Impact of polysubstance use on social and non-affective cognitive performance among treatment-seeking individuals with alcohol use disorders

Journal Pre-proofs Impact of Polysubstance Use on Social and Non-Affective Cognitive Per‐ formance among Treatment-Seeking Individuals with Alcohol Use Disorders Ben Lewis, Christian C. Garcia, Riley Bohan, Sara Jo Nixon PII: DOI: Reference:

S0306-4603(19)31291-2 https://doi.org/10.1016/j.addbeh.2020.106359 AB 106359

To appear in:

Addictive Behaviors Addictive Behaviors

Received Date: Revised Date: Accepted Date:

25 October 2019 22 January 2020 17 February 2020

Please cite this article as: B. Lewis, C.C. Garcia, R. Bohan, S.J. Nixon, Impact of Polysubstance Use on Social and Non-Affective Cognitive Performance among Treatment-Seeking Individuals with Alcohol Use Disorders, Addictive Behaviors Addictive Behaviors (2020), doi: https://doi.org/10.1016/j.addbeh.2020.106359

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© 2020 Published by Elsevier Ltd.

Impact of Polysubstance Use on Social and Non-Affective Cognitive Performance among TreatmentSeeking Individuals with Alcohol Use Disorders

Ben Lewis, Ph.D.1,2,3, Christian C. Garcia 2,3, Riley Bohan 3 & Sara Jo Nixon, Ph.D.1,2,3 1Department 2Department 3Center

of Psychiatry, University of Florida, Gainesville, FL, 32610, USA of Psychology, University of Florida, Gainesville, FL, 32610, USA

for Addiction Research & Education, University of Florida, Gainesville, FL, 32610, USA

Corresponding Author: Ben Lewis, Ph.D. University of Florida Department of Psychiatry Center for Addiction Research & Education PO Box 100256 Gainesville, FL, 32610 Telephone: (352) 294-8724 Fax: (352) 294-0197 [email protected]

Key Words: Alcohol Use Disorder, Polysubstance Use, Emotion Processing, Social Cognition, Face Processing

Abstract Background Individuals with alcohol use disorder (AUD) display deficits across a range of cognitive processes. Decrements in social cognition may be particularly important for interpersonal functioning and post-treatment adaptation. Although social cognitive deficits are associated with chronic use of numerous substances, the role of polysubstance use in AUD-associated deficits remains largely unaddressed. Methods Community volunteers (n=49; 22 men) and inpatient treatment-seekers with AUD were administered neurocognitive tasks indexing emotion processing and non-affective cognitive functioning. Tasks included an emotion discrimination task, a working memory task with affective stimuli, a general face processing (control) task, two measures of executive function, and two measures of visual spatial function. AUD subgroups included individuals with no recent (6-month) polysubstance use (AUD-Only; n=22; 15 men), and those with at least weekly use (Poly-SU; n=22; 18 men). Results Poly-SU individuals evinced disadvantaged performance relative to other groups on the emotion discrimination task [ps≤.001], affective working memory task [ps≤.050], and two executive function measures [ps≤.051]. No differences were observed for visual spatial functioning [ps≥.498] or general face processing [ps≥.190]. No performance differences between AUD-Only and community volunteers were noted. Conclusions Results extend the emerging literature exploring emotion processing in AUD and add to the established literature regarding cognitive deficits in this population. The data suggest that among individuals with AUD, those with polysubstance use may be particularly vulnerable to deficits in decoding emotional face content. The current work highlights the need to incorporate more nuanced and careful considerations of polysubstance use in the design and analysis for future investigations of alcohol-associated deficits in emotion processing.

Background Individuals with alcohol use disorder (AUD) display impairments in neurocognitive function, frequently including deficits in executive function (for reviews see Bates et al., 2002; Sullivan et al., 2010; Oscar-Berman et al., 2014; La Berre et al., 2017). While the nature and severity of cognitive perturbations are heterogeneous, current estimates indicate 50%-80% of AUD individuals are affected (Bates et al., 2002), with 30%-40% exhibiting clinically relevant levels of compromise throughout the first 2 months of abstinence (Diagnostic & Statistical Manual of Mental Disorders Fifth Edition, American Psychiatric Association, 2013). Although the literature interrogating the nature of these deficits is substantive, at least two facets remain understudied, including the degree to which social cognition is impacted and the extent to which polysubstance use affects outcomes; both are addressed in the current study.

Social interaction and interpersonal functioning depend on perception, identification, and implementation of cues transmitting affective information. A growing literature has described disruptions across these processes among individuals with AUDs, specifically including deficits in the capacities to interpret affective states, feelings, and emotions of others (e.g., Philippot et al., 1999; Maurage et al., 2011a; for reviews see Uekermann & Daum, 2008; Onuoha et al., 2016; Le Berre, 2019). While the broad range of tasks indexing emotion identification has included body postures (e.g., Maurage et al., 2009), vocal prosody (e.g., Monnot et al., 2001), and music (Kornreich et al., 2013), emotional facial expressions (EFEs) are employed most commonly and appear particularly sensitive to alcohol-related compromise (e.g., Frigerio et al., 2002; Oscar-Berman et al., 1990; Philippot et al., 1999; Townshend and Duka, 2003; Foisy et al., 2005; for reviews see Castellano et al., 2015; Donadon & Osorio, 2014). This work reveals AUD-associated deficits in identification accuracy and processing speed (e.g., Maurage et al., 2008b, Kornreich et al., 2003; Foisy et al., 2007), altered neural responses to emotionally valent stimuli (e.g., Marinkovic et al., 2009; Maurage et al., 2008a; Salloum et al., 2007), and impairments in judging intensity which vary by emotion (e.g., overestimation of anger, underestimation of sadness; Maurage et al., 2009). In addition to these “first-person” measures, AUD-associated decrements persist in more complex indices of emotional reasoning and affective theory of mind, including humor processing (e.g., Uekermann et al., 2007), social error (e.g., Cox et al., 2018), and capacity to perceive the emotional perspective of others (e.g., Maurage et al., 2011).

Several groups have interrogated EFE processing in samples with other substance use disorders (SUDs; e.g., Verdejo-Garcia et al., 2013; Bayrakci et al., 2015; Yip & Lee, 2006; Henry et al., 2009). In a recent meta-analysis, Castellano and colleagues (2015) reported that despite substantial heterogeneity within the AUD and SUD literatures, the estimated effect sizes between literatures were remarkably comparable, i.e., Cohen’s ds of 0.67 and 0.65, respectively. Unfortunately, the few examinations explicitly addressing polysubstance use (e.g., Fernandez-Serrano et al., 2010) lack specific comparison of use within AUD contexts.

Given the prevalence of polysubstance use in AUD populations, among whom approximately one third are estimated to have lifetime histories of comorbid SUDs (Staines et al., 2001), the common practice of excluding polysubstance using individuals from AUD-focused examination substantially limits sample representation and ecological validity. The paucity of attention to these issues represents a substantive gap in the extant literature, and is the focus of the current study.

We conducted a cross-sectional, between-groups analysis of performance across several measures, including an EFE processing task, an attend/ignore working memory task with emotionally salient stimuli, and four nonaffective tasks (two executive function; two visual processing). Groups included community controls (CCs) and treatment-seeking individuals with AUD, subgrouped into those with significant current polysubstance use (PolySU) and those without (AUD-Only). We have previously reported deficits in executive functioning and emotion processing in a sample of AUD individuals overlapping the current group (Hoffman et al., 2019), thus we expected both AUD groups would perform more poorly relative to CCs. We hypothesized that if alcohol use patterns/severity were equivalent between groups, deficits among Poly-SU individuals would be compounded by consequences of additional substance use, relative to other groups. However, given the equivalent effect sizes between AUD and SUD populations regarding emotion processing deficits (Castellano et al., 2015), potential differences between Poly-SU and AUD-Only individuals under conditions of non-equivalent alcohol consumption/severity remained an empirical question. Of further interest was the consistency of group differences across the variety of affective and non-affective cognitive measures. Materials and Methods

All participants provided written informed consent and were compensated for their time. The University of Florida Medical IRB approved all procedures. Participants Participants (N=93) included 49 CCs (22 men) and 44 inpatient treatment seekers with AUD, including subgroups of 22 AUD-Only (15 men) and 22 Poly-SU individuals (18 men). Participants provided basic demographic (e.g., age, education) information and self-reported medical histories. Substance use histories were collected, including chronicity, quantity, and frequency (Quantity Frequency Index; QFI; Cahalan et al., 1969) of recent consumption. Probabilistic psychiatric disorders were assessed with the computerized Diagnostic Interview Schedule, Version IV (cDIS-IV; Robins et al., 2000). Utilization of the alcohol craving questionnaire (Singleton et al., 2000) facilitated characterization of DSM-5 symptom severity. AUD groups met DSM-5 criteria for moderateto-severe AUD and were at least 21 days abstinent. Individuals were designated AUD-Only if they reported no recent (6 month) use of other substances (excluding nicotine and less than weekly cannabis use). Poly-SU individuals reported greater than weekly use of at least one substance not including alcohol, nicotine, or cannabis. Substance use data were gathered explicitly in the context of non-prescription use. In the current sample, this included use of opioids, psychostimulants, and/or benzodiazepines. Among CCs, histories of substance use disorders were exclusionary, as were recent drinking histories (i.e., >2/3 drink per day average, for women/men, respectively) suggestive of patterns that significantly exceeded “low-risk” guidelines (Department of Health & Human Services, 2015). Negative consequences of recent alcohol and drug use were quantified using the Short Inventory of Problems (SIP; Miller et al., 1996). The alcohol-specific SIP-2R (Miller et al., 1996) and a parallel form assessing drug use (SIP-DU) were both employed. A derived consequence score was computed using the highest value across substances for each SIP item. SIP subscales address physical, intrapersonal, work/financial, impulse control, and interpersonal consequences. Given the relevance of emotion processing deficits to interpersonal functioning (e.g., Hoffman et al., 2019), analyses included both total and interpersonal scores. Study inclusion was limited to individuals between 25 and 59 years of age with between 10 and 16 years of education. Exclusionary conditions included: 1) significant neurologic disorder/insult (e.g., stroke); 2) medical conditions (e.g. epilepsy) challenging interpretation of neurobehavioral function; 3) current medications

compromising neurobehavioral function (e.g., benzodiazepines); 4) lifetime psychotic or bipolar disorder; and 5) current panic disorders or social phobias. On testing day, negative results for breath alcohol content (Intoxylizer® 400PA; CMI, Inc., Owensboro, KY), urine toxicology screen for substance use (10-panel screen including cannabis, benzodiazepines, psychostimulants, opioids; Alere San Diego Inc., San Diego CA), and pregnancy tests were required for continued participation in all groups. Face Processing Tasks EFE processing was assessed with an emotion judgement task (EJT; adapted from Maurage et al., 2008b). To facilitate interpretation of EJT performance, a sex judgement task (SJT) provided control for non-affective facial processing. A two-choice discrimination procedure required responding on a two-button response pad with the index finger of the dominant hand. Task instructions emphasized the importance of response speed and accuracy. Task stimuli were adapted from the Pictures of Facial Affect stimulus set (Ekman & Friesen, 1976). Response windows were initiated and terminated with stimulus onset and offset (1500ms presentation time). Interstimulus intervals were 300ms. Exemplar stimuli from both tasks are presented in Figure 1. Emotion Judgement Task

For each face model, four facial expressions (happy, angry, sad and neutral) were

used to create the experimental stimuli. Task stimuli were created using digital morphing software (FantaMorph) to combine each of eight (4 men; 4 women) models’ neutral images with each of their emotional images, at two levels of intensity. Thus, 6 stimuli were generated for each model, including happy/neutral, angry/neutral, and sad/neutral combinations at 65% and 95% levels of emotion intensity. Preceding each task block, participants were informed of the two emotions to be discriminated (e.g., Sad vs. Happy) and their corresponding response buttons, which were alternated across blocks. Each block contained only a single pair of emotions to be discriminated, with all combinations of unique face model (8), emotion (2), and morph level (2) included (32 trials/block). Sequences of stimuli presentation were pseudorandomized such that sequential repetition of images containing either the same poser or same sex/morph/emotion combination was rare (<5% chance per block). Each of the three emotion pairs were repeated across 5 blocks (15 blocks total), with no sequential pair repetition. Sex Judgement Task Stimuli were created from pairs of male and female face models. Different models were used in the SJT and EJT tasks. Each of 8 models was used in only one of the 4 male/female pairs. Neutral faces

from each unique pair were used to generate morphed face stimuli at 65% and 95% for each sex. Thus, 16 unique stimuli were generated, with 4 stimuli from each of the following categories: 1) 95% female; 2) 65% female; 3) 65% male; and 4) 95% male. Each stimulus was presented once per block, with pseudorandom distribution to avoid sequential pair/morph repetitions. In all 16 blocks, participants indicated whether the face was more masculine or feminine. Emotional Attend/Ignore Working Memory Task The capacity to appropriately attend to or ignore emotionally salient stimuli constitutes a significant, if oftenoverlooked, facet of emotion processing. Thus, the current work incorporated an attend/ignore working memory task, modified from Gazzaley and colleagues (2005) to incorporate images from the FACES stimuli set (Ebner et al., 2010). The task includes presentation of two “face” stimuli and two “scene” stimuli per trial (i.e., 4 target stimuli per trial). On any given trial, participants were instructed to attend to only one of the two stimuli sets; scene stimuli were utilized due to their discernibility from facial stimuli. All stimuli were grayscale. Facial stimuli were evenly distributed between male/female face models across the task, but same-sex stimuli were maintained within each trial. Target stimuli were presented for 800ms each, with 200ms ISIs. All possible target sequences (e.g., Face-SceneFace-Scene) were utilized, with equal pseudorandom distribution across trials. After presentation of the four target stimuli, a 9000ms delay was introduced, with only a fixation cross presented onscreen. Following the delay, a probe image was presented for 1500ms. Participants used a two-button response pad to indicate whether the probe image matched (50% trials) or did not match (50% trials) stimuli in the target set (4000ms response period). A simplified diagram of this sequence is presented in Figure 2. Two instructional sets were utilized. Participants were instructed to either “Remember scenes but ignore faces” or “Remember faces but ignore scenes”. Order of instruction sets was counterbalanced, with equal distribution across participants. Probe images remained consistent with instructions (i.e., all probes images were scenes for ‘remember scenes’ condition). Both sets included instruction to respond “as quickly and accurately as possible”. Following each instruction set, participants completed a block of 72 trials. Emotionality of facial stimuli was varied within each block, with each trial including either neutral, happy, or fearful faces (24 trials/block for each). Non-Affective Cognitive Measures

Selection of non-affective measures was directed to indexing executive and visual processing. The former were selected based on extant literature suggesting relationships with social cognitive processes. The latter, to elucidate any differences in visual processing which might potentially impact performance on the visuallydependent emotion processing tasks. Executive functions were assessed with standard administrations of the Digit Symbol Substitution Task (DSST; Wechsler, 1981; Reitan & Wolfson, 1985) and Trail-Making Test Form B (TMT-B; Reitan, 1955; Reitan & Wolfson, 1985). Visual processing abilitites were assessed with two measures. Mental rotation abilities were assessed using the “Little Man” Task (LMT; Acker & Acker, 1982), in which participants must discriminate in which hand (i.e., Left vs. Right) a cartoon figure is holding an object. Presentation of the figure varied on two axes, including presentation facing toward/away from the viewer, and presentation upside-down/rightside-up, creating four possible presentation axis combinations with equal distribution across trials. A visual-perceptual analysis task (VPAT; Acker & Acker, 1982) assessed visual discrimination abilities. In each trial, three complex figures were presented onscreen, one of which deviated slightly from the other two identical figures. Participants were instructed to select the non-matching figure. All tasks included instruction to respond “as quickly and accurately as possible”. Data Analysis Planned analyses were conducted using PROC MIXED in SAS Version 9.4. All models were fit using restricted maximum likelihood estimation. Alpha was set at p=.05. Group (CC vs. AUD-Only vs. Poly-SU) was a fixed factor in all analyses. For EJT, SJT, and Attend/Ignore analyses, task condition (Attend/Ignore: Remember Faces vs. Ignore Faces), Face Content ([EJT: Happy vs. Angry vs. Sad]; [Attend/Ignore: Happy vs. Neutral vs. Fearful]; [SJT: Male vs. Female]) and Face Morph (EJT/SJT: 65% vs. 95%) were fixed and repeated factors. All models included Group by repeated factor interaction terms as fixed effects. Accuracy and reaction time (correct responses only) were dependent measures. Following planned analyses, two sets of exploratory analyses were conducted: 1) Informed by results of demographic and drinking analyses, which indicated differences between AUD groups on several relevant measures, post hoc covariate analyses were conducted; 2) To examine relationships between DVs, pairwise correlations were conducted among a constrained set of outcomes (accuracy measures from emotion processing tasks; composite scores for executive function and visual processing). To characterize potential group differences in these relationships, Fisher’s r-to-z transformations were conducted to compare correlation

coefficients between groups. These analyses served to both aid interpretation and provide preliminary guidance for future investigations. Results Participants

Participants (N=93 [49 CCs; 22 AUD-Only; 22 Poly-SU]) included Caucasian (n=67),

Black/African-American (n=17), Asian/Asian American (n=2), and American Indian (n=1) individuals. 6 participants endorsed “Other”, or failed to endorse a racial group. Five participants endorsed Hispanic ethnicity. Among individuals in the Poly-SU group, recent substance use included at least weekly use of cannabis (27%), psychostimulants (55%), opioids (55%), and benzodiazepines (32%). Consistent with the extant literature, differences in education, affective symptomatology, quantities of alcohol consumption, and negative consequences of drinking were observed between CCs and AUD groups. AUD-Only individuals were substantially older than the Poly-SU group, had later onset of alcohol problems, later initial entry into treatment, higher average alcohol consumption, and lower derived SIP scores (both total and interpersonal subscale). No difference between AUD subgroups was observed for days of alcohol abstinence. Descriptive statistics and summary of results are presented in Table 1. To further characterize substance use among AUD subgroups, proportional endorsements of lifetime problem use (excluding alcohol) were compared; problem use histories were endorsed by approximately 27% of the AUD-Only group and all (100%) participants in the Poly-SU group. Emotion Judgement Task Poly-SU individuals were less accurate in decoding face emotions [F(2,87)=8.17, p<.001], with contrasts confirming reduced accuracy relative to CC and AUD-Only groups [ts≥3.58, ps≤.001], which did not differ [p=.508]. Reaction time results indicated a significant group effect [F(2,87)=7.61, p<.001], albeit with unexpected directionality; group comparisons indicated AUD-Only individuals responded more rapidly relative to CC and Poly-SU groups [ts≥3.04, ps≤.003], which did not differ [p=.173]. No group interactions with either emotion or morph level were observed in accuracy or reaction time models. Results are depicted in Figure 3. Sex Judgement Task

To afford comparison with effects observed in the EJT task, full SJT univariate and

group contrast results are presented, regardless of significance. No effect of group [F(2,88)=0.89, p=.415] or significant contrasts [ts≤1.32, ps≥.190] were observed for SJT accuracy. Reaction time analyses yielded no group effect [F(2,87)=1.85, p=.163], although group contrasts suggested a trend toward slower responding in

ALC-Poly individuals relative to ALC-Only individuals [t=1.81, p=.074]. No other differences we noted [ts≤1.60, ps≥.112]. Results are depicted in Figure 4. Emotional Attend/Ignore Working Memory Task

Poly-SU individuals displayed accuracy deficits in the

attend/ignore task [F(2,87)=3.45, p=.036], with contrasts confirming lower accuracies relative to CC and AUDOnly groups [ts≥1.98, ps≤.050], which did not differ [p=.322]. Analysis of reaction time yielded similar results, with Poly-SU individuals responding more slowly than other groups [F(2,87)=3.85, p=.025]. Contrasts confirmed delayed responding relative to CC and AUD-Only groups [ts≥2.03, ps≤.045], which did not differ [p=.255]. No interactions with instruction set or face emotion were observed for accuracy or reaction time measures. Results are depicted in Figure 5. Non-Affective Measures

TMT-B results revealed longer time-to-completion among Poly-SU individuals

[F(2,88)=5.71, p=.005], with contrasts confirming disadvantaged performance relative to both CC and AUD-Only groups [ts≥1.98, ps≤.051], which were indistinguishable [p=.309]. Similarly, Poly-SU individuals translated fewer symbols in the DSST [F(2,88)=6.59, p=.002], relative to both other groups [ts≥2.60, ps≤.011], which did not differ [p=.634]. In contrast to these executive measures, no group differences were observed in either visual processing task [Fs≤0.49, ps≥.498]. Results are depicted in Figure 6. Covariate Analyses Given the differences between AUD-Only and Poly-SU groups on age, average alcohol consumption, substance use consequences, onset of alcohol problems, and initial treatment entry, these variables were considered as potential covariates. Correlation analyses revealed no significant associations between DVs and either consequences, problem onset, or initial treatment entry. Relationships with age and alcohol consumption were modest and not consistently observed, (r=.28 for age and EJT accuracy; r=.27 for QFI and attend/ignore accuracy), but appeared sufficient to warrant inclusion in covariate analyses. However, excepting reaction time measures, for which age had an expected positive association, neither covariate accounted for significant variance in DVs. In all cases, the significance and directionality of group effects persisted. Exploratory/Descriptive Analyses

Given the paucity of reports comparing performance either between

emotion processing tasks, or between emotion processing and non-affective measures, we conducted exploratory analyses to characterize relationships between DVs. Pearson correlations were conducted between accuracy measures on the affective tasks and composite scores of executive and visual spatial function

(averaged z-scores of TMT-B/DSST and LMT/VPAT, respectively). Pairwise correlations between all four measures were significant, including a particularly strong association between affective measures (r=.82). Correlation coefficients for the full sample are reported in Table 2a. Fisher’s r-to-z transformations were employed to facilitate comparison of coefficients between groups. Coefficients were equivalent between AUD subgroups across all four comparisons (zs≤1.36, ps≥.174), thus subgroups were combined to facilitate comparison between CC and AUD groups. Correlations between performance on visual spatial and affective tasks were stronger among CCs (ps≤.001). In contrast, relationships between executive and affective task performance appeared stronger among AUD individuals (ps≤.061). Correlation matrices for each group are presented in Tables 2b/c, respectively, with p-values indicating between-group significance.

Discussion Individuals with AUD who endorsed polysubstance use displayed disadvantaged performance in tasks probing emotion processing, attention to emotionality, and non-affective measures of executive function. These findings are consistent with diverse literatures examining neurocognitive concomitants of chronic alcohol/substance use. However, the observed performance disadvantages among this group, relative to non-polysubstance using AUD individuals, are novel. These findings contribute to several extant bodies of literature, including studies of emotion processing in chronic substance users and investigations attempting to disentangle complex consequences of polysubstance use.

Results were consistent with the hypothesis that polysubstance use would exacerbate neurocognitive deficits in treatment-seeking individuals with AUD. However, we proposed this hypothesis under conditions of similar alcohol use patterns and severity. Severity measures for negative consequences of drinking (SIP-2R) were comparable between AUD-Only and Poly-SU individuals, however appropriate comparison may have been challenged by ceiling effects, given the exceptionally high scores in both groups. Notably, comparison of the derived SIP score identified greater use consequences in the Poly-SU group. Consistent with observed emotion processing deficits, Poly-SU individuals also reported more severe interpersonal consequences of use (SIP subscale). Consideration of between-group drinking patterns revealed the AUD-Only group first experienced alcohol-related problems approximately six years later than the Poly-SU group, and were approximately ten

years older when first entering treatment. Taken together, these findings suggest that Poly-SU individuals experienced more severe substance use consequences and drinking trajectories, consistent with their disadvantaged cognitive performance. However, the substantially greater quantities of daily alcohol consumed by AUD-Only individuals highlights the multivariate nature of this comparison and challenges between-group severity assessment.

Emotional face processing deficits observed among the Poly-SU group are broadly consistent with those described in other (non-alcohol) substance use disorders, specifically including studies employing EFE paradigms (e.g., Verdejo-Garcia et al., 2007; Craparo et al., 2016; Foisy et al., 2005). However, while emotion processing impairments are reported in AUD and SUD populations, few studies have attempted to disentangle complex relationships regarding polysubstance use or explicitly compared AUD subgroups on the basis of their polysubstance use. Given estimates that approximately two thirds of individuals with AUDs endorse recent polysubstance use and approximately one third have lifetime histories of comorbid substance use disorders (Staines et al., 2001), this gap is somewhat surprising. In much of the relatively small literature focusing on emotion processing in AUD, polysubstance use is commonly addressed via exclusion of individuals with comorbid SUDs (e.g., Kornreich et al., 2013; Dethier et al., 2014; Maurage et al., 2008b; Uekermann et al., 2005). While appropriate, the exclusion of comorbid use disorders fails to eliminate significant subclinical use, which remains largely unaddressed in these studies. Although less common, some either fail to address use (e.g., Foisy et al., 2007; Kornreich et al., 2001) or describe use among the sample without further investigation or statistical control (e.g., Salloum et al., 2007; Lewis et al., 2019). Among the notable exceptions, Kornreich and colleagues (2003) examined EFE processing among several SUD groups, including AUD-Only individuals and those with comorbid opioid use disorders. These subgroups were indistinguishable from one another, but displayed deficits relative to controls. While Poly-SU performance in the current work was consistent with Kornreich’s observations, the equivalence between AUD-Only and CC groups was inconsistent with much of the extant literature, but not unprecedented (e.g., Dethier et al., 2013; Uekermann et al., 2005). This discrepancy may be related to specifics of the EFE tasks utilized, which often vary substantially across studies. The current EFE task was modified from Maurage et al., 2008b, and included conditions under which emotionality was salient and relatively easily discriminated, with mean accuracies across groups ranging from ~84-91%. In contrast, the

paradigm utilized by Kornreich and colleagues employed less discriminable stimuli (e.g., morph levels of 30%) and required selection from a larger array of eight potential emotions, resulting in accuracies of <50%, even among controls. As noted by Fein et al (2010), increasingly difficult tasks are more likely to reveal group differences in EFE decoding. Thus, the current task may have been insufficient to detect relatively subtle differences in the AUD-Only group, but adequate for observation of more exaggerated deficits among Poly-SU individuals. While plausible, this interpretation fails to explain either the similar pattern of results observed in the attend/ignore task or the between group differences noted in other studies utilizing the task (e.g., Maurage et al., 2008b). However, an advantage of this task lies in its capacity to demonstrate the potential functional impact of affective processing deficits. For instance, despite easily discriminable stimuli (e.g., Figure 1a), individuals in the Poly-SU group misidentified emotions at a rate of over 50% higher than that of community controls.

While EFE performance is likely impacted by individual differences in the degree to which emotionally-laden stimuli may tax attentional resources, EFE judgement paradigms provide an insufficient means by which to evaluate susceptibility to affective distractors. To address this, we utilized a modified attend/ignore working memory task (Gazzaley et al., 2005), in which the capacity to ignore task-irrelevant information (including emotional faces) should advantage performance. However, performance disadvantages in the Poly-SU group persisted across task conditions, i.e., Poly-SU individuals were less accurate in remembering target images regardless of the task relevance or emotional salience of the faces. While no group by condition interactions were observed, the conditions functioned as anticipated (e.g., greater difficulties ignoring faces vs scenes and negative vs. positive emotions). While disadvantaged Poly-SU performance is notable, the lack of sensitivity to task conditions challenges interpretation. Given the deficits in TMT-B and DSST performance, one potential interpretation is that these results reflect working memory deficits, with no specific contributions of emotion processing or attention to emotionality, despite the affective components of the task. This interpretation is supported by the strong correlation (r=.65) between attend/ignore performance and executive functioning among the AUD groups. Notably, although relevance of face content varied by task condition, the emotional content of the faces remained irrelevant across instructional sets. Thus, an alternative interpretation is that inappropriate attention to emotionality may have impacted both instructional sets, impairing both maintenance of scenespecific memories during the ‘remember scenes’ condition and encoding of face-specific features during the

‘remember faces’ condition. This assertion is supported by the exceptionally strong correlation across groups (r=.82) between performance on both emotion processing tasks. Importantly, these interpretations are not mutually exclusive; both provide guiding framework for future extensions of this work.

Results from non-affective cognitive measures were generally consistent with an extensive literature identifying particular susceptibility of executive functioning in AUD (e.g., Le Berre et al., 2017; Pitel et al., 2007; ), including AUD groups with polysubstance use (Nixon et al., 1998), as well as some sparing of visual spatial function in these populations (Nixon et al., 1998). The pattern of group differences across affective and non-affective measures was interesting, but not unexpected given an extensive literature associating the capacity to infer mental states of others with multiple executive processes (e.g., Wade et al., 2018). However, empirical study of relationships between executive functions and emotion-specific inferences are limited, even in community samples. Work by Maurage and colleagues (2011b, 2016) suggests that relative to non-affective mental inferences, which appear largely conserved, perceptions of emotionality may be particularly vulnerable to AUDassociated insult. Thus, the most parsimonious interpretation of associated affective/executive performance deficits among individuals with AUD is shared vulnerability to alcohol effects, perhaps due to shared reliance on prefrontal cortical functioning, as suggested in reviews conducted by Uekermann & Daum (2008) and Le Berre (2019). While results from the current Poly-SU sample are consistent with this interpretation, the difference between AUD subgroups and equivalence between AUD-Only and CC groups both suggest that use of additional substances in AUD samples may have an underappreciated contribution.

Limitations

Several limitations to this work should be noted: 1) Substance use in the Poly-SU group was

heterogeneous, including use of multiple drug classes at frequencies varying from weekly to daily use. While the observed differences relative to AUD-Only individuals are notable, extensions of this work statistically powered to differentiate contributions of specific drug classes and use patterns remain critical. 2) The cross-sectional nature of the study allows for characterization of recently detoxified individuals, but precludes generalization to functioning prior to treatment entry or during mid- to long-term sobriety. Future investigations of emotion processing employing longitudinal designs will substantially improve generalizability and better clarify the potential role of emotion processing in sustained recovery. 3) The current work includes only behavioral and self-

report data. Utilization of neuroimaging and/or electrophysiological methods will be critical to elucidating neural mechanisms underlying these observed behavioral differences. 4) While the employment of a well-validated and commonly-used stimuli set is a strength of this study, the set is limited to images of Caucasian individuals. The use of multi-racial stimuli sets or racially ambiguous stimuli would improve the generalizability of findings. 5) The sample size (n=22) in both AUD subgroups was modest, potentially resulting in lower-than-optimal power, especially given the heterogeneity in substances/patterns among the POLY-SU group.

Summary Taken together, our results extend the emerging literature exploring emotion processing in AUD and add to the established literature regarding cognitive deficits in this population. They suggest that among individuals with AUD, those with chronic polysubstance use may be particularly vulnerable to deficits in decoding emotional face content. While results from the attend/ignore task imply greater sensitivity to emotionally salient distractors among this polysubstance using group, further investigation is required to disentangle sensitivity to emotional stimuli from the observed deficits in executive functions. Given recent and growing attention to emotion processing in the AUD/SUD literatures and the range of unanswered questions regarding import to recovery, relapse, and function, the current work highlights the need to incorporate more nuanced and careful considerations of polysubstance use in the design and analysis of future emotion processing investigations.

Statement 1: Role of Funding Sources Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Grant Number R01 AA022456 (Sara Jo Nixon, PI). Further support was provided by K01AA026893 (Ben Lewis, PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Statement 2: Contributors SJN designed the study and wrote the protocol. CCG contributed to data collection, organization and verification. RB and CCG conducted preliminary analyses. RB conducted preparatory literature searches/review. BL contributed to task design, data collection/analysis, and wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.

Statement 3: Conflict of Interest All authors declare that they have no conflict of interest.

Statement 4: Acknowledgements The authors thank Robert Prather, Julianne Price, Lauren Hoffman, Ian Frazier, and Natalie Ebner for their contributions to data collection and/or task design. The authors thank the participating treatment facilities and all study volunteers.

Tables Table 1. Demographic, Affective, and Substance-Related Measures CCs M (SD)

AUD-Only M (SD)

Poly-SU M (SD)

Group Contrasts

41.9 (12.4) 14.8 (1.5)

46.4 (8.8)

37.7 (7.7)

Only > Poly, p<.01

13.2 (1.5)

12.9 (1.6)

AUDs < CC; ps<.01

5.2 (6.0)

15.5 (10.1)

15.0 (9.4)

AUDs > CC; ps<.01

43.6 (7.7)

52.6 (22.7)

56.3 (13.3)

AUDs > CC; ps≤.02

Avg. alcohol (oz./day) c

0.2 (0.3)

17.0 (10.4)

12.5 (8.7)

Only > Poly > CC; ps≤.03

Max. alcohol (single day)

1.8 (1.5)

20.5 (12.2)

20.7 (9.7)

AUDs > CC; ps≤.03

Substance Use Consequences d

0.8 (1.7)

31.1 (10.0)

39.5 (7.4)

Poly > Only > CC; ps<.01

Interpersonal Consequences d

0.2 (0.6)

6.9 (2.7)

8.5 (1.5)

Poly > Only > CC; ps<.01

Alcohol Consequences e

0.8 (1.7)

31.1 (10.0)

32.9 (10.7)

AUDs > CC; ps<.01

Drug Consequences f

-

-

35.4 (13.1)

-

Alc. Problem Onset Age

-

26.5 (10.3)

20.0 (5.1)

Only > Poly, p=.01

Initial Treatment Age

-

42.0 (10.8)

32.6 (7.1)

Only > Poly, p<.01

Days Abstinent

-

44.1 (18.3)

41.7 (13.7)

Only = Poly, p=.62

Measure Age (yrs) Education (yrs) Depressive Symptoms a Anxiety Symptoms b

a

Beck Depression Inventory-II; bSpielberger State Anxiety Index (age-adjusted scores); cAlcohol Quantity/Frequency Index averaged across 6 mo. prior to treatment initiation; dDerived SIP score across substances; eShort Inventory of Problems - Revised; fShort Inventory of Problems - Drug Use

Table 2. Correlation Matrices: Accuracy on Affective Measures & Executive/Visual Composite Scores 2a. All Groups Executive Function Vis-Spatial Function Emotion Judge Acc Attend/Ignore Acc

Executive Function

Vis-Spatial Function

Emotion Judge Acc

.348** .487** .454**

.562** .544**

.822**

Executive Function

Vis-Spatial Function

Emotion Judge Acc

.367 .617¥ .653*

.322** .262**

.811

**All ps<.001 2b. AUD Executive Function Vis-Spatial Function Emotion Judge Acc Attend/Ignore Acc

Differences relative to CCs: ¥p=.061; *p=.022; **ps≤.001

2c. CCs Executive Function Vis-Spatial Function Emotion Judge Acc Attend/Ignore Acc

Executive Function

Vis-Spatial Function

Emotion Judge Acc

.336 .367¥ .270*

.773** .769**

.834

Differences relative to AUD groups: ¥p=.061; *p=.022; **ps≤.001

Supplementary Table S1. Polysubstance Use by Drug Class Among Individuals in The Poly-SU Group

Participant 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Opioids

Psychostimulants + +

Cannabis +

+ + + + + +

+

+

+

+

Benzodiazepines

+ +

+ + + +

+ + +

+ +

+ + +

+

+ +

+ + + +

+ + +

All endorsements include frequencies of at least weekly nonprescription use over the six months prior to treatment entry.

Figures Figure 1. Emotion (1a) & Sex (1b) Judgement Tasks: Exemplar Stimuli

a.

Sad or Angry

Figure 2. Attend/Ignore Task

b.

Female or Male

Figure 3. Emotion Judgement Task Accuracy

Figure 4. Sex Judgement Task Accuracy

**

Figure 5. Affective Attend/Ignore Working Memory Accuracy

100

80

60

Control ALC Only ALC Poly

13

* 12

Digit Symbol Score

Trails-B Completion Time (sec)

Figure 6. Executive Function Scores

11

10

9

8

40

Control ALC Only ALC Poly

7

*

Figure Legend Figure 1. Figure 1A depicts an exemplar trial from the emotion judgement task (EJT), including face stimuli and instructional cue. The depicted trial includes an angry face at the 65% morph level presented during a “Sad or Angry” block. Figure 1B depicts an exemplar trial from the sex judgement task (SJT). The depicted trial includes a face morphed at 65%/35% male/female. Instructional cues were maintained on-screen for all tasks/trials. Right/left position of on-screen cues was kept consistent with right/left response button position. Figure 2. Figure 2 depicts stimuli presentation timing/sequencing in the attend/ignore working memory task. Fixation crosses occurred between each target stimulus (200 ms each) but are not depicted in the figure. An exemplar target stimuli sequence is utilized (sequence of face/scene stimuli was varied across trials). The figure depicts stimuli presentation during a “remember scenes/ignore faces” instructional set in which irrelevant stimuli included surprised female faces and the probe image matched one of the relevant scene targets. Figure 3. Figure 3 depicts EJT accuracy by Group (LS Means ±SE). ** Denotes significant differences (ps ≤ .001) relative to other groups. Figure 4. Figure 4 depicts SJT accuracy by Group (LS Means ±SE). Figure 5. Figure 5 depicts Attend/Ignore Working Memory Task accuracy by Group (LS Means ±SE). * Denotes significant differences (ps≤.050) relative to other groups. Figure 6. Figure 6 depicts performance in executive measures by Group (LS Means ±SE). Figure 6A depicts task completion time in the Trail Making Test-Form B. Figure 6B depicts scaled scores on the Digit Symbol Substitution Task. * Denotes significant differences (ps≤.051) relative to other groups.

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Conflict of Interest All authors declare that they have no conflict of interest.

Highlights: 

Emotion processing abilities were examined among individuals with alcohol use disorders (AUDs).



Polysubstance-using individuals with AUDs evinced disadvantaged performance relative to other groups on a range of cognitive measures.



Among individuals with AUD, those with polysubstance use may be particularly vulnerable to deficits in decoding emotional content.



Data emphasize need to consider polysubstance use in future analyses of alcohol-associated deficits in social cognition.