide mass univariate analysis demonstrated the significant correlation of individual attentional bias effect for cocaine stimuli with distributed activations in the frontal, occipitotemporal, parietal, cingulate, and premotor cortex. These findings support the involvement of multiple processes and brain networks in mediating individual differences in risk for relapse associated with drug dependence.

Individual Variation in the Motivational and Neurobiological Effects of an Opioid Cue

“…However, one limitation of the study is that Fos was only quantified from a portion of each structure and may not be representative of the entire region. Interestingly, these data parallel some recent human imaging work that has shown individual variation in the ability of both food and drug cues to elicit brain activity throughout the 'motive circuit' (Beaver et al, 2006;Janes et al, 2010;Kilts et al, 2014). It was also interesting that the food and opioid cue engaged essentially the same brain regions in STs.…”

Visual cortex activation to drug cues: A meta-analysis of functional neuroimaging papers in addiction and substance abuse literature

“…Many studies have investigated attentional bias as a bell-weather for treatment success across multiple drug-classes including in cocaine (Kennedy et al, 2014; Kilts et al, 2014), heroin (Marrisen et al, 2006), smoking (Waters et al, 2003, Janes et al, 2010), and alcohol (Cox et al, 2002). Other studies have even used attentional bias as a target of treatment itself including Attentional Retraining (Atwood et al, 2008).…”

Estimating brain network activity through back-projection of ICA components to GLM maps

“…ICA has been broadly used in functional magnetic resonance imaging (fMRI) to identify task-activated brain networks (Congdon, et al 2010; McKeown, et al 1998; Stanger, et al 2013; Worhunsky, et al 2013). ICA is frequently followed with general linear modeling (GLM) to assess how these ICA-identified networks are recruited by fMRI tasks (Calhoun, et al 2001; Kilts, et al 2013). As a data-driven approach, ICA does not require a priori information about the source signals to identify them; it has thus been used to identify brain networks in the absence of task (i.e.…”