Differential associations of adolescent versus young adult cannabis initiation with longitudinal brain change and behavior.

Leveraging ~10 years of prospective longitudinal data on 704 participants, we examined the effects of adolescent versus young adult cannabis initiation on MRI-assessed cortical thickness development and behavior. Data were obtained from the IMAGEN study conducted across eight European sites. We identified IMAGEN participants who reported being cannabis-naïve at baseline and had data available at baseline, 5-year, and 9-year follow-up visits. Cannabis use was assessed with the European School Survey Project on Alcohol and Drugs. T1-weighted MR images were processed through the CIVET pipeline. Cannabis initiation occurring during adolescence (14-19 years) and young adulthood (19-22 years) was associated with differing patterns of longitudinal cortical thickness change. Associations between adolescent cannabis initiation and cortical thickness change were observed primarily in dorso- and ventrolateral portions of the prefrontal cortex. In contrast, cannabis initiation occurring between 19 and 22 years of age was associated with thickness change in temporal and cortical midline areas. Follow-up analysis revealed that longitudinal brain change related to adolescent initiation persisted into young adulthood and partially mediated the association between adolescent cannabis use and past-month cocaine, ecstasy, and cannabis use at age 22. Extent of cannabis initiation during young adulthood (from 19 to 22 years) had an indirect effect on psychotic symptoms at age 22 through thickness change in temporal areas. Results suggest that developmental timing of cannabis exposure may have a marked effect on neuroanatomical correlates of cannabis use as well as associated behavioral sequelae. Critically, this work provides a foundation for neurodevelopmentally informed models of cannabis exposure in humans.

Machine learning approaches linking brain function to behavior in the ABCD STOP task.

The stop-signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop-signal reaction-time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibition remain unclear, consistent with the general pattern of quite modest brain-behavior associations that have been recently reported in well-powered large-sample studies. Here, we investigated the potential of multivariate, machine learning (ML) methods to improve the estimation of individual differences in SSRT with multimodal structural and functional region of interest-level neuroimaging data from 9- to 11-year-olds children in the ABCD Study. Six ML algorithms were assessed across modalities and fMRI tasks. We verified that SST activation performed best in predicting SSRT among multiple modalities including morphological MRI (cortical surface area/thickness), diffusion tensor imaging, and fMRI task activations, and then showed that SST activation explained 12% of the variance in SSRT using cross-validation and out-of-sample lockbox data sets (n = 7298). Brain regions that were more active during the task and that showed more interindividual variation in activation were better at capturing individual differences in performance on the task, but this was only true for activations when successfully inhibiting. Cortical regions outperformed subcortical areas in explaining individual differences but the two hemispheres performed equally well. These results demonstrate that the detection of reproducible links between brain function and performance can be improved with multivariate approaches and give insight into a number of brain systems contributing to individual differences in this fundamental cognitive control process.

A machine learning-derived neuroanatomical pattern predicts delayed reward discounting in the Human Connectome Project Young Adult sample.

Delayed reward discounting (DRD) is defined as the extent to which person favors smaller rewards that are immediately available over larger rewards available in the future. Higher levels of DRD have been identified in individuals with a wide range of clinical disorders. Although there have been studies adopting larger samples and using only gray matter volume to characterize the neuroanatomical correlates of DRD, it is still unclear whether previously identified relationships are generalizable (out-of-sample) and how cortical thickness and cortical surface area contribute to DRD. In this study, using the Human Connectome Project Young Adult dataset (N = 1038), a machine learning cross-validated elastic net regression approach was used to characterize the neuroanatomical pattern of structural magnetic resonance imaging variables associated with DRD. The results revealed a multi-region neuroanatomical pattern predicted DRD and this was robust in a held-out test set (morphometry-only R2 = 3.34%, morphometry + demographics R2  = 6.96%). The neuroanatomical pattern included regions implicated in the default mode network, executive control network, and salience network. The relationship of these regions with DRD was further supported by univariate linear mixed effects modeling results, in which many of the regions identified as part of this pattern showed significant univariate associations with DRD. Taken together, these findings provide evidence that a machine learning-derived neuroanatomical pattern encompassing various theoretically relevant brain networks produces robustly predicts DRD in a large sample of healthy young adults.

Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study.

The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.

Structural brain differences do not mediate the relations between sex and personality or psychopathology.

INTRODUCTION: Males and females tend to exhibit small but reliable differences in personality traits and indices of psychopathology that are relatively stable over time and across cultures. Previous work suggests that sex differences in brain structure account for differences in domains of cognition. METHODS: We used data from the Human Connectome Project (N = 1098) to test whether sex differences in brain morphometry account for observed differences in the personality traits neuroticism and agreeableness, as well as symptoms of internalizing and externalizing psychopathology. We operationalized brain morphometry in three ways: omnibus measures (e.g., total gray matter volume), Glasser regions defined through a multi-modal parcellation approach, and Desikan regions defined by structural features of the brain. RESULTS: Most expected sex differences in personality, psychopathology, and brain morphometry were observed, but the statistical mediation analyses were null: sex differences in brain morphometry did not account for sex differences in personality or psychopathology. CONCLUSIONS: Men and women tend to exhibit meaningful differences in personality and psychopathology, as well as in omnibus morphometry and regional morphometric differences as defined by the Glasser and Desikan atlases, but these morphometric differences appear unrelated to the psychological differences.

Investigation of Psychiatric and Neuropsychological Correlates of Default Mode Network and Dorsal Attention Network Anticorrelation in Children.

The default mode network (DMN) and dorsal attention network (DAN) demonstrate an intrinsic "anticorrelation" in healthy adults, which is thought to represent the functional segregation between internally and externally directed thought. Reduced segregation of these networks has been proposed as a mechanism for cognitive deficits that occurs in many psychiatric disorders, but this association has rarely been tested in pre-adolescent children. The current analysis used data from the Adolescent Brain Cognitive Development study to examine the relationship between the strength of DMN/DAN anticorrelation and psychiatric symptoms in the largest sample to date of 9- to 10-year-old children (N = 6543). The relationship of DMN/DAN anticorrelation to a battery of neuropsychological tests was also assessed. DMN/DAN anticorrelation was robustly linked to attention problems, as well as age, sex, and socioeconomic factors. Other psychiatric correlates identified in prior reports were not robustly linked to DMN/DAN anticorrelation after controlling for demographic covariates. Among neuropsychological measures, the clearest correlates of DMN/DAN anticorrelation were the Card Sort task of executive function and cognitive flexibility and the NIH Toolbox Total Cognitive Score, although these did not survive correction for socioeconomic factors. These findings indicate a complicated relationship between DMN/DAN anticorrelation and demographics, neuropsychological function, and psychiatric problems.

Recent cannabis use is associated with smaller hippocampus volume: High-resolution segmentation of structural subfields in a large non-clinical sample.

There is mixed evidence that individuals who use cannabis have reduced hippocampal and amygdalar gray matter volume, potentially because of small sample sizes and imprecise morphological characterization. New automated segmentation procedures have improved the measurement of these structures and allow better examination of their subfields, which have been linked to distinct aspects of memory and emotion. The current study applies this new segmentation procedure to the Human Connectome Project Young Adult dataset (N = 1080) to investigate associations of cannabis use with gray matter volume in the hippocampus and amygdala. Results revealed significant bilateral inverse associations of hippocampal volume with recent cannabis use (THC+ urine drug screen; P < .005). Hippocampal subfield analyses indicated these associations were primarily driven by the head of the hippocampus, the first section of the cornu amonis (CA1), the subicular complex, and the molecular layer of the hippocampus. No associations were detected for age of cannabis initiation, the frequency of cannabis use across the lifespan, or the lifetime presence of cannabis use disorder. In one of the largest studies to date, these results support the hypothesis that recent cannabis use is linked to reduced hippocampal volume, but that this effect may dissipate following prolonged abstinence. Furthermore, these results clarify the specific subfields which may be most associated with recent cannabis use.

The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables.

Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure. In Part 1 of the current study, we conducted a descriptive review of relevant articles from the past two years of NeuroImage in order to identify the most commonly used covariates in work of this nature. Age, sex, and intracranial volume were found to be the most commonly used covariates, although the number of covariates used ranged from 0 to 14, with 37 different covariate sets across the 68 models tested. In Part 2, we used data from the Human Connectome Project to investigate the degree to which the addition of common covariates altered the relations between individual difference variables (i.e., personality traits, psychopathology, cognitive tasks) and regional gray matter volume (GMV), as well as the statistical significance of values associated with these effect sizes. Using traditional and random sampling approaches, our results varied widely, such that some covariate sets influenced the relations between the individual difference variables and GMV very little, while the addition of other covariate sets resulted in a substantially different pattern of results compared to models with no covariates. In sum, these results suggest that the use of covariates should be critically examined and discussed as part of the conversation on replicability in structural neuroimaging. We conclude by recommending that researchers pre-register their analytic strategy and present information on how relations differ based on the inclusion of covariates.

Cannabis involvement and neuropsychological performance: findings from the Human Connectome Project

Background: There is evidence that heavy cannabis use is associated with decrements in cognitive performance, but findings are mixed and studies are often limited by small sample sizes and narrow adjustment for potential confounding variables. In a comparatively large sample, the current study examined associations between multiple indicators of cannabis use in relation to performance on a variety of neuropsychological tasks. Methods: Participants were 1121 adults (54% female) enrolled in the Human Connectome Project. Cannabis involvement comprised recent cannabis use (positive tetrahydrocannabinol screen), total number of lifetime uses, cannabis use disorder and age at first use. The neuropsychological battery comprised performance in episodic memory, fluid intelligence, attention, working memory, executive function, impulsive decision-making, processing speed and psychomotor dexterity. Covariates were age, sex, income, family structure and alcohol and tobacco use. Results: Positive urinary tetrahydrocannabinol status was associated with worse performance in episodic memory and processing speed, and positive cannabis use disorder status was associated with lower fluid intelligence (all p < 0.005). No other significant associations were present. Limitations: The sample was limited to young adults aged 22­36 years. The measures of cannabis involvement were relatively coarse. Conclusion: Beyond an array of potential confounders, recent cannabis use was associated with deficits in memory and psychomotor performance, and cannabis use disorder was associated with lower overall cognitive functioning in a large normative sample of adults. The findings pertaining to recent use have particular relevance for occupational settings.

Associations Between Drinking and Cortical Thickness in Younger Adult Drinkers: Findings From the Human Connectome Project.

BACKGROUND: Previous neuroimaging studies examining relations between alcohol misuse and cortical thickness have revealed that increased drinking quantity and alcohol-related problems are associated with thinner cortex. Although conflicting regional effects are often observed, associations are generally localized to frontal regions (e.g., dorsolateral prefrontal cortex [DLPFC], inferior frontal gyrus [IFG], and anterior cingulate cortex). Inconsistent findings may be attributed to methodological differences, modest sample sizes, and limited consideration of sex differences. METHODS: This study examined neuroanatomical correlates of drinking quantity and heavy episodic drinking in a large sample of younger adults (N = 706; Mage  = 28.8; 51% female) using magnetic resonance imaging data from the Human Connectome Project. Exploratory analyses examined neuroanatomical correlates of executive function (flanker task) and working memory (list sorting). RESULTS: Hierarchical linear regression models (controlling for age, sex, education, income, smoking, drug use, twin status, and intracranial volume) revealed significant inverse associations between drinks in past week and frequency of heavy drinking and cortical thickness in a majority of regions examined. The largest effect sizes were found for frontal regions (DLPFC, IFG, and the precentral gyrus). Follow-up regression models revealed that the left DLPFC was uniquely associated with both drinking variables. Sex differences were also observed, with significant effects largely specific to men. CONCLUSIONS: This study adds to the understanding of brain correlates of alcohol use in a large, gender-balanced sample of younger adults. Although the cross-sectional methodology precludes causal inferences, these findings provide a foundation for rigorous hypothesis testing in future longitudinal investigations.

Distinct functional and structural neural underpinnings of working memory.

Working memory (WM), the short-term abstraction and manipulation of information, is an essential neurocognitive process in daily functioning. Few studies have concurrently examined the functional and structural neural correlates of WM and the current study did so to characterize both overlapping and unique associations. Participants were a large sample of adults from the Human Connectome Project (N = 1064; 54% female) who completed an in-scanner visual N-back WM task. The results indicate a clear dissociation between BOLD activation during the WM task and brain structure in relation to performance. In particular, while activation in the middle frontal gyrus was positively associated with WM performance, cortical thickness in this region was inversely associated with performance. Additional unique associations with WM were BOLD activation in superior parietal lobule, cingulate, and fusiform gyrus and gray matter volume in the orbitofrontal cortex and cuneus. Across findings, substantially larger effects were observed for functional associations relative to structural associations. These results provide further evidence implicating frontoparietal subunits of the brain in WM. Moreover, these findings reveal the distinct, and in some cases opposing, roles of brain structure and neural activation in WM, highlighting the lack of homology between structure and function in relation to cognition.

Neural correlates of tobacco cue reactivity predict duration to lapse and continuous abstinence in smoking cessation treatment.

It has been hypothesized that neural reactivity to drug cues in certain limbic/paralimbic regions of the brain is an indicator of addiction severity and a marker for likelihood of success in treatment. To address this question, in the current study, 32 participants (44 percent female) completed a functional magnetic resonance imaging cigarette cue exposure paradigm 2 hours after smoking, and then enrolled in a 9-week smoking cessation treatment program. Neural activation to smoking cues was measured in five a priori defined limbic/paralimbic regions previously implicated with cue reactivity across substances. These included regions of the ventral striatum, anterior cingulate cortex and amygdala. Cox proportional hazard modeling was conducted to predict the number of days to first smoking lapse by using neural activation in these regions. Greater neural activation during pre-treatment exposure to smoking cues in the right ventral striatum, the left amygdala, and the anterior cingulate was associated with longer periods of abstinence following cessation. A similar pattern was present for continuous abstinence for the full duration of treatment. While baseline levels of nicotine dependence were strongly associated with treatment outcome, activation in the right ventral striatum predicted duration of abstinence beyond level of nicotine dependence. These results suggest that pre-treatment reactivity to smoking cues in areas associated with cue reactivity may be associated with successfully maintaining abstinence during treatment. This is consistent with models that propose that as addiction becomes more severe, motivational processing shifts from regions that subserve reward salience and learning to regions responsible motor behavior and habit learning.

Neuroanatomical foundations of delayed reward discounting decision making.

Resolving tradeoffs between smaller immediate rewards and larger delayed rewards is ubiquitous in daily life and steep discounting of future rewards is associated with several psychiatric conditions. This form of decision-making is referred to as delayed reward discounting (DRD) and the features of brain structure associated with DRD are not well understood. The current study characterized the relationship between gray matter volume (GMV) and DRD in a sample of 1038 healthy adults (54.7% female) using cortical parcellation, subcortical segmentation, and voxelwise cortical surface-based group analyses. The results indicate that steeper DRD was significantly associated with lower total cortical GMV, but not subcortical GMV. In parcellation analyses, less GMV in 20 discrete cortical regions was associated with steeper DRD. Of these regions, only GMV in the middle temporal gyrus (MTG) and entorhinal cortex (EC) were uniquely associated with DRD. Voxelwise surface-based analyses corroborated these findings, again revealing significant associations between steeper DRD and less GMV in the MTG and EC. To inform the roles of MTG and EC in DRD, connectivity analysis of resting state data (N = 1003) using seed regions from the structural findings was conducted. This revealed that spontaneous activity in the MTG and EC was correlated with activation in the ventromedial prefrontal cortex, posterior cingulate cortex, and inferior parietal lobule, regions associated with the default mode network, which involves prospection, self-reflective thinking and mental simulation. Furthermore, meta-analytic co-activation analysis using Neurosynth revealed a similar pattern across 11,406 task-fMRI studies. Collectively, these findings provide robust evidence that morphometric characteristics of the temporal lobe are associated with DRD preferences and suggest it may be because of their role in mental activities in common with default mode activity.

Lower subcortical gray matter volume in both younger smokers and established smokers relative to non-smokers.

Although established adult smokers with long histories of nicotine dependence have lower neural tissue volume than non-smokers, it is not clear if lower regional brain volume is also observed in younger, less established smokers. The primary goal of this study was to investigate neural tissue volume in a large group of smokers and non-smokers, with a secondary goal of measuring the impact of age on these effects. We used voxel-based morphometry to compare regional gray matter volume in 118 individuals (59 smokers, 59 age- and gender-matched non-smokers). Younger smokers had significantly lower gray matter volume in the left thalamus and the left amygdala than their non-smoking peers (family-wise error-corrected clusters, P < 0.05). There was no correlation between smoking use variables and tissue volume among younger smokers. Established smokers had significantly lower gray matter volume than age-matched non-smokers in the insula, parahippocampal gyrus and pallidum. Medial prefrontal cortex gray matter volume was negatively correlated with pack-years of smoking among the established smokers, but not the younger smokers. These data reveal that regional tissue volume differences are not limited exclusively to established smokers. Deficits in young adults indicate that cigarette smoking may either be deleterious to the thalamus and amygdala at an earlier age than previously reported, or that pre-existing differences in these areas may predispose individuals to the development of nicotine dependence.

Testing whether the relations between sex and psychopathology are accounted for by structural morphometry in ABCD.

Sex differences in psychopathology are well-established, with females demonstrating higher rates of internalizing (INT) psychopathology and males demonstrating higher rates of externalizing (EXT) psychopathology. Using two waves of data from the Adolescent Brain Cognitive Development Study (N = 6,778 at each wave), the current study tested whether the relations between sex and psychopathology might be accounted for by structural brain differences. In general, we found robust, relatively consistent relations between sex and structural morphometry across waves. Relatively few morphometric brain variables were significantly related to INT or EXT across waves, however, with very small effect sizes when present. Next, we tested the extent to which each morphometric brain variable could account for the associations of sex with INT and EXT psychopathology. We found a total of 26 brain regions that accounted for significant portions of the associations between sex and psychopathology across both waves (almost all related to EXT), although the effects present were very small. The current evidence suggests that in children aged 9-12, multiple whole-brain and regional brain variables appear to statistically account for small portions of the sex-psychopathology links, especially for externalizing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neuroanatomical profile of BMI implicates impulsive delay discounting and general cognitive ability.

OBJECTIVE: Obesity is a disorder of excessive adiposity, typically assessed via the anthropometric density measure of BMI. Numerous studies have implicated BMI with differences in brain structure, but with highly inconsistent findings. METHODS: Machine learning elastic net regression models with cross-validation were conducted to characterize a neuroanatomical morphometry profile associated with BMI in 1100 participants (22% BMI > 30, n = 242) from the Human Connectome Project Young Adult project. RESULTS: Using five-fold cross-validation, the multiregion neuroanatomical profile substantively predicted BMI (R2 = 10.05%), and this was robust in a held-out test set (R2 = 8.87%). In terms of specific regions, the neuroanatomical profile was enriched for nodes in the default mode, executive control, and salience networks. The relationship between the morphometry profile and BMI itself was partially mediated by impulsive delay discounting and general cognitive ability. CONCLUSIONS: Taken together, these findings reveal a robust machine learning-derived neuroanatomical profile of BMI, one that comprises nodes in motivational brain networks and suggests the functional links to obesity are via self-regulatory capacity and cognitive function.

Molecular brain differences and cannabis involvement: A systematic review of positron emission tomography studies.

BACKGROUND: An increasing number of studies have used positron emission tomography (PET) to investigate molecular neurobiological differences in individuals who use cannabis. This study aimed to systematically review PET imaging research in individuals who use cannabis or have cannabis use disorder (CUD). METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria, a comprehensive systematic review was undertaken using the PubMed, Scopus, PsycINFO and Web of Science databases. RESULTS: In total, 20 studies were identified and grouped into three themes: (1) studies of the dopamine system primarily found that cannabis use was associated with abnormal striatal dopamine synthesis capacity, which was in turn correlated with clinical symptoms; (2) studies of the endocannabinoid system found that cannabis use and CUD are associated with lower cannabinoid receptor type 1 availability and global reductions in fatty acid amide hydrolase binding; (3) studies of brain metabolism found that individuals who use cannabis exhibit lower normalized glucose metabolism in both cortical and subcortical brain regions, and reduced cerebral blood flow in the lateral prefrontal cortex during experimental tasks. Heterogeneity across studies prevented meta-analysis. CONCLUSION: Existing PET imaging research reveals substantive molecular differences in cannabis users in the dopamine and endocannabinoid systems, and in global brain metabolism, although the heterogeneity of designs and approaches is very high, and whether these differences are causal versus consequential is largely unclear.

Test-retest reliability of the neuroanatomical correlates of impulsive personality traits in the adolescent brain cognitive development study.

While the neuroanatomical correlates of impulsivity in youths have been examined, there is little research on whether those correlates are consistent across childhood/adolescence. The current study uses data from the age 11/12 (N = 7,083) visit of the Adolescent Brain Cognitive Development Study to investigate the replicability of previous work (Owens et al., 2020) the neuroanatomical correlates of impulsive personality traits identified at age 9/10. Neuroanatomy was measured using structural and diffusion magnetic resonance imaging, and impulsive personality was measured using the UPPS-P Impulsive Behavior Scale. Replicability was quantified using three Open Science Collaboration replication criteria, intraclass correlations, and elastic net regression modeling to make predictions across timepoints. Replicability was highly variable among traits: The neuroanatomical correlates of positive urgency showed substantial similarity between ages 9/10 and 11/12, negative urgency and sensation seeking showed moderate similarity across ages, and (lack of) premeditation and perseverance showed substantial dissimilarity across ages. In all cases, effect sizes between impulsive traits and brain variables were small. These findings suggest that, even for studies with large sample sizes and the same participant pool, the replicability of brain-behavior correlations across a 2-year period cannot be assumed. This may be due to developmental changes across the two timepoints or false-positive/false-negative results at one or both timepoints. These results also highlight an array of neuroanatomical structures that may be important to impulsive personality traits across development from childhood into adolescence. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Low-to-moderate level of perceived stress strengthens working memory: Testing the hormesis hypothesis through neural activation.

The negative impact of stress on neurocognitive functioning is extensively documented by empirical research. However, emerging reports suggest that stress may also confer positive neurocognitive effects. This hypothesis has been advanced by the hormesis model of psychosocial stress, in which low-moderate levels of stress are expected to result in neurocognitive benefits, such as improved working memory (WM), a central executive function. We tested the hormesis hypothesis, purporting an inverted U-shaped relation between stress and neurocognitive performance, in a large sample of young adults from the Human Connectome Project (n = 1000, Mage = 28.74, SD = 3.67, 54.3% female). In particular, we investigated whether neural response during a WM challenge is a potential intermediary through which low-moderate levels of stress confer beneficial effects on WM performance. Further, we tested whether the association between low-moderate prolonged stress and WM-related neural function was stronger in contexts with more psychosocial resources. Findings showed that low-moderate levels of perceived stress were associated with elevated WM-related neural activation, resulting in more optimal WM behavioral performance (α *ß = -0.02, p = .046). The strength of this association tapered off at high-stress levels. Finally, we found that the benefit of low-moderate stress was stronger among individuals with access to higher levels of psychosocial resources (ß = -0.06, p = .021). By drawing attention to the dose-dependent, nonlinear relation between stress and WM, this study highlights emerging evidence of a process by which mild stress induces neurocognitive benefits, and the psychosocial context under which benefits are most likely to manifest.

Neuroeconomic predictors of smoking cessation outcomes: A preliminary study of delay discounting in treatment-seeking adult smokers.

Large proportions of smokers are unsuccessful in evidence-based smoking cessation treatment and identifying prognostic predictors may inform improvements in treatment. Steep discounting of delayed rewards (delay discounting) is a robust predictor of poor smoking cessation outcome, but the underlying neural predictors have not been investigated. Forty-one treatment-seeking adult smokers completed a functional magnetic resonance imaging (fMRI) delay discounting paradigm prior to initiating a 9-week smoking cessation treatment protocol. Behavioral performance significantly predicted treatment outcomes (verified 7-day abstinence, n = 18; relapse, n = 23). Participants in the relapse group exhibited smaller area under the curve (d = 1.10) and smaller AUC was correlated with fewer days to smoking relapse (r = 0.56, p < 0.001) Neural correlates of discounting included medial and dorsolateral prefrontal cortex, posterior cingulate, precuneus and anterior insula, and interactions between choice type and relapse status were present for the dorsolateral prefrontal cortex, precuneus and the striatum. This initial investigation implicates differential neural activity in regions associated with frontal executive and default mode activity, as well as motivational circuits. Larger samples are needed to improve the resolution in identifying the neural underpinnings linking steep delay discounting to smoking cessation.