توسعه کار واکنش پذیری نشانه الکل

Abstract Background The physiological and cognitive reactions provoked by alcohol cues, as compared to non-alcohol cues, can predict future drinking. Alcohol cue reactivity tasks have been developed; however, most were created for use with alcohol use disordered individuals and utilize limited or only partially standardized stimuli. This project systematically created an alcohol cue reactivity task for studies with non-drinkers, using well-characterized stimuli. Objectives We comprehensively standardized 60 alcohol and 60 non-alcohol beverage pictures using ratings from young non-drinkers (N = 82) on affective and perceptual features. Results A statistical matching approach yielded 26 matched alcohol–non-alcohol picture pairs matched on valence, arousal, image complexity, brightness, and hue. The task was piloted and further refined to 22 picture pairs. An 8-minute, 32-second event-related task was created using a random stimulus function for optimized condition timing and systematic presentation of the images. Conclusions The long-term objectives of this project are to utilize this task with non-drinking youth to investigate how reactivity to alcohol stimuli may predict alcohol use initiation and escalation, to help identify the role of exposure to alcohol stimuli on the subsequent development of alcohol-related problems.

Introduction Subjective alcohol craving and responses to alcohol-specific cues (e.g., pictures of alcoholic beverages) have been shown to produce different physiological and cognitive interference responses in contrast with exposure to non-alcohol cues (e.g., delayed reaction times on attentional tasks) (Bruce & Jones, 2004). Specifically, prior investigations have shown that alcoholics report higher subjective reactions (e.g., craving) to alcohol stimuli (e.g., pictures) when compared to non-alcoholic stimuli (Drobes, 2002) and social drinkers (George et al., 2001). Similarly, adults (Monti et al., 1987) and adolescents (Thomas and Deas, 2005 and Thomas et al., 2005) with alcohol dependence have demonstrated differential physiological responses, such as increased salivation, to the sight and smell of alcoholic beverages as compared to non-alcoholic beverages. This population has also evidenced cognitive interference (e.g., delayed reaction times) when presented with alcohol cues (e.g., alcoholic beverages or alcohol-related words) (Bauer and Cox, 1998 and Sayette et al., 1994). Social drinkers also show delayed reaction times (Bruce & Jones, 2004) or alcohol bias (Townshend & Duka, 2001) when presented with alcohol stimuli, which often correlated with the level of alcohol involvement. Cue reactivity paradigms have been used for tailoring alcohol interventions (Drummond and Glautier, 1994 and Rohsenow et al., 2001), evaluating the efficacy of alcohol treatment programs (Hutchison et al., 2006 and Schneider et al., 2001), and examining degree of reactivity in relation to duration of abstinence (Monti et al., 1993). In brief, from the concentration of studies with alcohol using individuals, alcohol cue reactivity appears to develop through personal alcohol use. Only a few studies have examined alcohol cue reactivity among individuals at risk for alcohol use disorders (AUD) ( Tapert et al., 2003). In the present study, an alcohol cue reactivity task was developed using stimuli ratings of non-drinkers, who have previously been shown to have different subjective affective responses to alcohol beverage images as compared to drinkers ( Pulido, Mok, Brown, & Tapert, 2009). This alcohol cue reactivity task was developed for future use with non-drinkers at risk for AUD, to help determine whether attentional bias is developed only through personal alcohol use experiences, or if it can also be learned through modeling, and whether cue reactivity can predict subsequent drinking behavior. Ultimately, these findings can help in the development of effective AUD prevention programming. 1.1. Alcohol cue reactivity studies and limitations Prior to assessing alcohol cue reactivity, a task for such purpose needs to be developed. Stimuli standardization and implementation into a task is time consuming, and several research groups have substantially advanced our understanding of stimulus characteristics important to consider when creating an alcohol cue reactivity task (Braus et al., 2001, Grusser et al., 2000, Grusser et al., 2004, Wrase et al., 2002 and Wrase et al., 2007). First, stimulus affective and perceptual characteristics are important to consider. Fortunately, some investigators have highlighted the importance of stimulus standardization and reported efforts to standardize task materials prior to task creation (e.g., Grusser et al., 2000 and Wrase et al., 2002). However, standardization procedures have typically explored only one dimension such as valence (e.g., Bauer & Cox, 1998) or the visual complexity of the stimuli (e.g., Bruce & Jones, 2004). A task simultaneously considering multiple task-relevant stimulus parameters is yet to be developed. Second, the standardization of a limited quantity of stimuli (e.g., Grusser et al., 2000) restricts its utility for creating an alcohol cue reactivity task since stimuli repetition can reduce statistical power or even confound results (Schwartz et al., 2003). For instance, a modest quantity of items has been managed in some studies by supplementing the task with stimuli standardized with divergent procedures (e.g., George et al., 2001, Hermann et al., 2006 and Myrick et al., 2004). Third, in cases where alcohol visual stimuli standardization has been undertaken with small (Grusser et al., 2000 and Lang et al., 1999) and large (Stritzke et al., 2004 and Wrase et al., 2002) item pools, participants' alcohol use characteristics are often unknown, despite this being an important correlate of alcohol stimulus ratings (Pulido, Mok et al., 2009). Finally, although various tasks are currently available to assess alcohol cue reactivity among AUD individuals, no task has been developed to specifically examine alcohol cue reactivity among non-drinking individuals at risk for AUD. This study utilized a database of affective (i.e., valence and arousal) and perceptual (i.e., familiarity and image complexity) ratings from 82 non-drinking individuals and objective brightness and color measures to statistically match 120 alcohol and non-alcohol beverage pictures for an alcohol cue reactivity task. The task developed here will improve upon existing alcohol cue reactivity paradigms in that it was developed by means of a novel and stringent procedure, simultaneously taking into consideration multiple recommended task development procedures. These included using a large item pool, collecting ratings from non-drinkers and covering relevant parameters, using an objective matching approach, optimizing task design for fMRI, including an active control condition, and conducting a pilot study. The careful creation of the task will allow for more accurate neural assessment of alcohol cue reactivity and comparison of results across samples.

. Results In summary, 120 beverage pictures were subjected to a statistical matching process, involving exclusion on the basis of visual inspection and ratings of familiarity in a sample of 82 non-drinking youth. This yielded 26 alcohol and 26 non-alcohol beverage pictures that were optimally matched in terms of affective and perceptual ratings as well as objective indices of brightness and hue content. Ratings and other image parameters averaged across the 26 alcohol and 26 non-alcohol pictures are shown in Table 2. In this sample of non-drinkers, the non-alcohol pictures tended to be rated more favorably (i.e., positive valence) while, on average, the alcohol pictures were rated as neutral. Arousal, complexity, brightness, and color content indices were not statistically different between the two picture types. Table 2. Mean parameter ratings by picture type for the 26 picture pairs (N = 82). Alcohol Non-alcohol M (SD) M (SD) Valence a⁎ 5.0 (1.2) 3.9 (0.9) Arousal a 5.2 (1.4) 4.9 (1.1) Complexity 3.2 (0.6) 3.2 (0.7) Brightness 140.6 (51.5) 135.5 (47.6) Red 134.7 (49.4) 124.4 (47.0) Green 100.8 (50.5) 110.0 (49.0) Blue 88.7 (56.1) 106.0 (52.7) ⁎ p ≤ 0.001. a For valence and arousal, a rating of 5 represents neutral while smaller ratings indicate more pleasantness and arousal while viewing the picture (range 1 to 9). Table options The 26 picture pairs and respective active control condition were administered to a new adolescent sample (N = 21) for further evaluation of behavioral responses. Based on responses of these adolescents to the 26 picture pairs, poor matching and item recognition was observed for some stimuli, resulting in the removal of 4 of the 26 picture pairs. Parameter ratings and objective indices of brightness and hue content for the 22 alcohol and non-alcohol picture pairs, according to this non-drinking sample (N = 82), remained significant for valence and non-significant for arousal, complexity, brightness and color. Mood, age, gender, ethnicity, and paternal AUD were explored as predictors for the 22 picture pairs' valence, arousal, and complexity ratings. Participant's mood, age, ethnicity, and paternal AUD were not significant predictors of picture ratings. However, gender was significant at predicting non-alcohol pictures valence (females M = 3.7, SD = 1.2, males M = 4.2, SD = 0.7; t(65) = − 2.2, p = 0.03) and alcohol pictures complexity ratings (females M = 3.6, SD = 0.6, males M = 3.1, SD = 0.7; t(80) = 2.9, p < 0.01). Specifically, females, when compared to males, rated non-alcohol pictures as being more pleasant and alcohol pictures as being more perceptually complex. RSFgen was used again to generate a random stimulus function for the 22 alcohol and non-alcohol picture pairs and active control conditions (see Fig. 2); they were then programmed into E-Prime. A final task was created as an event-related task with 4 conditions (i.e., alcohol, non-alcohol, and alcohol and non-alcohol active controls) each with 22 stimuli. The task begins with a 12-second fixation (rest) period. The alcohol and non-alcohol stimuli are each presented 4 times (i.e., 88 trials per condition) while the active control stimuli are presented once (i.e., 22 trials per condition) to result in 220 trials. Each trial is presented for 750 ms (Heinz et al., 2007) with a 1250 ms inter-stimulus interval (i.e., blank screen), and 15 intermittent fixation periods (i.e., screen with a centered cross-hair) of 2, 4, or 6 s (i.e., for a total of 60 s of fixation throughout the task). During future functional magnetic resonance imaging data analysis, the fixation and active control conditions provide the opportunity to contrast activation to alcohol and non-alcohol pictures to a rest (i.e., fixation) and visual (i.e., active control) baselines. The alcohol cue reactivity task is 8 min and 32 s in duration. Task instructions are to press a key within 2000 ms of stimuli presentation in response to whether participants Like (left button), feel Neutral (down button), or Dislike (right button) seeing the beverage picture. Ratings and reaction times are logged for statistical analysis. A practice task with non-beverage pictures was created for establishing task demands outside the scanner. Portion of alcohol cue reactivity task reference function generated by AFNI ... Fig. 2. Portion of alcohol cue reactivity task reference function generated by AFNI RSFgen. Task is 8 min, 32 s in duration, with 2-second trials. The x-axis represents the first 101 s of the task; the y-axis represents the task conditions (alcohol and non-alcohol active control conditions were collapsed into one category). The horizontal lines of the blue graph represent the condition and its duration during the time course of the task. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)