Drug-related attentional bias (AB), the effect for which substance-addicted patients involuntarily orient their attention toward drug-related cues, has been considered a fundamental factor in substance abuse, addiction development and maintenance (Mckay, 1999; Franken et al., 2003; Field and Cox, 2008; Robinson and Berridge, 2008). AB has been observed in various types of addictions, including alcohol (Townshend and Duka, 2001), cigarette (Chanon et al., 2010), heroin (Waters et al., 2012), and cocaine (Mayer et al., 2016), even in abstinent individuals (Noël et al., 2006; Rahmanian et al., 2006; Wang et al., 2007). For example, previous studies have shown that abstinent heroin addicts (AHAs) exhibit AB to heroin-related stimuli (Marissen et al., 2006). However, few studies investigated the underlying neural correlates of AB for heroin-related stimuli in AHAs. The dot-probe task, developed by MacLeod et al. (1986), is a widely used paradigm to investigate AB (Norman et al., 2014; Ursache and Blair, 2015). It is based on the observation that subjects tend to respond faster to a probe stimulus that is presented in an attended rather than unattended area (Franken et al., 2000). Recently, the task has been extended to investigate AB in cigarette (Ehrman et al., 2002; Spencer, 2015), alcohol (Klein et al., 2013; Mcateer et al., 2015; Clerkin et al., 2016), as well as drug dependence (Lubman et al., 2000; Bradley et al., 2003; Field et al., 2009; Gardini, 2009).

Electroencephalography directly measures neural activity, which can be used to investigate information processing and functional interactions in the human brain with millisecond resolution (Liu et al., 2017). Particularly, the high temporal resolution of event-related potentials (ERPs) allows us to examine sequential cognitive processing states involved in a task. For example, early visual components approximately 80–250 ms after the stimuli onset, P1 or N1, are typically associated with the lower-order visual processing (Omoto et al., 2010), such as the identification of stimuli and their global encoding process (Warbrick et al., 2014), whereas late components from 250 to 500 ms, P2, N2, and P3, are thought to reflect higher-order cognitive processes (Michalewski et al., 1986; Kanske et al., 2011; Ibanez et al., 2012; Kompatsiari et al., 2016), such as selective attention processing, conflict processing, stimulus categorizing, and inhibition processes (Luck et al., 1990; Bocquillon et al., 2014). The P3 component is a positive deflection with a peak around 300 ms after stimulus onset (Herrmann and Knight, 2001), which is related to selective attention processes. Overall, ERPs permit to explore the neural basis of cognitive processes with high sensitivity and reliability, and are complementary to behavioral analyses conducted, for instance, by measuring reaction times (RTs) (Kappenman et al., 2014).

Previous brain imaging studies, using positron-emission tomography (PET) and functional magnetic resonance imaging (fMRI) techniques, have reported that the brain regions that are most vulnerable to heroin addiction are specific prefrontal, parietal, occipital, and temporal regions and subcortical regions (Kilts et al., 2001) linked with reward, motivation/drive, memory/learning, inhibition as well as emotional control (Pandria et al., 2016). AB to drug-related cues generally activates parts of the prefrontal cortex that are relevant to attentional processing (Goldstein and Volkow, 2011), such as dorsolateral prefrontal cortex (dlPFC), the anterior cingulate cortex (ACC), and the inferior frontal gyrus (IFG). However, the low temporal resolution of PET and fMRI does not allow us to disentangle fast cognitive processes underlying AB. In this regard, the EEG source localization technique can be utilized to explore the underlying neural changes of drug-related AB and the associated brain regions (Field and Cox, 2008; Janes et al., 2010; Crunelle et al., 2012).

The aim of this study is to investigate the neural abnormalities of drug-related AB in heroin dependence using a dot-probe task, combining ERP analyses, and source localizations. We hypothesize that AHAs would respond faster than healthy controls (HCs) to the dots that replace drug-related stimuli compared to neutral stimuli. Furthermore, we expect that source analysis of ERP components in AHAs would provide electrophysiological evidences for abnormalities in cognitive processing related to AB.

The task performance, measured by accuracy rate, for AHAs (92.64 ± 4.61%) and HCs (92.82 ± 4.02%) showed no significant difference [t(43) = 0.0283, p = 0.9776], implying that the difference in the educational level between AHAs and HCs did not affect task performance. A 2 × 2 ANOVA on the RTs showed no significant main effect for group or condition (congruent, incongruent), respectively [group: F(1,43) = 0.107, p = 0.745; condition: F(1,43) = 0.004, p = 0.95], whereas the group × condition interaction was significant [F(1,43) = 8.03, p = 0.007]. Moreover, RTs in different conditions were significantly different both for AHAs [F(1,43) = 3.61, p = 0.044] and for HCs [F(1,43) = 4.448, p = 0.04]. Specifically, AHAs tended to have quicker response to targets preceded by drug-related cues compared to targets preceded by neutral images, whereas the opposite pattern was observed in HCs (Figure 2).

To investigate the effects of drug-related cues on the allocation of attentional resources, we compared the ERP components between AHAs and HCs. In particular, we investigated the image-elicited P1 (Supplementary Figure S1), N1, P2, and N2 (Figure 3), and target-elicited P3 (Figure 4 and Supplementary Figure S2). Using a repeated-measure ANOVA, we observed a main effect of group on P1 latency [F(1,43) = 15.246, p < 0.001], N1 amplitude [F(1,43) = 4.418, p = 0.041], P2 amplitude [F(1,43) = 5.336, p = 0.026], N2 amplitude [F(1,43) = 19.486, p < 0.001], and P3 latency [F(1,43) = 25.683, p < 0.001], but not on P1 amplitude [F(1,43) = 0.423, p = 0.519] or P3 amplitude [F(1,43) = 0.676, p = 0.416]. Condition (congruent vs. incongruent) and group (AHA vs. HC) had a significant interaction effect on P3 amplitude [F(1,43) = 7.140, p = 0.011], but not on P3 latency [F(1,43) = 0.489, p = 0.488]. For P3 amplitude, the congruent or incongruent condition showed a significant effect on P3 amplitude for the AHA group [F(1,43) = 8.08, p = 0.007], but not the HC group [F(1,43) = 0.76, p = 0.388] (Figure 4C). Importantly, the amplitudes of target-elicited P3 showed significantly positive correlation with RTs in both congruent (r = 0.5634, p < 0.01) and incongruent conditions (r = 0.5561, p < 0.01) for HCs (Figure 5A). Surprisingly, anti-correlations were obtained in both congruent condition (r = -0.2450, p = 0.2844) and incongruent condition (r = -0.1303, p = 0.5734) for AHAs, although they did not reach significance (Figure 5B). Notably, we did not find any significant correlation between RT and withdrawal time (r = -0.063, p = 0.787 for congruent condition; r = 0.117, p = 0.612 for incongruent condition), neither between P3 amplitude and withdrawal time (r = -0.040, p = 0.865 for congruent condition; r = -0.132, p = 0.570 incongruent condition).

Source localization revealed several brain regions for image-related ERP components (i.e., P1, N1, P2, N2) of interest (Figure 6, Table 1, and Supplementary Table S1). During the P1 time window, dorsal posterior cingulate cortex (dPCC) and superior parietal lobe (SPL) were significantly more active in AHAs than in HCs, whether the drug-related cue was presented in the left or right hemi-spatial field. Also, strong neuronal activity in dPCC in AHAs was maintained until the N1 response. For the P2 and N2 time windows, we found significant clusters of differential activation in the SPL and IFG for drug-related cue both in the left or right hemi-spatial fields. We then examined the neural sources associated with target-related P3 activity (Figure 7, Table 1, and Supplementary Table S1). We observed the medial parietal lobe and occipital lobe in AHAs to be significantly less active both in congruent and incongruent conditions, whereas brain activity in MTG was reduced for AHAs in the incongruent condition, but not the congruent condition. A within-subject comparison showed reduced activity in superior frontal gyrus (SFG), dorsolateral prefrontal lobe, dorsal anterior cingulate cortex (dACC) and IPL for AHAs in the incongruent compared to the congruent condition, whereas no brain regions showed differential activity between two conditions for HCs.

The present study explored the neural correlates of drug-related AB in AHAs by examining ERP components in a dot-probe task. Our behavioral results confirmed the hypothesis that AHAs respond faster than HCs to dots that replace drug-related stimuli, as compared to neutral stimuli. More importantly, the influence of the drug-related cue on attentional processing was reflected by altered neural responses in the early (sensory) stage, as indexed by the P1, N1, P2 responses, but also the late (cognitive) processing stage, as indexed by the N2 and P3 responses. These findings provided novel insights into the neural mechanisms underlying AB toward the drug-related cues in AHAs.

In line with a previous study (Constantinou et al., 2010), we found behavioral evidence of an AB to drug in the AHA group using the traditional RT measure of drug-related bias in a dot-probe task (i.e., the difference in RT on congruent and incongruent trials). Specifically, we observed faster RTs in congruent as compared to incongruent conditions in AHAs, suggesting that the attention of heroin addicts was attracted by drug-related images even after a certain withdrawal period (Figure 2). Previous studies showed that drug-related cues could produce a strong subjective craving in AHAs, which may result in AB (Franken, 2003; Lubman et al., 2008). On the contrary, HCs showed longer RTs in the congruent compared to the incongruent condition, which might be due to an intrinsic avoidance response to drugs (Banerjee, 1971).

The ERP analysis permitted to identify distinct neural responses associated with drug-related AB in AHAs. Visual information processing may be characterized by four different ERP components, related to sensory encoding (around about 80 ms), early categorization (around about 100 ms), and stimulus recognition (around about 150 ms) (Richards, 2003; Lithfous et al., 2014; Oren et al., 2016), and spatial orienting and visual short-term memory (VSTM) (Nobre et al., 2008; Kuo et al., 2014) (around about 250 ms). We observed a significantly smaller P1 latency in AHAs than HCs, suggesting that the manifestation of AB started from an early stage of stimulus categorization and drug contexts might be encoded more quickly in AHAs (Supplementary Figure S1A). However, we did not find significant differences in the contralateral P1 amplitude between AHAs and HCs. This result might be explained by the presence of drug-related image and neutral scenery image bilaterally. Moreover, the source localization for P1 mainly identified dPCC and SPL (Figure 6). This is in good agreement with other studies showing that dPCC is associated with stimulus encoding (Tucker et al., 2011), and SPL is involved in maintaining a spatial reference system for goal oriented behavior. It may be associated with spatial integration of visual features (Wilkinson et al., 2002; Cornette et al., 2006; Molenberghs et al., 2016) and be also related to attentional shifting (Molenberghs et al., 2007; Vandenberghe et al., 2012). More generally, it is involved in the compilation of an attentional priority map (Gillebert et al., 2012). Unlike the P1 amplitude reflecting the inhibition, the N1 amplitude reflects the amount of initial input to attentional resources to the cues (Li et al., 2014). Our study showed significantly larger N1 amplitude in AHAs than in HCs, especially at parietal and occipital electrodes (Figures 3A,B). Visual spatial attention signals from parietal to occipital cortex enable top-down attention processing (Lauritzen et al., 2009). Accordingly, more attention may be allocated to drug-related cues in AHAs in bottom-up attention process. In line with previous studies (Rosazza et al., 2009), we found the N1 topography to be characterized by a typical bilateral posterior negativity, which is consistent with sources located in bilateral occipital–temporal regions (Figure 3B, left panel). More specifically, we found dPCC to be hyperactive in AHAs in the N1 time window (Figure 6B). dPCC is functionally connected with dorsal attention regions (Campbell et al., 2013) and involved in memory loading for the stimulus processing (Oren et al., 2016) and selective processing of external task-relevant information (Campbell et al., 2013).

The parieto–occipital P2, which was evoked at the latency of around 280 ms by image cues, has been considered to be related to memory performance (Dunn et al., 1998) and working memory (Lefebvre et al., 2005). In turn, N2 is typically associated with response to previous memorized stimuli (Hu et al., 2013) and cognitive control of response inhibition. Its neural sources are most likely located in dACC (Nieuwenhuis et al., 2003; Botvinick et al., 2004). In line with previous studies (Howard and Chaiwutikornwanich, 2006; Pinal et al., 2014; Gajewski and Falkenstein, 2015), we observed larger P2 amplitude and N2 amplitude in AHAs, possibly indicating the reinforcement of the memory related to drugs and an increased allocation of attention to drug-related stimulus in AHAs. Source localization for P2 and N2 period mainly identified SPL and IFG (Figure 6C). IFG, a region involved in ventral attention network, is thought to play an important role in stimulus-driven orientation of covert visual spatial attention (Corbetta et al., 2000; Serences et al., 2005). Concurring with previous studies (Luedke et al., 2013), stronger activity in SPL and IFG areas of AHAs during N2 and P2 periods might relate to filtering of irrelevant stimuli.

The analysis of ERPs following the presentation of dot stimuli permitted to investigate how drug-related attention bias affected cognitive processing (Field et al., 2009; Lobben and D’Ascenzo, 2015). Previous studies considered the P3 as an endogenous psychological component, a sign of processes of memory access evoked by evaluation of stimuli in tasks requiring a covert or an overt response (Donchin, 1979, 1981; Polich, 2007). In particular, the latency of P3 is associated with the evaluation of stimuli and strategy adjustment for subsequent processing steps (Donchin, 1979, 1981). Our results showed shorter P3 latency in AHA compared to HC (Figures 4A,C left panel), reflecting a shorter time required for the evaluation or classification of the stimulus in AHAs, and lower P3 amplitude in AHAs in the congruent compared to the incongruent (Figures 4C right panel, 5B). This suggests that working memory in AHAs is slowly updated in the congruent condition (Donchin, 1979, 1981). P3 amplitudes were found to be positively correlated with RTs (Figure 5A) for HCs, which is well in line with the concept that larger P3 amplitude reflects greater perceptual load (Wu et al., 2009; Wang et al., 2014) and higher distribution of psychological resources (Polich, 2007). This positive correlation was lost in AHAs (Figure 5B). This might be caused by altered attention processes in AHAs (Wang et al., 2014). Previous EEG studies showed that the neurophysiological activity of P3 wave emerge from bilateral occipital areas during visual attention and visual memory cognitive tasks (Coullaut-Valera García et al., 2007), and fMRI studies indicated that the medial wall of the SPL may contribute to bottom-up visual integration (Pflugshaupt et al., 2016). Concurring with previous studies, our P3 results suggested that the drug-related AB in AHAs might largely affect bottom-up attention processes and memory cognitive responses relevant to the dot stimulus. Importantly, the effects of withdrawal treatment on the bottom-up attention processes (indexed by P3 amplitude or by RT) might not be simply dependent on the withdrawal time, since neither RT nor P3 amplitude was correlated with withdrawal time. Notably, significant differences in the SFG, dorsal-lateral prefrontal cortex (dlFPC), dACC, and IPL were found between the congruent and incongruent conditions in AHAs, but not in HCs (Figure 7). This indicates differences in AHAs between congruent and incongruent conditions. Previous fMRI studies showed that the response inhibition process in heroin addiction is associated with abnormal brain activity in dACC and SFG (Lee et al., 2005; Fu et al., 2008; Schmidt et al., 2014). dlPFC is known to be involved in the generation of the P3 component, which reflects top-down processes as stimulus categorization and voluntary decision-making (Bocquillon et al., 2014; Kuo et al., 2014). These results may help explaining why AHAs easily relapse again.

Although our study led to a series of findings that might contribute to a better understanding of attention bias in heroin dependence, some limitations need to be noted. First, the low educational level of subjects, in particular for AHAs, might have an impact on their performance in the task. Second, for the experiment protocol, we did not use a varying time for image presentation, neither a jittered intertrial interval. This may raise questions regarding the extent to which neural and behavioral effects that are attributed to attention are confounded by perceptual expectations. Third, we did not track eye-movements during the experiment. Eye-tracking technology, a non-invasive method for measuring gaze, has been proven to be a useful tool to investigate the visual AB (Shechner et al., 2013; Garcíablanco et al., 2014; Fashler and Joel, 2016), which can be considered in future studies. Fourth, the low-density EEG montage and the use of a volume conduction template might have limited the spatial resolution and the accuracy of source reconstruction (Liu et al., 2017). Finally, we found that early visual stimulus processing affects subsequent cognitive processing, as measured by RT. Future studies are therefore warranted to disentangle effects of attention and prediction on early stimulus processing.

In this study, we investigated the neural correlates of drug-related attention bias in AHAs. We revealed that primary differences compared to healthy individuals are already coded in early stimulus encoding and recognition. Moreover, late responses were also aberrant, and possibly related to impaired stimulus evaluation and inhibition. Together, these findings may contribute to a better understanding of the neural basis of attention bias.

BH and QL: study conception and design. QZ, HL, and YL: acquisition of data. HL, BH, CG, DM, and QL: analysis and interpretation of data. All authors have drafted the manuscript. CG, DM, and QL: critical revision.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer LK and handling Editor declared their shared affiliation.