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Nogo Tasks fMRI Studies on Alcohol Effect on Inhibition
C H A P T E R
20 Social Drinking and Motor Inhibition: Evidences From FMRI Go/Nogo Tasks fMRI Studies on Alcohol Effect on Inhibition Elisa Schro¨der and Salvatore Campanella Laboratory of Medical Psychology and Addictology, ULB Neuroscience Institute (UNI), Universite´ Libre de Bruxelles (ULB), Brussels, Belgium
LIST OF ABBREVIATIONS RI ED AUD rIFG ACC DLPFC (pre)SMA RT
response inhibition error detection alcohol use disorder right inferior frontal gyrus anterior cingulate cortex dorsolateral prefrontal cortex (pre)supplementary motor area reaction time
INTRODUCTION Inhibition, as defined by the ability to withhold inadequate responses, is a key cognitive component of human behavior (Hofmann, Schmeichel, & Baddeley, 2012). Inhibition allows us to suppress responses that are inappropriate in a specific context (e.g., stopping from laughing at a funeral), to suppress no-longer relevant responses and to orient our actions toward a new goal (e.g., stopping yourself from turning the lights on when you enter a room where the lights are already turned on), or to protect ourselves from immediate threat (e.g., stopping from crossing the road when a fast-moving car appears) (MacLeod, 2007). With regard to alcohol use, the role of response inhibition (RI) is particularly crucial as it will, for instance, allow users to resist the temptation to consume heavy amounts of alcohol at parties or in
Neuroscience of Alcohol. DOI: https://doi.org/10.1016/B978-0-12-813125-1.00020-9
social contexts where everyone else is drinking (Ewing, Sakhardande, & Blakemore, 2014). When considering substance dependence, subjects display an inability to abstain from searching for and consuming the substance(s) of abuse (Luijten et al., 2014). Thereby, unsurprisingly, alcohol use disorder (AUD) have been linked to RI deficits, as alcoholic patients tend to display poorer RI performances than matched controls (Noe¨l et al., 2013; Petit et al., 2014; see Smith, Mattick, Jamadar, & Iredale, 2014 for a meta-analysis), and deficient inhibition is believed to be linked to the relapse rate in AUD, along with attentional bias towards alcohol-related cues (Wiers et al., 2007). Several studies have highlighted the neural correlates of RI in addictive behaviors (see Luijten et al., 2014 for a review), but surprisingly few have tried to underline the effect of alcohol use on neural correlates of RI in drinkers (but not addicts). This chapter will review fMRI studies having investigated RI in young drinkers thanks to a well-known paradigm, the Go/No-Go task.
THE GO/NO-GO TASK The Go/No-Go task is one of the most commonly used tasks to measure RI, along with the Stop Signal Task (Luijten et al., 2014). Participants are asked to
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respond as quickly as possible to a frequent Go stimulus, and to withhold their response to an infrequent No-Go stimulus (see Fig. 20.1). In this way, the task installs a dominant, automatic response towards the stimuli that the participants have to overcome when an infrequent No-Go stimulus appears (Kiefer, Marzinzik, Weisbrod, Scherg, & Spitzer, 1998). Behavioral performances at Go/No-Go tasks can be measured by the reaction times (RTs) (how quickly the subject will respond to Go stimuli), No-Go false-alarms (the subjects respond to a No-Go stimulus when they shouldn’t have done so) also referred to as the error rate, and Go-correct hits (the subject adequately responds to Go stimuli). The Go/No-Go task allows the study of the neural activity related to RI, thanks to the No-Go trials, and the neural activity related to error detection (ED), as the task typically generates relatively high errors rates thanks to the time pressure (Campanella et al., 2017). The neural correlates of RI in the Go/No-Go task are mainly located within the inferior frontal gyrus (IFG), the anterior cingulate cortex (ACC), the (pre) supplementary motor area (preSMA) and the dorsolateral prefrontal cortex (DLPFC), along with parietal and subcortical areas (such as the thalamus and basal ganglia) (Chambers, Garavan, & Bellgrove, 2009; Simmonds, Pekar, & Mostofsky, 2008; Steele et al., 2013). ED activates the ACC, preSMA, insula,
FIGURE 20.1 The Go/No-Go task. Subjects have to push on a button for every Go stimulus (here: the letter “M”), which is frequent and usually appears for 70% of trials, and to withhold for every NoGo trials (here: the letter “W”). Thanks to this design, subjects learn a dominant response (i.e., pushing as fast as possible whenever something appears on the screen as it is more probable to have a Go stimulus), and have to inhibit this response in front of a No-Go stimulus.
thalamus, and DLPFC (Hester, Fassbender, & Garavan, 2004; Menon, Adleman, White, Glover, & Reiss, 2001).
DIFFERENTIAL ACTIVATIONS RELATED TO ALCOHOL CONSUMPTION PATTERNS IN COLLEGE STUDENTS Ahmadi and colleagues (2013) compared fMRI and behavioral responses of heavy (n 5 56) versus light (n 5 36) drinkers (constituted of college students, ranging in age from 18 to 20) during a Go/No-Go task. At the behavioral level, they found that heavy drinkers were significantly slower for Go-correct hits and for No-Go false-alarms (erroneous motor response for a No-Go stimulus) RT’s. Heavy and light drinkers performed similarly in regards to error rates. Functional imaging results about No-Go correct rejections showed that heavy drinkers showed a decreased activity compared to light drinkers in the left SMA, bilateral ACC, bilateral parietal lobules, thalamus, putamen, hippocampus and right parahippocampal gyrus, bilateral middle frontal gyrus, and left superior temporal gyrus. Moreover, the Blood-oxygen-level dependent (BOLD) signal changes in some of these regions (the ACC, left postcentral, thalamus, middle frontal gyrus, and right putamen) correlated negatively with alcoholconsumption related scores (such as the number of blackouts). Overall, heavy drinkers were, therefore, slower and presented a decreased pattern of activity in the regions of interest for RI and attention. The authors suggested that their results were the consequence of a general decrease of the efficiency of inhibitory function in young heavy drinkers, mirroring the deficiencies observed in AUD. Ames and colleagues (2014) also compared heavy (n 5 21) and light (n 5 20) drinkers (college students, ranging in age from 18 to 22), with an adaptation of the Go/No-Go task in order to add alcohol-related images (bottle of beer) as No-Go stimuli, expecting an increase rate of false-alarms in heavy drinkers when confronted to alcohol-related cues. As in the study of Ahmadi and colleagues, overall, light drinkers performed better than heavy drinkers, specifically with an increase rate of Go-correct hits, but heavy drinkers did not display the expected increase in false-alarms on No-Go trials. However, with regards to the imaging results, heavy drinkers showed an increased activity in the right DLPFC and in the anterior and mid cingulate cortex. The alcohol-related stimuli were specifically related to an increased neural activity in heavy drinkers in the right anterior insula when compared to light drinkers. The increased activity observed in heavy drinkers was interpreted by the authors as a consequence of the presentation of the alcohol-related
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DIFFERENTIAL ACTIVATIONS RELATED TO ALCOHOL CONSUMPTION PATTERNS IN COLLEGE STUDENTS
stimuli, as the increased incentive value of the alcoholrelated cues may have biased the cognitive processing and saturated their working memory (as reflected by the activation of the DLPFC) and, therefore, resulted in a greater difficulty to withhold their response. More specifically, the authors suggested that the attentional bias generated by the alcohol-related stimuli might result from an increased exposure to alcohol. One main limitation to the interpretation of the study of Ames and colleagues relied on the fact that the design did not allowed the authors to differentiate the activity linked to the attentional bias towards alcohol-related cues from the activity linked to RI per se. Campanella and colleagues (2017) designed a “contextual Go/No-Go task” in which the alcohol-related cues were not substituted to the No-Go stimuli, but were presented as a long-lasting background upon which the Go or No-Go stimuli were presented (see Fig. 20.2). Doing so, the authors specifically tested behavioral and BOLD responses to the Go/No-Go task from heavy (n 5 19) versus light (n 5 17) drinkers,
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comparing the impact of the context (Alcohol Context, Nonalcohol Context and No Context) on RI and ED. First, the behavioral results both from heavy and light drinkers did not show any effect of context on RI. Second, heavy, and light drinkers did not differ in their behavioral performances. fMRI results regarding the correct No-Go trials (succeeded RI) did not reveal any difference between light and heavy drinkers. However, fMRI BOLD signals consequents to falsealarm on No-Go trials (failed RI) showed that heavy drinkers presented an increased activity in the left superior occipital, the left caudate, left amygdale, and right cerebellum areas when contrasted with light drinkers, while light drinkers showed an increased activity in the right inferior frontal, right middle cingulate, and left superior temporal regions when contrasted with heavy drinkers (see Fig. 20.3). The authors suggested that the lack of differences in BOLD signal in RI might index a still-efficient inhibitory control and play a “protective role” against dependence. Results on ED were interpreted by the authors as the
FIGURE 20.2
The contextual Go/No-Go Task. The design is similar to a classical Go/No-Go task, the subjects have to press a button for each Go stimulus (“M”) and prevent themselves from pushing for No-Go trials (“W”), but here the stimuli are displayed on a contextual background. Three kinds of context exist in this study: No Context (NC, black background), Nonalcohol related Context (NAC, neutral image) and Alcohol related Context (AC, image of alcohol). Source: From Campanella, S., Absil, J., Carbia Sinde, C., Schroder, E., Peigneux, P., Bourguignon, M., . . . De Tie`ge, X. (2017). Neural correlates of correct and failed response inhibition in heavy versus light social drinkers: an fMRI study during a go/nogo task by healthy participants. Brain Imaging and Behavior, 11(6), 1796 1811. doi:10.1007/s11682-016-9654-y. Note: Presented with the permission of Salvatore Campanella.
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FIGURE 20.3 Brain areas activated by false-alarm. The parcel on the left (“Failed Inhibitions”) represents the average of all participants (heavy and lights drinkers) in situations of a false-alarm. Parcels on the right illustrate the group differences in error-linked activity. Light drinkers (on top) exhibit stronger activation in “executive-based” areas, while heavy drinkers show a stronger “visually-driven” emotional reactivity. Source: From Campanella, S., Absil, J., Carbia Sinde, C., Schroder, E., Peigneux, P., Bourguignon, M., . . . De Tie`ge, X. (2017). Neural correlates of correct and failed response inhibition in heavy versus light social drinkers: an fMRI study during a go/no-go task by healthy participants. Brain Imaging and Behavior, 11(6), 1796 1811. doi:10.1007/s11682-016-9654-y. Note: Presented with the permission of Salvatore Campanella.
manifestation of a different type of strategy engaged by light versus heavy drinkers when facing an error: light drinkers recruited areas linked to RI, response selection and prediction of actions while heavy drinkers recruited areas associated with movement control, proactive slowing, and visual computing. The authors concluded that light drinkers disclosed an “executive-based response” to errors, and heavy drinkers a “visuallydriven emotional response” to errors. Interestingly, most of the subjects recruited in the aforementioned studies were qualified either as light or heavy drinkers, the last presenting a hazardous consumption pattern in the heavy drinker group, as defined by the Alcohol Use Disorders Identification Test (AUDIT) (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). In a recent study, Hatchard and colleagues (2017) pointed toward the effects of a low-level consumption pattern on neural functional activity. The authors compared the performances and BOLD signals of alcohol users (n 5 17) versus controls (n 5 11) recruited among young adults (age ranging from 19 to 21). The two groups did not differ in regards to their behavioral performances on the Go/No-Go task. fMRI results showed that alcohol users showed an increased activation in the left superior frontal gyrus and the left precentral gyrus, right superior parietal lobule and cerebellum, and in the left hippocampus and parahippocampal gyrus. The increased activity in the left superior frontal and precentral gyrus (regions thought to be involved in response competition and response
correction) for the alcohol users was interpreted as an indication of compensatory mechanisms, along the activation of the superior parietal lobe (associated with selfmonitoring of responses) and the cerebellum (associated with motor functioning). Moreover, the unexpected activation of the hippocampus might be associated, according to the authors, to a compensatory recruitment of additional areas displayed by alcohol users when they are “struggling” with the Go/No-Go task. Therefore, the results suggest that even restricted alcohol consumption might already impact the neural functioning of the brain in a situation requiring inhibition. Moreover, this increased activity displayed by drinkers might be predictive of a further escalation of the maximum number of drinks consumed in one occasion. Indeed, Worhunsky and colleagues (2016) studied the activity of the frontoparietal network in 36 young adults (of 18 years old) during a Go/No-Go task and observed that the increased engagement of this network successfully predicted the augmentation of the number of drinks 1 year later. Altogether, studies about social drinking and inhibitory control suggest a precocious impact of alcohol consumption, even at a low-level of consumption, on the functioning of brain areas related to RI and ED. Behavioral impairments linked to alcohol consumption remain unclear, as the results are inconsistent. However, the question about the causal relationship between alcohol consumption and neural dysfunction still needs to be addressed.
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DISRUPTED NEURAL SIGNATURE OF RI AS A PRECURSOR OR A CONSEQUENCE OF ALCOHOL USE?
DISRUPTED NEURAL SIGNATURE OF RI AS A PRECURSOR OR A CONSEQUENCE OF ALCOHOL USE? The longitudinal study of Norman and colleagues (2011) offers insights about the predictive factors of further alcohol abuse. Adolescents aged from 12 to 14 years old underwent an fMRI Go/No-Go task, and were then followed annually. Based on their evolution, subjects were then classified as future heavy users (n 5 21) or future controls (n 5 17). Future heavy users and future controls did not differ in regards to their behavioral performances. However, future heavy users displayed a decreased inhibition BOLD response to NoGo trials in the left DLPFC, left SMA, right inferior frontal and medial frontal, bilateral motor and left cingulate, left putamen and middle temporal, and inferior parietal regions bilaterally. A close pattern of decreased activation in the ventromedial prefrontal cortex as a predictor of further drug dependence symptoms (over an 18 month follow-up) has also been found by Mahmood and colleagues (2013) in older adolescents, between 16 and 19 years old. These results suggest an altered neural activation during RI in future heavy users, supporting the hypothesis that adolescents presenting a decreased neural activation linked to inhibition could be at risk of problem behaviors, such as alcohol use. However, the preservation of the behavioral performances suggests a relative efficiency of prefrontal mediated control. The authors pinpointed an alternative explanation about the decreased neural activation linked to RI: as
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the subjects are still in a period of brain development, the observed reduced activation might indicate a delayed maturation which could, in turn, be predictive of future heavy consumption of alcohol. About this aspect, the study of Wetherill and colleagues (2013) brought new perspectives in comparing behavioral and fMRI responses of adolescents to a Go/ No-Go task before any onset of heavy drinking, and 3 years later. A group of adolescents who transitioned into heavy drinking (n 5 20) and a group of matched nondrinking adolescents (n 5 20) were constituted. Results showed that, while behavioral performances at the task improved with age, no differences in behavioral performances were observed between the two groups. Nonetheless, fMRI results showed differential patterns of activation depending on the group: at baseline, before any onset of alcohol use, future heavy drinkers showed less activation for No-Go trials in the bilateral middle frontal gyri, the right inferior parietal lobule, the left putamen, and the left cerebellar tonsil. But at the follow-up, heavy drinkers showed greater activation in these regions (except for the left putamen) (see Fig. 20.4). The authors are connecting these results with the typical neural maturation observed in adolescents: activations tend to diminished with time as neural networks become more specialized, efficient and refined. Therefore, it is expected to observe a decrease in neural activation as adolescents grow older. However, it appeared that adolescents who have evolved towards heavy drinking showed an opposite pattern, with an increase of activation, suggesting alcohol may affect the typical neural development. FIGURE 20.4 Interaction between group (future heavy drinkers vs controls) and time (baseline vs follow-up 3 years later). At baseline, future heavy drinkers showed a general hypoactivation compared to controls in left Medial Frontal Gyrus (L MFG), left Putamen, left Cerebellar Tonsil, right Middle Frontal Gyrus, and right Inferior Parietal Lobule, suggesting a preexisting vulnerability. At the time of the follow-up, controls showed a decrease of their activation while heavy drinkers showed an increase of their activation in the same areas (except for the putamen), suggesting a stronger involvement in the task in order to be able to succeed it. Source: From Wetherill, R. R., Squeglia, L. M., Yang, T. T., & Tapert, S. F. (2013). A longitudinal examination of adolescent response inhibition: Neural differences before and after the initiation of heavy drinking. Psychopharmacology, 230(4), 663 671. doi:10.1007/s00213-013-3198-2. Note: Presented with the permission of Susan Tapert.
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Beyond the examination of future heavy or hazardous drinkers, such a pattern of decreased frontal activity during a Go/No-Go task has also been observed in youths with a family history of alcoholism. More specifically, youths with a positive familial history of alcoholism showed a decreased activation in the left middle frontal gyrus. The authors, therefore, suggested that this altered activity may underlie disinhibition and potentially lead to future AUD (Schweinsburg et al., 2004). It is interesting to note that an overall blunted activation pattern in the prefrontal cortical regions (e.g., the left middle frontal gyrus) following a failed inhibition in the Go/No-Go task during childhood appears to be predictive of a transition to a problematic substance use that is not specific to alcohol, as it may also concern illicit substance use (Heitzeg et al., 2014).
particular context, or to stop an already initiated response that is no longer relevant. Error detection Error detection and processing refers to the cognitive mechanisms implicated in the detection of an error, the emotional response to the error and the further adjustments made in order to avoid any further errors of this type. Alcohol use disorder AUD are referring to the patterns of alcohol drinking that are susceptible to have a harmful impact on physical or mental health, such as alcohol dependence and alcohol abuse. Reaction time The time a subject takes to produce a motor response to a stimulus. False-alarms errors (or commission errors) A false-alarm (or commission) error corresponds to a response given to a stimulus that had to be inhibited.
KEY FACTS Heavy/Hazardous/Binge Drinking What do those categories mean? The answer may vary depending on who is responding to the question.
CONCLUSIONS Altogether, these studies suggest that the mutual influences of RI and alcohol consumption are extremely narrow. On the one hand, subjects that will further develop a pattern of alcohol abuse seem to present specific vulnerabilities indexed by a decrease activity in, for instance, the frontal gyrus, parietal areas, and putamen (Ahmadi et al., 2013; Norman et al., 2011; Wetherill et al., 2013). On the other hand, subject who indeed transitioned into alcohol use (even with a restricted consumption) display increased activities in these regions, suggesting that a particular effort has to be invested in order to perform normally at the Go/No-Go task (Ames et al., 2014; Campanella et al., 2017; Hatchard et al., 2017; Wetherill et al., 2013). Thus, preexisting vulnerabilities may increase the probability of alcohol consumption and abuse (Thomsen, Osterland, Hesse, & Ewing, 2018), which in return might superimpose neural dysfunctions related the RI. The lack of consistent results on behavioral performances enhances the importance of neuroimaging techniques, as compensatory mechanisms might be at play and mask any reorganization of the functional activity consequent to specifics vulnerabilities. The results emphasize the crucial necessity to develop efficient prevention strategies regarding alcohol use and adolescent, as alcohol can significantly affect the developing brain even at very early stages of alcohol consumption.
MINI-DICTIONARY OF TERMS Response inhibition Response inhibition is thought as the mechanism that allows us to stop or abort inappropriate responses in a
• The National Institute on Alcohol Abuse and Alcoholism (NIAAA) defines binge drinking as a pattern of consumption that will bring blood alcohol concentration levels above 0.08 g/dL. The Substance Abuse and Mental Health Services Administration (SAMHSA, USA) speaks about binge drinking when a subject drinks 5 1 (for men) or 4 1 (for women) drinks on the same occasion. • Hazardous alcohol consumption is defined by a score superior or equal to 8 at the Alcohol Use Disorders Identification Test (AUDIT, Saunders et al., 1993). • Heavy alcohol use is defined by the SAMHSA as 5 or more episodes of binge drinking per month. Overall, definitions may vary tremendously, making it difficult to compare different studies when the targeted population is not well defined.
SUMMARY POINTS • Response inhibition is critical in addictive and substance abuse behaviors as it allows the subject to refrain from seeking and consuming the substance. • The results about behavioral performance of response inhibition in heavy drinkers, studied thanks to Go/No-Go tasks, are inconsistent as in most studies heavy and light drinkers perform similarly. • Neural correlates of response inhibition—indexed by an hypoactivation of the areas involved in response inhibition—in heavy drinkers show that the deterioration of response inhibition can be seen
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REFERENCES
prior the onset of alcohol use and might, therefore, be considered as a vulnerability factor. • A general hyperactivation of the areas involved in response inhibition in heavy drinkers is generally reported and appears as a manifestation of compensation mechanisms. Such activation patterns can be observed even when the alcohol consumption is low. • Results suggest that the impact of alcohol on response inhibition is extremely precocious.
References Ahmadi, A., Pearlson, G. D., Meda, S. A., Dager, A., Potenza, M. N., Rosen, R., . . . Stevens, M. C. (2013). Influence of alcohol use on neural response to Go/No-Go task in college drinkers. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 38(11), 2197 2208. Available from https://doi.org/10.1038/npp.2013.119. Ames, S. L., Wong, S. W., Bechara, A., Cappelli, C., Dust, M., Grenard, J. L., & Stacy, A. W. (2014). Neural correlates of a Go/ NoGo task with alcohol stimuli in light and heavy young drinkers. Behavioural Brain Research, 274, 382 389. Available from https://doi.org/10.1016/j.bbr.2014.08.039. Campanella, S., Absil, J., Carbia Sinde, C., Schroder, E., Peigneux, P., Bourguignon, M., . . . De Tie`ge, X. (2017). Neural correlates of correct and failed response inhibition in heavy versus light social drinkers: An fMRI study during a go/no-go task by healthy participants. Brain Imaging and Behavior, 11(6), 1796 1811. Available from https://doi.org/10.1007/s11682-016-9654-y. Chambers, C. D., Garavan, H., & Bellgrove, M. A. (2009). Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neuroscience and Biobehavioral Reviews, 33 (5), 631 646. Available from https://doi.org/10.1016/j.neubio rev.2008.08.016. Ewing, S. W. F., Sakhardande, A., & Blakemore, S.-J. (2014). The effect of alcohol consumption on the adolescent brain: A systematic review of MRI and fMRI studies of alcohol-using youth. NeuroImage Clinical, 5, 420 437. Available from https://doi.org/ 10.1016/j.nicl.2014.06.011. Hatchard, T., Mioduszewski, O., Fall, C., Byron-Alhassan, A., Fried, P., & Smith, A. M. (2017). Neural impact of low-level alcohol use on response inhibition: An fMRI investigation in young adults. Behavioural Brain Research, 329, 12 19. Available from https:// doi.org/10.1016/j.bbr.2017.04.032. Heitzeg, M. M., Nigg, J. T., Hardee, J. E., Soules, M., Steinberg, D., Zubieta, J. K., & Zucker, R. A. (2014). Left middle frontal gyrus response to inhibitory errors in children prospectively predicts early problem substance use. Drug and Alcohol Dependence, 141, 51 57. Available from https://doi.org/10.1016/j.drugalcdep. 2014.05.002. Hester, R., Fassbender, C., & Garavan, H. (2004). Individual differences in error processing: A review and reanalysis of three eventrelated fMRI studies using the GO/NOGO task. Cerebral Cortex, 14(9), 986 994. Available from https://doi.org/10.1093/cercor/ bhh059. Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174 180. Available from https://doi.org/10.1016/j.tics.2012.01.006. Kiefer, M., Marzinzik, F., Weisbrod, M., Scherg, M., & Spitzer, M. (1998). The time course of brain activations during response
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inhibition: Evidence from event-related potentials in a go/no go task. Neuroreport, 9(4), 765 770. Luijten, M., Machielsen, M. W. J., Veltman, D. J., Hester, R., de Haan, L., & Franken, I. H. A. (2014). Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people with substance dependence and behavioural addictions. Journal of Psychiatry & Neuroscience: JPN, 39(3), 149 169. MacLeod, C. M. (2007). The concept of inhibition in cognition. In D. S. Gorfein, & C. M. MacLeod (Eds.), Inhibition in cognition (pp. 3 23). Washington, DC: American Psychological Association. Available from http://dx.doi.org/10.1037/11587-001. Mahmood, O. M., Goldenberg, D., Thayer, R., Migliorini, R., Simmons, A. N., & Tapert, S. F. (2013). Adolescents’ fMRI activation to a response inhibition task predicts future substance use. Addictive Behaviors, 38(1), 1435 1441. Available from https://doi. org/10.1016/j.addbeh.2012.07.012. Menon, V., Adleman, N. E., White, C. D., Glover, G. H., & Reiss, A. L. (2001). Error-related brain activation during a Go/NoGo response inhibition task. Human Brain Mapping, 12(3), 131 143. Noe¨l, X., Van der Linden, M., Brevers, D., Campanella, S., Verbanck, P., Hanak, C., . . . Verbruggen, F. (2013). Separating intentional inhibition of prepotent responses and resistance to proactive interference in alcohol-dependent individuals. Drug and Alcohol Dependence, 128(3), 200 205. Available from https://doi.org/ 10.1016/j.drugalcdep.2012.08.021. Norman, A. L., Pulido, C., Squeglia, L. M., Spadoni, A. D., Paulus, M. P., & Tapert, S. F. (2011). Neural activation during inhibition predicts initiation of substance use in adolescence. Drug and Alcohol Dependence, 119(3), 216 223. Available from https://doi. org/10.1016/j.drugalcdep.2011.06.019. Petit, G., Cimochowska, A., Kornreich, C., Hanak, C., Verbanck, P., & Campanella, S. (2014). Neurophysiological correlates of response inhibition predict relapse in detoxified alcoholic patients: Some preliminary evidence from event-related potentials. Neuropsychiatric Disease and Treatment, 10, 1025 1037. Available from https://doi.org/10.2147/NDT.S61475. Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption--II. Addiction (Abingdon, England), 88(6), 791 804. Schweinsburg, A. D., Paulus, M. P., Barlett, V. C., Killeen, L. A., Caldwell, L. C., Pulido, C., . . . Tapert, S. F. (2004). An FMRI study of response inhibition in youths with a family history of alcoholism. Annals of the New York Academy of Sciences, 1021(1), 391 394. Available from https://doi.org/10.1196/annals.1308.050. Simmonds, D. J., Pekar, J. J., & Mostofsky, S. H. (2008). Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia, 46 (1), 224 232. Available from https://doi.org/10.1016/j. neuropsychologia.2007.07.015. Smith, J. L., Mattick, R. P., Jamadar, S. D., & Iredale, J. M. (2014). Deficits in behavioural inhibition in substance abuse and addiction: A meta-analysis. Drug & Alcohol Dependence, 145, 1 33. Available from https://doi.org/10.1016/j.drugalcdep.2014.08.009. Steele, V. R., Aharoni, E., Munro, G. E., Calhoun, V. D., Nyalakanti, P., Stevens, M. C., . . . Kiehl, K. A. (2013). A large scale (N 5 102) functional neuroimaging study of response inhibition in a Go/ NoGo task. Behavioural Brain Research, 256, 529 536. Available from https://doi.org/10.1016/j.bbr.2013.06.001. Thomsen, K. R., Osterland, T. B., Hesse, M., & Ewing, S. W. F. (2018). The intersection between response inhibition and substance use among adolescents. Addictive Behaviors, 78, 228 230. Available from https://doi.org/10.1016/j.addbeh.2017.11.043.
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20. SOCIAL DRINKING AND MOTOR INHIBITION
Wetherill, R. R., Squeglia, L. M., Yang, T. T., & Tapert, S. F. (2013). A longitudinal examination of adolescent response inhibition: Neural differences before and after the initiation of heavy drinking. Psychopharmacology, 230(4), 663 671. Available from https:// doi.org/10.1007/s00213-013-3198-2. Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C. M. E., Sher, K. J., . . . Stacy, A. W. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology,
Biochemistry, and Behavior, 86(2), 263 283. Available from https:// doi.org/10.1016/j.pbb.2006.09.021. Worhunsky, P. D., Dager, A. D., Meda, S. A., Khadka, S., Stevens, M. C., Austad, C. S., . . . Potenza, M. N. (2016). A preliminary prospective study of an escalation in ‘maximum daily drinks’, frontoparietal circuitry and impulsivity-related domains in young adult drinkers. Neuropsychopharmacology, 41(6), 1637. Available from https://doi.org/10.1038/npp.2015.332.
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20 Social Drinking and Motor Inhibition: Evidences From FMRI Go/Nogo Tasks fMRI Studies on Alcohol Effect on Inhibition Elisa Schro¨der and Salvatore Campanella Laboratory of Medical Psychology and Addictology, ULB Neuroscience Institute (UNI), Universite´ Libre de Bruxelles (ULB), Brussels, Belgium
LIST OF ABBREVIATIONS RI ED AUD rIFG ACC DLPFC (pre)SMA RT
response inhibition error detection alcohol use disorder right inferior frontal gyrus anterior cingulate cortex dorsolateral prefrontal cortex (pre)supplementary motor area reaction time
INTRODUCTION Inhibition, as defined by the ability to withhold inadequate responses, is a key cognitive component of human behavior (Hofmann, Schmeichel, & Baddeley, 2012). Inhibition allows us to suppress responses that are inappropriate in a specific context (e.g., stopping from laughing at a funeral), to suppress no-longer relevant responses and to orient our actions toward a new goal (e.g., stopping yourself from turning the lights on when you enter a room where the lights are already turned on), or to protect ourselves from immediate threat (e.g., stopping from crossing the road when a fast-moving car appears) (MacLeod, 2007). With regard to alcohol use, the role of response inhibition (RI) is particularly crucial as it will, for instance, allow users to resist the temptation to consume heavy amounts of alcohol at parties or in
Neuroscience of Alcohol. DOI: https://doi.org/10.1016/B978-0-12-813125-1.00020-9
social contexts where everyone else is drinking (Ewing, Sakhardande, & Blakemore, 2014). When considering substance dependence, subjects display an inability to abstain from searching for and consuming the substance(s) of abuse (Luijten et al., 2014). Thereby, unsurprisingly, alcohol use disorder (AUD) have been linked to RI deficits, as alcoholic patients tend to display poorer RI performances than matched controls (Noe¨l et al., 2013; Petit et al., 2014; see Smith, Mattick, Jamadar, & Iredale, 2014 for a meta-analysis), and deficient inhibition is believed to be linked to the relapse rate in AUD, along with attentional bias towards alcohol-related cues (Wiers et al., 2007). Several studies have highlighted the neural correlates of RI in addictive behaviors (see Luijten et al., 2014 for a review), but surprisingly few have tried to underline the effect of alcohol use on neural correlates of RI in drinkers (but not addicts). This chapter will review fMRI studies having investigated RI in young drinkers thanks to a well-known paradigm, the Go/No-Go task.
THE GO/NO-GO TASK The Go/No-Go task is one of the most commonly used tasks to measure RI, along with the Stop Signal Task (Luijten et al., 2014). Participants are asked to
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respond as quickly as possible to a frequent Go stimulus, and to withhold their response to an infrequent No-Go stimulus (see Fig. 20.1). In this way, the task installs a dominant, automatic response towards the stimuli that the participants have to overcome when an infrequent No-Go stimulus appears (Kiefer, Marzinzik, Weisbrod, Scherg, & Spitzer, 1998). Behavioral performances at Go/No-Go tasks can be measured by the reaction times (RTs) (how quickly the subject will respond to Go stimuli), No-Go false-alarms (the subjects respond to a No-Go stimulus when they shouldn’t have done so) also referred to as the error rate, and Go-correct hits (the subject adequately responds to Go stimuli). The Go/No-Go task allows the study of the neural activity related to RI, thanks to the No-Go trials, and the neural activity related to error detection (ED), as the task typically generates relatively high errors rates thanks to the time pressure (Campanella et al., 2017). The neural correlates of RI in the Go/No-Go task are mainly located within the inferior frontal gyrus (IFG), the anterior cingulate cortex (ACC), the (pre) supplementary motor area (preSMA) and the dorsolateral prefrontal cortex (DLPFC), along with parietal and subcortical areas (such as the thalamus and basal ganglia) (Chambers, Garavan, & Bellgrove, 2009; Simmonds, Pekar, & Mostofsky, 2008; Steele et al., 2013). ED activates the ACC, preSMA, insula,
FIGURE 20.1 The Go/No-Go task. Subjects have to push on a button for every Go stimulus (here: the letter “M”), which is frequent and usually appears for 70% of trials, and to withhold for every NoGo trials (here: the letter “W”). Thanks to this design, subjects learn a dominant response (i.e., pushing as fast as possible whenever something appears on the screen as it is more probable to have a Go stimulus), and have to inhibit this response in front of a No-Go stimulus.
thalamus, and DLPFC (Hester, Fassbender, & Garavan, 2004; Menon, Adleman, White, Glover, & Reiss, 2001).
DIFFERENTIAL ACTIVATIONS RELATED TO ALCOHOL CONSUMPTION PATTERNS IN COLLEGE STUDENTS Ahmadi and colleagues (2013) compared fMRI and behavioral responses of heavy (n 5 56) versus light (n 5 36) drinkers (constituted of college students, ranging in age from 18 to 20) during a Go/No-Go task. At the behavioral level, they found that heavy drinkers were significantly slower for Go-correct hits and for No-Go false-alarms (erroneous motor response for a No-Go stimulus) RT’s. Heavy and light drinkers performed similarly in regards to error rates. Functional imaging results about No-Go correct rejections showed that heavy drinkers showed a decreased activity compared to light drinkers in the left SMA, bilateral ACC, bilateral parietal lobules, thalamus, putamen, hippocampus and right parahippocampal gyrus, bilateral middle frontal gyrus, and left superior temporal gyrus. Moreover, the Blood-oxygen-level dependent (BOLD) signal changes in some of these regions (the ACC, left postcentral, thalamus, middle frontal gyrus, and right putamen) correlated negatively with alcoholconsumption related scores (such as the number of blackouts). Overall, heavy drinkers were, therefore, slower and presented a decreased pattern of activity in the regions of interest for RI and attention. The authors suggested that their results were the consequence of a general decrease of the efficiency of inhibitory function in young heavy drinkers, mirroring the deficiencies observed in AUD. Ames and colleagues (2014) also compared heavy (n 5 21) and light (n 5 20) drinkers (college students, ranging in age from 18 to 22), with an adaptation of the Go/No-Go task in order to add alcohol-related images (bottle of beer) as No-Go stimuli, expecting an increase rate of false-alarms in heavy drinkers when confronted to alcohol-related cues. As in the study of Ahmadi and colleagues, overall, light drinkers performed better than heavy drinkers, specifically with an increase rate of Go-correct hits, but heavy drinkers did not display the expected increase in false-alarms on No-Go trials. However, with regards to the imaging results, heavy drinkers showed an increased activity in the right DLPFC and in the anterior and mid cingulate cortex. The alcohol-related stimuli were specifically related to an increased neural activity in heavy drinkers in the right anterior insula when compared to light drinkers. The increased activity observed in heavy drinkers was interpreted by the authors as a consequence of the presentation of the alcohol-related
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DIFFERENTIAL ACTIVATIONS RELATED TO ALCOHOL CONSUMPTION PATTERNS IN COLLEGE STUDENTS
stimuli, as the increased incentive value of the alcoholrelated cues may have biased the cognitive processing and saturated their working memory (as reflected by the activation of the DLPFC) and, therefore, resulted in a greater difficulty to withhold their response. More specifically, the authors suggested that the attentional bias generated by the alcohol-related stimuli might result from an increased exposure to alcohol. One main limitation to the interpretation of the study of Ames and colleagues relied on the fact that the design did not allowed the authors to differentiate the activity linked to the attentional bias towards alcohol-related cues from the activity linked to RI per se. Campanella and colleagues (2017) designed a “contextual Go/No-Go task” in which the alcohol-related cues were not substituted to the No-Go stimuli, but were presented as a long-lasting background upon which the Go or No-Go stimuli were presented (see Fig. 20.2). Doing so, the authors specifically tested behavioral and BOLD responses to the Go/No-Go task from heavy (n 5 19) versus light (n 5 17) drinkers,
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comparing the impact of the context (Alcohol Context, Nonalcohol Context and No Context) on RI and ED. First, the behavioral results both from heavy and light drinkers did not show any effect of context on RI. Second, heavy, and light drinkers did not differ in their behavioral performances. fMRI results regarding the correct No-Go trials (succeeded RI) did not reveal any difference between light and heavy drinkers. However, fMRI BOLD signals consequents to falsealarm on No-Go trials (failed RI) showed that heavy drinkers presented an increased activity in the left superior occipital, the left caudate, left amygdale, and right cerebellum areas when contrasted with light drinkers, while light drinkers showed an increased activity in the right inferior frontal, right middle cingulate, and left superior temporal regions when contrasted with heavy drinkers (see Fig. 20.3). The authors suggested that the lack of differences in BOLD signal in RI might index a still-efficient inhibitory control and play a “protective role” against dependence. Results on ED were interpreted by the authors as the
FIGURE 20.2
The contextual Go/No-Go Task. The design is similar to a classical Go/No-Go task, the subjects have to press a button for each Go stimulus (“M”) and prevent themselves from pushing for No-Go trials (“W”), but here the stimuli are displayed on a contextual background. Three kinds of context exist in this study: No Context (NC, black background), Nonalcohol related Context (NAC, neutral image) and Alcohol related Context (AC, image of alcohol). Source: From Campanella, S., Absil, J., Carbia Sinde, C., Schroder, E., Peigneux, P., Bourguignon, M., . . . De Tie`ge, X. (2017). Neural correlates of correct and failed response inhibition in heavy versus light social drinkers: an fMRI study during a go/nogo task by healthy participants. Brain Imaging and Behavior, 11(6), 1796 1811. doi:10.1007/s11682-016-9654-y. Note: Presented with the permission of Salvatore Campanella.
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FIGURE 20.3 Brain areas activated by false-alarm. The parcel on the left (“Failed Inhibitions”) represents the average of all participants (heavy and lights drinkers) in situations of a false-alarm. Parcels on the right illustrate the group differences in error-linked activity. Light drinkers (on top) exhibit stronger activation in “executive-based” areas, while heavy drinkers show a stronger “visually-driven” emotional reactivity. Source: From Campanella, S., Absil, J., Carbia Sinde, C., Schroder, E., Peigneux, P., Bourguignon, M., . . . De Tie`ge, X. (2017). Neural correlates of correct and failed response inhibition in heavy versus light social drinkers: an fMRI study during a go/no-go task by healthy participants. Brain Imaging and Behavior, 11(6), 1796 1811. doi:10.1007/s11682-016-9654-y. Note: Presented with the permission of Salvatore Campanella.
manifestation of a different type of strategy engaged by light versus heavy drinkers when facing an error: light drinkers recruited areas linked to RI, response selection and prediction of actions while heavy drinkers recruited areas associated with movement control, proactive slowing, and visual computing. The authors concluded that light drinkers disclosed an “executive-based response” to errors, and heavy drinkers a “visuallydriven emotional response” to errors. Interestingly, most of the subjects recruited in the aforementioned studies were qualified either as light or heavy drinkers, the last presenting a hazardous consumption pattern in the heavy drinker group, as defined by the Alcohol Use Disorders Identification Test (AUDIT) (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). In a recent study, Hatchard and colleagues (2017) pointed toward the effects of a low-level consumption pattern on neural functional activity. The authors compared the performances and BOLD signals of alcohol users (n 5 17) versus controls (n 5 11) recruited among young adults (age ranging from 19 to 21). The two groups did not differ in regards to their behavioral performances on the Go/No-Go task. fMRI results showed that alcohol users showed an increased activation in the left superior frontal gyrus and the left precentral gyrus, right superior parietal lobule and cerebellum, and in the left hippocampus and parahippocampal gyrus. The increased activity in the left superior frontal and precentral gyrus (regions thought to be involved in response competition and response
correction) for the alcohol users was interpreted as an indication of compensatory mechanisms, along the activation of the superior parietal lobe (associated with selfmonitoring of responses) and the cerebellum (associated with motor functioning). Moreover, the unexpected activation of the hippocampus might be associated, according to the authors, to a compensatory recruitment of additional areas displayed by alcohol users when they are “struggling” with the Go/No-Go task. Therefore, the results suggest that even restricted alcohol consumption might already impact the neural functioning of the brain in a situation requiring inhibition. Moreover, this increased activity displayed by drinkers might be predictive of a further escalation of the maximum number of drinks consumed in one occasion. Indeed, Worhunsky and colleagues (2016) studied the activity of the frontoparietal network in 36 young adults (of 18 years old) during a Go/No-Go task and observed that the increased engagement of this network successfully predicted the augmentation of the number of drinks 1 year later. Altogether, studies about social drinking and inhibitory control suggest a precocious impact of alcohol consumption, even at a low-level of consumption, on the functioning of brain areas related to RI and ED. Behavioral impairments linked to alcohol consumption remain unclear, as the results are inconsistent. However, the question about the causal relationship between alcohol consumption and neural dysfunction still needs to be addressed.
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DISRUPTED NEURAL SIGNATURE OF RI AS A PRECURSOR OR A CONSEQUENCE OF ALCOHOL USE?
DISRUPTED NEURAL SIGNATURE OF RI AS A PRECURSOR OR A CONSEQUENCE OF ALCOHOL USE? The longitudinal study of Norman and colleagues (2011) offers insights about the predictive factors of further alcohol abuse. Adolescents aged from 12 to 14 years old underwent an fMRI Go/No-Go task, and were then followed annually. Based on their evolution, subjects were then classified as future heavy users (n 5 21) or future controls (n 5 17). Future heavy users and future controls did not differ in regards to their behavioral performances. However, future heavy users displayed a decreased inhibition BOLD response to NoGo trials in the left DLPFC, left SMA, right inferior frontal and medial frontal, bilateral motor and left cingulate, left putamen and middle temporal, and inferior parietal regions bilaterally. A close pattern of decreased activation in the ventromedial prefrontal cortex as a predictor of further drug dependence symptoms (over an 18 month follow-up) has also been found by Mahmood and colleagues (2013) in older adolescents, between 16 and 19 years old. These results suggest an altered neural activation during RI in future heavy users, supporting the hypothesis that adolescents presenting a decreased neural activation linked to inhibition could be at risk of problem behaviors, such as alcohol use. However, the preservation of the behavioral performances suggests a relative efficiency of prefrontal mediated control. The authors pinpointed an alternative explanation about the decreased neural activation linked to RI: as
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the subjects are still in a period of brain development, the observed reduced activation might indicate a delayed maturation which could, in turn, be predictive of future heavy consumption of alcohol. About this aspect, the study of Wetherill and colleagues (2013) brought new perspectives in comparing behavioral and fMRI responses of adolescents to a Go/ No-Go task before any onset of heavy drinking, and 3 years later. A group of adolescents who transitioned into heavy drinking (n 5 20) and a group of matched nondrinking adolescents (n 5 20) were constituted. Results showed that, while behavioral performances at the task improved with age, no differences in behavioral performances were observed between the two groups. Nonetheless, fMRI results showed differential patterns of activation depending on the group: at baseline, before any onset of alcohol use, future heavy drinkers showed less activation for No-Go trials in the bilateral middle frontal gyri, the right inferior parietal lobule, the left putamen, and the left cerebellar tonsil. But at the follow-up, heavy drinkers showed greater activation in these regions (except for the left putamen) (see Fig. 20.4). The authors are connecting these results with the typical neural maturation observed in adolescents: activations tend to diminished with time as neural networks become more specialized, efficient and refined. Therefore, it is expected to observe a decrease in neural activation as adolescents grow older. However, it appeared that adolescents who have evolved towards heavy drinking showed an opposite pattern, with an increase of activation, suggesting alcohol may affect the typical neural development. FIGURE 20.4 Interaction between group (future heavy drinkers vs controls) and time (baseline vs follow-up 3 years later). At baseline, future heavy drinkers showed a general hypoactivation compared to controls in left Medial Frontal Gyrus (L MFG), left Putamen, left Cerebellar Tonsil, right Middle Frontal Gyrus, and right Inferior Parietal Lobule, suggesting a preexisting vulnerability. At the time of the follow-up, controls showed a decrease of their activation while heavy drinkers showed an increase of their activation in the same areas (except for the putamen), suggesting a stronger involvement in the task in order to be able to succeed it. Source: From Wetherill, R. R., Squeglia, L. M., Yang, T. T., & Tapert, S. F. (2013). A longitudinal examination of adolescent response inhibition: Neural differences before and after the initiation of heavy drinking. Psychopharmacology, 230(4), 663 671. doi:10.1007/s00213-013-3198-2. Note: Presented with the permission of Susan Tapert.
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Beyond the examination of future heavy or hazardous drinkers, such a pattern of decreased frontal activity during a Go/No-Go task has also been observed in youths with a family history of alcoholism. More specifically, youths with a positive familial history of alcoholism showed a decreased activation in the left middle frontal gyrus. The authors, therefore, suggested that this altered activity may underlie disinhibition and potentially lead to future AUD (Schweinsburg et al., 2004). It is interesting to note that an overall blunted activation pattern in the prefrontal cortical regions (e.g., the left middle frontal gyrus) following a failed inhibition in the Go/No-Go task during childhood appears to be predictive of a transition to a problematic substance use that is not specific to alcohol, as it may also concern illicit substance use (Heitzeg et al., 2014).
particular context, or to stop an already initiated response that is no longer relevant. Error detection Error detection and processing refers to the cognitive mechanisms implicated in the detection of an error, the emotional response to the error and the further adjustments made in order to avoid any further errors of this type. Alcohol use disorder AUD are referring to the patterns of alcohol drinking that are susceptible to have a harmful impact on physical or mental health, such as alcohol dependence and alcohol abuse. Reaction time The time a subject takes to produce a motor response to a stimulus. False-alarms errors (or commission errors) A false-alarm (or commission) error corresponds to a response given to a stimulus that had to be inhibited.
KEY FACTS Heavy/Hazardous/Binge Drinking What do those categories mean? The answer may vary depending on who is responding to the question.
CONCLUSIONS Altogether, these studies suggest that the mutual influences of RI and alcohol consumption are extremely narrow. On the one hand, subjects that will further develop a pattern of alcohol abuse seem to present specific vulnerabilities indexed by a decrease activity in, for instance, the frontal gyrus, parietal areas, and putamen (Ahmadi et al., 2013; Norman et al., 2011; Wetherill et al., 2013). On the other hand, subject who indeed transitioned into alcohol use (even with a restricted consumption) display increased activities in these regions, suggesting that a particular effort has to be invested in order to perform normally at the Go/No-Go task (Ames et al., 2014; Campanella et al., 2017; Hatchard et al., 2017; Wetherill et al., 2013). Thus, preexisting vulnerabilities may increase the probability of alcohol consumption and abuse (Thomsen, Osterland, Hesse, & Ewing, 2018), which in return might superimpose neural dysfunctions related the RI. The lack of consistent results on behavioral performances enhances the importance of neuroimaging techniques, as compensatory mechanisms might be at play and mask any reorganization of the functional activity consequent to specifics vulnerabilities. The results emphasize the crucial necessity to develop efficient prevention strategies regarding alcohol use and adolescent, as alcohol can significantly affect the developing brain even at very early stages of alcohol consumption.
MINI-DICTIONARY OF TERMS Response inhibition Response inhibition is thought as the mechanism that allows us to stop or abort inappropriate responses in a
• The National Institute on Alcohol Abuse and Alcoholism (NIAAA) defines binge drinking as a pattern of consumption that will bring blood alcohol concentration levels above 0.08 g/dL. The Substance Abuse and Mental Health Services Administration (SAMHSA, USA) speaks about binge drinking when a subject drinks 5 1 (for men) or 4 1 (for women) drinks on the same occasion. • Hazardous alcohol consumption is defined by a score superior or equal to 8 at the Alcohol Use Disorders Identification Test (AUDIT, Saunders et al., 1993). • Heavy alcohol use is defined by the SAMHSA as 5 or more episodes of binge drinking per month. Overall, definitions may vary tremendously, making it difficult to compare different studies when the targeted population is not well defined.
SUMMARY POINTS • Response inhibition is critical in addictive and substance abuse behaviors as it allows the subject to refrain from seeking and consuming the substance. • The results about behavioral performance of response inhibition in heavy drinkers, studied thanks to Go/No-Go tasks, are inconsistent as in most studies heavy and light drinkers perform similarly. • Neural correlates of response inhibition—indexed by an hypoactivation of the areas involved in response inhibition—in heavy drinkers show that the deterioration of response inhibition can be seen
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REFERENCES
prior the onset of alcohol use and might, therefore, be considered as a vulnerability factor. • A general hyperactivation of the areas involved in response inhibition in heavy drinkers is generally reported and appears as a manifestation of compensation mechanisms. Such activation patterns can be observed even when the alcohol consumption is low. • Results suggest that the impact of alcohol on response inhibition is extremely precocious.
References Ahmadi, A., Pearlson, G. D., Meda, S. A., Dager, A., Potenza, M. N., Rosen, R., . . . Stevens, M. C. (2013). Influence of alcohol use on neural response to Go/No-Go task in college drinkers. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 38(11), 2197 2208. Available from https://doi.org/10.1038/npp.2013.119. Ames, S. L., Wong, S. W., Bechara, A., Cappelli, C., Dust, M., Grenard, J. L., & Stacy, A. W. (2014). Neural correlates of a Go/ NoGo task with alcohol stimuli in light and heavy young drinkers. Behavioural Brain Research, 274, 382 389. Available from https://doi.org/10.1016/j.bbr.2014.08.039. Campanella, S., Absil, J., Carbia Sinde, C., Schroder, E., Peigneux, P., Bourguignon, M., . . . De Tie`ge, X. (2017). Neural correlates of correct and failed response inhibition in heavy versus light social drinkers: An fMRI study during a go/no-go task by healthy participants. Brain Imaging and Behavior, 11(6), 1796 1811. Available from https://doi.org/10.1007/s11682-016-9654-y. Chambers, C. D., Garavan, H., & Bellgrove, M. A. (2009). Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neuroscience and Biobehavioral Reviews, 33 (5), 631 646. Available from https://doi.org/10.1016/j.neubio rev.2008.08.016. Ewing, S. W. F., Sakhardande, A., & Blakemore, S.-J. (2014). The effect of alcohol consumption on the adolescent brain: A systematic review of MRI and fMRI studies of alcohol-using youth. NeuroImage Clinical, 5, 420 437. Available from https://doi.org/ 10.1016/j.nicl.2014.06.011. Hatchard, T., Mioduszewski, O., Fall, C., Byron-Alhassan, A., Fried, P., & Smith, A. M. (2017). Neural impact of low-level alcohol use on response inhibition: An fMRI investigation in young adults. Behavioural Brain Research, 329, 12 19. Available from https:// doi.org/10.1016/j.bbr.2017.04.032. Heitzeg, M. M., Nigg, J. T., Hardee, J. E., Soules, M., Steinberg, D., Zubieta, J. K., & Zucker, R. A. (2014). Left middle frontal gyrus response to inhibitory errors in children prospectively predicts early problem substance use. Drug and Alcohol Dependence, 141, 51 57. Available from https://doi.org/10.1016/j.drugalcdep. 2014.05.002. Hester, R., Fassbender, C., & Garavan, H. (2004). Individual differences in error processing: A review and reanalysis of three eventrelated fMRI studies using the GO/NOGO task. Cerebral Cortex, 14(9), 986 994. Available from https://doi.org/10.1093/cercor/ bhh059. Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174 180. Available from https://doi.org/10.1016/j.tics.2012.01.006. Kiefer, M., Marzinzik, F., Weisbrod, M., Scherg, M., & Spitzer, M. (1998). The time course of brain activations during response
193
inhibition: Evidence from event-related potentials in a go/no go task. Neuroreport, 9(4), 765 770. Luijten, M., Machielsen, M. W. J., Veltman, D. J., Hester, R., de Haan, L., & Franken, I. H. A. (2014). Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people with substance dependence and behavioural addictions. Journal of Psychiatry & Neuroscience: JPN, 39(3), 149 169. MacLeod, C. M. (2007). The concept of inhibition in cognition. In D. S. Gorfein, & C. M. MacLeod (Eds.), Inhibition in cognition (pp. 3 23). Washington, DC: American Psychological Association. Available from http://dx.doi.org/10.1037/11587-001. Mahmood, O. M., Goldenberg, D., Thayer, R., Migliorini, R., Simmons, A. N., & Tapert, S. F. (2013). Adolescents’ fMRI activation to a response inhibition task predicts future substance use. Addictive Behaviors, 38(1), 1435 1441. Available from https://doi. org/10.1016/j.addbeh.2012.07.012. Menon, V., Adleman, N. E., White, C. D., Glover, G. H., & Reiss, A. L. (2001). Error-related brain activation during a Go/NoGo response inhibition task. Human Brain Mapping, 12(3), 131 143. Noe¨l, X., Van der Linden, M., Brevers, D., Campanella, S., Verbanck, P., Hanak, C., . . . Verbruggen, F. (2013). Separating intentional inhibition of prepotent responses and resistance to proactive interference in alcohol-dependent individuals. Drug and Alcohol Dependence, 128(3), 200 205. Available from https://doi.org/ 10.1016/j.drugalcdep.2012.08.021. Norman, A. L., Pulido, C., Squeglia, L. M., Spadoni, A. D., Paulus, M. P., & Tapert, S. F. (2011). Neural activation during inhibition predicts initiation of substance use in adolescence. Drug and Alcohol Dependence, 119(3), 216 223. Available from https://doi. org/10.1016/j.drugalcdep.2011.06.019. Petit, G., Cimochowska, A., Kornreich, C., Hanak, C., Verbanck, P., & Campanella, S. (2014). Neurophysiological correlates of response inhibition predict relapse in detoxified alcoholic patients: Some preliminary evidence from event-related potentials. Neuropsychiatric Disease and Treatment, 10, 1025 1037. Available from https://doi.org/10.2147/NDT.S61475. Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption--II. Addiction (Abingdon, England), 88(6), 791 804. Schweinsburg, A. D., Paulus, M. P., Barlett, V. C., Killeen, L. A., Caldwell, L. C., Pulido, C., . . . Tapert, S. F. (2004). An FMRI study of response inhibition in youths with a family history of alcoholism. Annals of the New York Academy of Sciences, 1021(1), 391 394. Available from https://doi.org/10.1196/annals.1308.050. Simmonds, D. J., Pekar, J. J., & Mostofsky, S. H. (2008). Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia, 46 (1), 224 232. Available from https://doi.org/10.1016/j. neuropsychologia.2007.07.015. Smith, J. L., Mattick, R. P., Jamadar, S. D., & Iredale, J. M. (2014). Deficits in behavioural inhibition in substance abuse and addiction: A meta-analysis. Drug & Alcohol Dependence, 145, 1 33. Available from https://doi.org/10.1016/j.drugalcdep.2014.08.009. Steele, V. R., Aharoni, E., Munro, G. E., Calhoun, V. D., Nyalakanti, P., Stevens, M. C., . . . Kiehl, K. A. (2013). A large scale (N 5 102) functional neuroimaging study of response inhibition in a Go/ NoGo task. Behavioural Brain Research, 256, 529 536. Available from https://doi.org/10.1016/j.bbr.2013.06.001. Thomsen, K. R., Osterland, T. B., Hesse, M., & Ewing, S. W. F. (2018). The intersection between response inhibition and substance use among adolescents. Addictive Behaviors, 78, 228 230. Available from https://doi.org/10.1016/j.addbeh.2017.11.043.
II. NEUROBIOLOGY
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20. SOCIAL DRINKING AND MOTOR INHIBITION
Wetherill, R. R., Squeglia, L. M., Yang, T. T., & Tapert, S. F. (2013). A longitudinal examination of adolescent response inhibition: Neural differences before and after the initiation of heavy drinking. Psychopharmacology, 230(4), 663 671. Available from https:// doi.org/10.1007/s00213-013-3198-2. Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C. M. E., Sher, K. J., . . . Stacy, A. W. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology,
Biochemistry, and Behavior, 86(2), 263 283. Available from https:// doi.org/10.1016/j.pbb.2006.09.021. Worhunsky, P. D., Dager, A. D., Meda, S. A., Khadka, S., Stevens, M. C., Austad, C. S., . . . Potenza, M. N. (2016). A preliminary prospective study of an escalation in ‘maximum daily drinks’, frontoparietal circuitry and impulsivity-related domains in young adult drinkers. Neuropsychopharmacology, 41(6), 1637. Available from https://doi.org/10.1038/npp.2015.332.
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