The present study applied MVPA and spectral DCM analysis methods to investigate the neurobiological substrates of AUD during the resting state. Our results show that image-based machine-learning techniques can be used to distinguish AUD. Compared to ALFF, fALFF, DCpb, and DCpw, ReHo showed the highest accuracy in classifying AUD from HCs (Classification accuracy:98.57%). The most informative brain regions for the classification are left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These brain regions are involved in executive control, decision-making, and reward/loss processing and might provide a novel perspective for the clinical diagnosis of AUD. These findings were validated using an independent data set, achieving a validation accuracy of 91.67%. Our results demonstrate the potential of image-based machine-learning techniques in predicting addiction severity (MAST and AUDIT scores) among patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. This finding was validated in an independent data set. Moreover, this study represents the first endeavor to employ spectral DCM to identify impaired causal interactions among brain regions associated with executive control, decision-making, and reward/loss processing based on resting-state fMRI data obtained from healthy and AUD subjects. Our findings reveal significant differences in intrinsic self-connections and extrinsic connections between AUD and HCs. In addition, the strength of effective connectivity from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These results implicated that impulsiveness may be critical personality traits individuals have that makes them vulnerable to alcohol use and develop AUD.
Executive control deficit is a crucial characteristic of AUD (Wilcox, Dekonenko, Mayer, Bogenschutz, & Turner, 2014). Despite being aware of the negative physical, psychological, occupational, or social consequences of continued alcohol use, AUD individuals are unable to reduce or inhibit alcohol consumption(Strosche et al., 2021; Wilcox et al., 2014). An emerging view considers impaired executive control as both a determinant and a consequence of addictive behaviors(Dalley, Everitt, & Robbins, 2011). Brain regions implicated in executive control include the dorsolateral prefrontal cortex (DLPFC), inferior frontal gyrus (IFG), dACC, and pre-SMA(Aron, Robbins, & Poldrack, 2004; Crowe et al., 2013; Feil et al., 2010; Gbadeyan, McMahon, Steinhauser, & Meinzer, 2016; Nachev, Kennard, & Husain, 2008). In our study, one of the most consistent findings with previous research is that the most informative brain region for classification and prediction includes the dACC and pre-SMA. We also observed decreased intrinsic effective self-connections of the left pre-SMA. These findings suggest that individuals with AUD exhibit significant impairment of executive control, and the degree of executive control impairment worsens with the increase of the severity of the addiction.
The dACC is also involved in detecting the presence of cognitive conflict, and error monitoring and detection(Bush et al., 2002; Van Veen & Carter, 2002). In addition to error and conflict monitoring, the dACC may be critical for the expression of conditioned fear and anxiety(Büchel, Morris, Dolan, & Friston, 1998; Phelps, Delgado, Nearing, & LeDoux, 2004). Studies related to obsessive-compulsive disorder suggested that the dACC-mediated faulty error signals, elevated fear, and anxiety contribute to the obsessions observed in obsessive-compulsive disorder(Dougherty et al., 2002; Milad & Rauch, 2012). Compulsivity has been identified as the central characteristic of AUD, including excessive time spent drinking, neglect of other goal-directed behaviors (such as employment and family activities), and even a failure to avoid physical self-harm, as well as subjective correlates of drinking behavior, such as craving(Augier et al., 2018; Lesscher & Vanderschuren, 2012; Siciliano et al., 2019). Consistent with previous research(Galandra et al., 2019a; Herremans et al., 2016), we also found a bio-marker effect of the dACC (it is one of the brain regions that contributes the most information to the HCs and AUD classification) in categorical AUD. These findings suggest that the dysfunctional dACC-mediated faulty error signals, elevated fear, and anxiety contribute to the obsessions observed in AUD.
AUD individuals are also impaired across several measures of decision-making(Gowin, Sloan, Swan, Momenan, & Ramchandani, 2019; Lim, Cservenka, & Ray, 2017). In the evaluation of risk, AUD individuals showed decreased loss sensitivity in a mixed gamble study(Genauck et al., 2017), which can lead to a change in ongoing behavior. A large meta-analysis of the existing neuroimaging data was used to show that LOFC activity is related to the evaluation of loss(Kringelbach, 2005). Our results show that the LOFC is also one of the most informative brain regions for classification and prediction. These results indicate that AUD individuals show significant impairment in the evaluation of risks of drinking, and the sensitivity to the negative consequences of alcohol consumption decreases with the increase of the severity of the addiction.
Moreover, burgeoning evidence suggests that addiction to drugs (e.g., alcohol) is associated with a general bias to a habitual (also known as ‘model-free’) mode of behavior, as distinct from goal-directed (or ‘model-based’) behavior. Habitual behavior is generally associated with activity in the putamen, whereas goal-directed behavior is associated with activity in the caudate(Balleine & O'doherty, 2010; Sjoerds et al., 2013). Habitual behavior can also be perseverative to the extent that it can be said to be ‘out of control’. Lacking top-down executive control over habitual behavior, individuals will exhibit compulsive behaviors (e.g., compulsive alcohol intake)(Lüscher, Robbins, & Everitt, 2020). In our study, we further observed decreased effective connectivity from the left pre-SMA to the right putamen and from the right dACC to the right putamen. The effective strength from the left SMA to the right putamen and from the right dACC to the right putamen showed a significant negative correlation with addiction severity (AUDIT and MAST scores). More importantly, the effective strength from the right dACC to the right putamen mediated the association between addiction severity (MAST scores) and compulsive scores. These findings suggest that AUD individuals exhibit a lack of top-down control over habitual alcohol consumption-compulsive alcohol consumption, and the impairment is exacerbated by increased alcohol consumption.
The most informative regions for the classification and prediction also included the right NACC. As part of the reward system, the NACC plays an important role in processing rewarding, reinforcing stimuli (such as alcohol) (Galandra et al., 2019b). Alcohol consumption has rewarding properties in both animals and humans driven by enhanced dopamine and opioid transmission in the basal ganglia (Koob & Volkow, 2016). Human imaging studies of acute alcohol administration demonstrated that intravenous alcohol increased dopamine release in the right NACC(Martinez et al., 2005; Yoder et al., 2016). In addition, we also observed increased effective connectivity from the right NACC to the left pre-SMA. Moreover, the effective strength from the right NACC to the left pre-SMA mediated the association between addiction severity (MAST scores) and impulsive scores. These results suggested that AUD individuals exhibit excessive sensitivity to rewarding reinforcement (e.g., alcohol consumption), and are significantly impaired in their ability to inhibit the impulsiveness to seek such rewarding reinforcement. These may be the underlying neural bases for impulsive alcohol consumption.
Several researchers have proposed a significant correlation between alcohol use disorders and impulse control disorders(Ernst & Paulus, 2005; Everitt & Robbins, 2005; Kalivas & Volkow, 2005). Nevertheless, a consensus has not been reached regarding whether impulsivity emerges as a result of prolonged alcohol exposure or predates alcohol consumption, thereby increasing individuals' susceptibility to addiction. Aragues and her colleagues suggested that impulsivity could already be present practically from birth as a personality trait, and it may be a marker for early use and/or abuse of alcohol(Aragues, Jurado, Quinto, & Rubio, 2011). The results of our mediation analysis indicate that impulsivity as an independent variable affected the addiction severity (MAST scores) through the mean effective connectivity strength from the right NACC to the left pre-SMA. This study offers crucial evidence supporting the notion that individuals exhibiting high levels of impulsivity are at heightened susceptibility to alcohol use disorder (AUD). These findings present an opportunity to further develop robust methods for identifying hazardous drinkers or individuals with an alcohol use disorder (AUD), as well as refine diagnostic instruments to increase their applicability across treatment settings and subpopulations. Additionally, these findings offer important targets for interventions aimed at preventing and treating AUD. Utilizing various techniques, such as transcranial electrical stimulation, to decrease impulsivity in individuals with a high risk of AUD by reducing the effective connectivity strength from the right NACC to the left pre-SMA may prove to be an efficacious approach for preventing and treating AUD.
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Although our study is the first to identify abnormalities in brain effective connectivity specific to individuals with AUD using spectral DCM based on the MVPA, there are several notable limitations. Firstly, due to the demographic distribution of AUD in treatment, data were collected exclusively from male subjects, and future research should give full consideration to including female subjects. Secondly, the co-occurrence of smoking and alcohol use disorders is common, and although our performed a partial correlation analysis to control for the effects of smoking, future studies could employ more rigorous controls to mitigate potential confounding effects of smoking. Thirdly, future studies should consider incorporating multimodal neuroimaging data to improve the classification of AUD from HCs. Additionally, it is important to investigate the efficacy connectivity among all brain regions as excluded regions may contain valuable information. This could lead to a better understanding of large-scale efficacy network connectivity abnormalities in individuals with AUD.