As previously mentioned, altered brain functioning, specifically involving both reward circuits and inhibitory control circuits has been found in substance users (Courtney, Schacht, Hutchison, Roche, & Ray, 2016; Edward, 2001; Parvaz, 2012; Géraldine Petit, Maurage, Kornreich, Verbanck, & Campanella, 2014) and in overeaters and obese individuals (Burger & Stice, 2011; Carnell, Gibson, Benson, Ochner, & Geliebter, 2012). These changes generate reinforcing (conditioning) effects, which have been linked to changes in the neural processing of appetitive (salient) stimuli, which have been recently unified in models that attempt to explain the neural basis for both the addiction and the obesity epidemics (Jentsch & Pennington, 2014; Volkow, Wang, Fowler, & Telang, 2008).
Focusing on cue reactivity to rewarding stimuli, in particular two ERPs involving time-locked recordings to specific stimuli, measured by electroencephalography (EEG), were found to have increased amplitude during the cognitive processing of alcohol related stimuli: the frontal-central Positive peak (P300) and the Late Positive Potential (LPP). For a review see Littel, Euser, Munafò, & Franken (2012). Enhancement of these ERP components was also observed in adults exposed to food cues, relative to control cues (Nijs, Franken, & Muris, 2008). The enhancement of these components reflects the processing of motivationally salient cues (substance-related cues), and explains the allocation of attention and memory resources towards these stimuli (which are relevant to their motivational states) in SUD individuals (Franken, 2003; Littel, Euser, Munafò, & Franken, 2012).
Regarding the inhibitory control circuits, ERP studies during response inhibition in substance users relative to controls suggest that biomarkers of inhibition
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are the frontal Negative peak (N200). This is found to be greater during unsuccessful inhibition, while the P300 biomarker is reduced during successful inhibition (Euser & Franken, 2012; Kok, Ramautar, De Ruiter, Band, & Ridderinkhof, 2004; Oddy & Barry, 2009; Ruchsow et al., 2008). Likewise N200 was enhanced when processing food-cues, relative to non food-cues during a GNGT in females who scored highly as external eaters and with greater BMI (Nijs, Franken, & Muris, 2009; Watson & Garvey, 2013).
Therefore, one or more of these brain mechanisms may mediate the effects of CBM. However, changes in brain activity following CBM have only recently been investigated (Bowley et al., 2013; Cabrera et al., 2016; den Uyl, Gladwin, Rinck, Lindenmeyer, & Wiers, 2016; den Uyl, Gladwin, & Wiers, 2016; Korucuoglu, Gladwin, & Wiers, 2014, 2016; Spierer et al., 2013; Verdejo-Garcia, 2016; Wiers et al., 2014; Wiers & Wiers, 2016; Zilverstand, Parvaz, Moeller, & Goldstein, 2016). One of the proposed mechanisms of CBM (Spierer et al., 2013; Wiers & Wiers, 2016) is linked to the modulation and strengthening of the PFC (involved in the cognitive processing and regulation of emotional information) and the dorsal ACC (involved in the resolution of emotional conflicts, for example during cravings). This hypothesis is supported by imaging literature on cognitive bias reactivity in anxiety, depression and addiction, prior to CBM. In abstinent alcoholics, compared to controls, alcohol approach biases have been associated with increased activity in the NAcc and the mPFC (Ernst et al., 2014; Wiers et al., 2014). Therefore, CBM may modify activation of these regions during performance in these tasks.
Recent research indirectly tested this hypothesis by stimulating the PFC and ACC with neuro-modulatory techniques, such as transcranial direct current stimulation (tDCS), which influences neural excitability and plasticity, with the intention of enhancing CBM effects. Findings in hazardous drinkers and alcohol- dependent patients are inconclusive, and show no robust moderating effects of tDCS on CBM (den Uyl, Gladwin, Rinck, et al., 2016; den Uyl, Gladwin, & Wiers, 2016). However, a recent exploratory tDCS study on women found a reduced N200 component and enhanced P300 component when responding to No-Go trials to both food cues and control stimuli: tDCS stimuli increased inhibitory control bio-markers and also modulated the reduction in calorie intake (Lapenta, Sierve, de Macedo, Fregni, & Boggio, 2014).
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An additional hypothesis (Cabrera et al., 2016; Spierer et al., 2013; Verdejo- Garcia, 2016; Wiers & Wiers, 2016; Zilverstand et al., 2016), proposes reductions post-CBM in the activation of mesolimbic structures (see Schacht, Anton, & Myrick, 2013). As discussed previously, since mesolimbic structures are involved in the modulation of stimulus incentive salience (Koob & Volkow, 2010), and because CBM involves the formation of new stimulus-response associations by modifying the original salience (valence) of the stimuli (Veling et al., 2008), CBM may consequently reduce the activation of these structures. Most of the neuro-CBM imaging literature focuses on CAT interventions, specifically targeting anxiety and depression (Wiers & Wiers, 2016). Two recent fMRI studies investigating CAT neuro-mechanisms have been published in the addiction field; demonstrating reduced activation in the amygdala (Wiers, Stelzel, et al., 2015) and in the medial PFC (mPFC; Wiers, Ludwig, et al., 2015) in alcohol-dependent patients after multiple sessions of training. This suggests a blunting effect of CBM on the incentive salience of alcohol stimuli (Gladwin et al., 2016), even though these effects were inconsistently associated with changes in behavioural performance (Wiers, Ludwig, et al., 2015).
Imaging literature regarding ICT effects is certainly lacking (for a review of this see Verdejo-Garcia, 2016). One ICT study on hazardous drinkers is reported in the literature and adopts a GNG paradigm during EEG recordings (Bowley et al., 2013). Results during passive viewing of three different types of stimuli (alcohol, water and landscapes) showed that a brief single dose of ICT (in which individuals inhibited their responses to beer cues), or a brief regular intervention relative to an opposite ‘Go training’ (in which individuals responded to beer cues), reduced left frontal activity post-intervention relative to pre-training. However, these decreases did not reach statistical significance, but this may be due to the addition of the passive-viewing task post-ICT which may have weakened the effects. These trends seem to suggest some kind of improvement of the inhibitory control following training, as suggested by Spierer and colleagues (2013), but further studies are needed to validate these claims. Finally, one other study investigated neural correlates of ICT in healthy individuals, adopting an SST paradigm. This showed increased activation of the inferior frontal gyrus (IFG) during response preparation to ‘Go cues’ and a decrease in the same region during inhibition, which correlated with a general improvement in task performance (Berkman, Kahn, & Merchant, 2014).
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In summary, the evidence base pertaining to the brain mechanisms that underlie the effects of CBM is limited and findings are very contradictory. Therefore, the mechanisms of action of CBM remain poorly understood. Future studies should focus on the brain mechanism that underlie as they provide insights that can contribute to the development and optimisation of these promising interventions (Verdejo-Garcia, 2016; Zilverstand et al., 2016).