Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study.

BACKGROUND AND AIMS: The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls. DESIGN: This was a cross-sectional study. SETTING: This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites. PARTICIPANTS: This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls. MEASUREMENTS: This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls. FINDINGS: Alcohol use disorder (ß = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (ß = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models. CONCLUSIONS: People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Consumption of different combinations of human milk oligosaccharides in the first 6 mo of infancy is positively associated with early cognition at 2 y of age in a longitudinal cohort of Latino children.

BACKGROUND: Lactation has been widely associated with optimal neurocognitive development, but the underlying mechanism remains unknown. Human milk oligosaccharides (HMOs) are complex sugars that support brain development, but previous studies examining their associations with cognition have yielded inconsistent findings. OBJECTIVES: This study aimed to provide a broader understanding of how HMOs jointly influence cognition. METHODS: We used data from an ongoing longitudinal cohort of Latino mother-infant dyads. Human milk samples from 1 mo (n = 157) and 6 mo (n = 107) postpartum were assessed for the 19 most abundant HMOs. Cognitive performance was assessed at 2 y using the Bayley Scale of Infant and Toddler Development. A partial least squares model identified HMO combinations predictive of cognitive scores. RESULTS: At 1 mo, the combination of higher concentrations of lacto-N-neotetraose (LNnT), lacto-N-tetraose (LNT), lacto-N-fucopentaose (LNFP)-III, 6'-sialyllactose, and 2'-fucosyllactose (FL) with lower concentrations of sialyllacto-N-tetraose (LST) b, LNFP-II, fucodisialyllacto-N-hexaose, and 3-FL significantly predicted higher cognitive scores (ß: 0.61; 95% confidence interval [CI]: 0.30, 0.92), explaining an additional 8% of the variance over a model with only nuisance covariates (11%). Additional analyses revealed that the combination of higher LNFP-III and lower LSTb alone explained 5% more of the variation in cognitive scores (ß: 0.66; 95% CI: 0.24, 1.09). At 6 mo (n = 107), higher LNnT, LNT, and LNFP-III and lower 3FL and LSTb concentrations explained an extra 6% of the variance in cognitive scores (ß: 0.43; 95% CI: 0.12, 0.75). CONCLUSIONS: This study highlights specific HMO combinations in early life influencing cognitive performance at 2 y.

Maternal Diet Associated with Oligosaccharide Abundances in Human Milk from Latina Mothers.

Growing evidence indicates that human milk oligosaccharides (HMOs) are important bioactive compounds that enhance health and developmental outcomes in breastfed babies. Maternal dietary intake likely contributes to variation in HMO composition, but studies identifying diet-HMO relationships are few and inconsistent. This study aimed to investigate how the maternal intake of macronutrients and micronutrients-specifically proteins, fats, vitamins, and minerals-associated with HMOs at 1 month (n = 210), 6 months (n = 131), and 12 months postpartum (n = 84). Several associations between maternal dietary factors and HMO profiles were identified utilizing partial correlation analysis. For example, maternal free sugar (rho = -0.02, p < 0.01), added sugar (rho = -0.22, p < 0.01), and sugary sweetened beverage (rho = -0.22, p < 0.01) intake were negatively correlated with the most abundant HMO, 2'-fucosyllactose (2'-FL), at 1 month, suggesting that higher sugar consumption was associated with reduced levels of 2'-FL. Further, vitamins D, C, K, and the minerals zinc and potassium were positively correlated with 2'-FL at 1 month (pAll < 0.05). For the longitudinal analysis, a mixed-effects linear regression model revealed significant associations between maternal vitamin intake and HMO profiles over time. For example, for each unit increase in niacin intake, there was a 31.355 nmol/mL increase in 2'-FL concentration (p = 0.03). Overall, the results provide additional evidence supporting a role for maternal nutrition in shaping HMO profiles, which may inform future intervention strategies with the potential of improving infant growth and development through optimal HMO levels in mothers' milk.

Smaller subcortical volume relates to greater weight gain in girls with initially healthy weight.

OBJECTIVE: Among 3614 youth who were 9 to 12 years old and initially did not have overweight or obesity (12% [n = 385] developed overweight or obesity), we examined the natural progression of weight gain and brain structure development during a 2-year period with a high risk for obesity (e.g., pre- and early adolescence) to determine the following: 1) whether variation in maturational trajectories of the brain regions contributes to weight gain; and/or 2) whether weight gain contributes to altered brain development. METHODS: Data were gathered from the Adolescent Brain Cognitive Development (ABCD) Study. Linear mixed-effects regression models controlled for puberty, caregiver education, handedness, and intracranial volume (random effects: magnetic resonance scanner [MRI] scanner and participant). Because pubertal development occurs earlier in girls, analyses were stratified by sex. RESULTS: For girls, but not boys, independent of puberty, greater increases in BMI were driven by smaller volumes over time in the bilateral accumbens, amygdala, hippocampus, and thalamus, right caudate and ventral diencephalon, and left pallidum (all p < 0.05). CONCLUSIONS: The results suggest a potential phenotype for identifying obesity risk because underlying differences among regions involved in food intake were related to greater weight gain in girls, but not in boys. Importantly, 2 years of weight gain may not be sufficient to alter brain development, highlighting early puberty as a critical time to prevent negative neurological outcomes.

Allostatic load, adverse childhood experiences, executive functions, and BMI status in adolescents and young adults.

OBJECTIVES: Chronic stress induces preclinical changes in the metabolic, cardiovascular, and immune systems. This phenomenon, known as allostatic load (AL), can impair executive functions (EF), which may be even more affected in individuals with excess weight due to their characteristic inflammatory state and cardiometabolic changes. Adverse childhood experiences (ACEs) contribute to AL and may influence executive functioning presumably via alterations within the hypothalamic-pituitary axis, including epigenetic modifications. We assess the relationship between AL and EF in youth with and without excess weight, and the effect ACEs on executive functioning. METHODS: One hundred eighty-two adolescents and young adults (85 with normal weight and 97 with overweight/obesity; 10-21 years) were recruited. The estimated AL index included the following: systolic and diastolic blood pressure, glycated hemoglobin, high- and low-density lipoprotein cholesterol, triglycerides, high-sensitivity C-reactive protein, fibrinogen, and cortisol. ACEs were measured using the Juvenile Victimization Questionnaire. The neuropsychological evaluation included the assessment of inhibition, working memory, and cognitive flexibility processes. RESULTS: AL was not significantly associated with executive functioning, and this relationship did not depend on body-weight status. ACEs, available for 57 of 182 participants, were significantly associated with poorer executive functioning. CONCLUSIONS: Our study shows that AL is not associated with executive functioning in adolescents and young adults. Since the current sample was young, we hypothesize that a longer exposure to AL might be required for its negative effects to surface. Nevertheless, exposure to early adversity seems to be associated with poorer executive functioning in youth.

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics.

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Pre-pregnancy maternal obesity and infant neurodevelopmental outcomes in Latino infants.

OBJECTIVE: This study explores the impact of maternal pre-pregnancy BMI on infant neurodevelopment at 24 months in low-income Latino families. It also investigates whether infant diet mediates this relationship. METHODS: Latino mother-infant pairs (n = 163) were enrolled at 1 month post partum and were followed for 2 years, with assessments at 6-month intervals. Maternal pre-pregnancy anthropometrics were self-reported at baseline, and child neurodevelopment was assessed at 24 months using the Bayley Scales of Infant Development. Diet quality of infants was measured using the Healthy Eating Index (HEI)-2015 and HEI-Toddlers-2020 scores at multiple time points. Mediation and regression models that adjust for maternal factors were used to examine the associations. RESULTS: Pre-pregnancy BMI showed significant negative associations with child cognitive scores (ß = -0.1, 95% CI: -0.2 to -0.06, p < 0.001) and language scores (ß = -0.1, 95% CI: -0.2 to -0.03, p = 0.01) at 24 months. Infant HEI-2015 scores at 24 months partly mediated these associations, explaining 23% and 30% of the total effect on cognitive and language subscales, respectively. No specific dietary components in infants mediated the relationship, except for the total HEI-2015 score. CONCLUSIONS: Managing maternal obesity pre-pregnancy is crucial for improving infant neurodevelopmental outcomes, especially in low-income Latino families. Promoting healthy weight and enhancing infant diet quality can enhance neurodevelopment in these populations.

Brain structural covariance network features are robust markers of early heavy alcohol use.

BACKGROUND AND AIMS: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. DESIGN AND SETTING: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years). CASES: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. MEASUREMENTS: Graph theory metrics of segregation and integration were used to summarize SCN. FINDINGS: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. CONCLUSION: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Variation in executive function relates to BMI increases in youth who were initially of a healthy weight in the ABCD Study.

OBJECTIVE: The study aim was to determine whether (A) differences in executive function (EF) and cognition precede weight gain or (B) weight gain causes changes to EF and cognition. METHODS: Data were gathered from the Adolescent Brain Cognitive Development (ABCD) Study (release 4.0; ages 9-12 years old [N = 2794]; 100% had healthy weight at baseline [i.e., 9/10 years old], 12.4% had unhealthy weight by ages 11/12 years). EF and cognition were assessed across several domains (e.g., impulsivity, inhibitory control, processing speed, memory); BMI was calculated from height and weight. Nested random-effects mixed models examined (A) BMI ~ EF × Time (i.e., variation in EF/cognition precedes weight gain) and (B) EF ~ BMI × Time (weight gain causes changes to EF/cognition) and controlled for sex, puberty, and caregiver education; random effects were site and subject. RESULTS: Variation in impulsivity, memory, learning, and processing speed was associated with greater increases in BMI trajectories from 9 to 12 years old. Weight gain was associated with a decrease in inhibitory control, but no other associations were observed. CONCLUSIONS: Underlying variation in EF and cognition may be important for weight gain, but 2 years of weight gain may not be enough to have clinical implications for EF and cognition beyond inhibitory control. These findings suggest that more attention should be paid to the inclusion of EF programs in obesity prevention efforts.

Body mass index, systemic inflammation and cognitive performance in adolescents: A cross-sectional study.

BACKGROUND: Excessive body weight has been related to lower cognitive performance. One of the mechanisms through which excess body weight may affect cognition is inflammation. HYPOTHESIS: Our hypothesis is that both body mass index (BMI) and circulating levels of inflammatory biomarkers will be negatively related to cognitive performance. DESIGN: Cross-sectional study. SETTING: Users of the public health centres of the Consorci Sanitari de Terrassa (Terrassa, Spain) between 2010 and 2017 aged 12-21 years. PARTICIPANTS: One hundred and five adolescents (46 normoweight, 18 overweight, 41 obese). MEASUREMENTS: Levels of high sensitivity C-reactive protein, interleukin 6, tumour necrosis factor α (TNFα) and fibrinogen were determined from blood samples. Cognitive performance was evaluated and six cognitive composites were obtained: working memory, cognitive flexibility, inhibitory control, decision-making, verbal memory, and fine motor speed. A single multivariate general lineal model was used to assess the influence of the four inflammatory biomarkers, as well as participants' BMI, sex, and age on the 6 cognitive indexes. RESULTS: An inverse relationship between BMI and inhibitory control (F = 5.688, p = .019; ß = -0.212, p = .031), verbal memory (F = 5.404, p = .022; ß = -0.255, p = .009) and fine motor speed (F = 9.038, p = .003; ß = -0.319, p = .001) was observed. Levels of TNFα and fibrinogen were inversely related to inhibitory control (F = 5.055, p = .027; ß = -0.226, p = .021) and verbal memory (F = 4.732, p = .032; ß = -0.274, p = .005), respectively. LIMITATIONS: The cross-sectional nature of the study, the use of cognitive tests designed for clinical purposes, and the use of BMI as a proxy for adiposity are limitations of our study that must be taken into account when interpreting results. CONCLUSIONS: Our data indicate that some components of executive functions, together with verbal memory, are sensitive to specific obesity-related inflammatory agents at early ages.

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.

Evaluation of triple negative breast cancer with heterogeneous immune infiltration.

Introduction: Tumor infiltrating lymphocytes (TILs) are known to be a prognostic and predictive biomarker in breast cancer, particularly in triple negative breast cancer (TNBC) patients. International guidelines have been proposed to evaluate them in the clinical setting as a continuous variable, without a clear defined cut-off. However, there are scenarios where the immune infiltration is heterogeneous that some areas of the patient's tumour have high numbers of TILs while other areas completely lack them. This spontaneous presentation of a heterogeneous immune infiltration could be a great opportunity to study why some tumours present TILs at diagnosis but others do not, while eliminating inter patient's differences. Methods: In this study, we have identified five TNBC patients that showed great TIL heterogeneity, with areas of low (≤5%) and high (≥50%) numbers of TILs in their surgical specimens. To evaluate immune infiltration heterogeneity, we performed and analyzed bulk RNA-sequencing in three independent triplicates from the high and low TIL areas of each patient. Results: Gene expression was homogeneous within the triplicates in each area but was remarkable different between TILs regions. These differences were not only due to the presence of TILs as there were other non-inflammatory genes and pathways differentially expressed between the two areas. Discussion: This highlights the importance of intratumour heterogeneity driving the immune infiltration, and not patient's characteristics like the HLA phenotype, germline DNA or immune repertoire.

Beyond BMI: cardiometabolic measures as predictors of impulsivity and white matter changes in adolescents.

Obesity is characterized by cardiometabolic and neurocognitive changes. However, how these two factors relate to each other in this population is unknown. We tested the association that cardiometabolic measures may have with impulse behaviors and white matter microstructure in adolescents with and without an excess weight. One hundred and eight adolescents (43 normal-weight and 65 overweight/obesity; 11-19 years old) were medically and psychologically (Temperament Character Inventory Revised, Three-Factor Eating Questionnaire-R18, Conners' Continuous Performance Test-II, Stroop Color and Word Test, Wisconsin Card Sorting Test, Kirby Delay Discounting Task) evaluated. A subsample of participants (n = 56) underwent a brain magnetic resonance imaging acquisition. In adolescents, higher triglycerides and having a body mass index indicative of overweight/obesity predicted a more impulsive performance in Conners' Continuous Performance Test-II (higher commission errors). In addition, higher glucose and diastolic blood pressure values predicted increments in the Three-Factor Eating Questionnaire-R18 emotional eating scale. Neuroanatomically, cingulum fractional anisotropy showed a negative relationship with glycated hemoglobin. The evaluation of the neurocognitive differences associated with obesity, usually based on body mass index, should be complemented with cardiometabolic measures.

Spatio-temporal modeling of infectious diseases by integrating compartment and point process models.

Infectious disease modeling plays an important role in understanding disease spreading dynamics and can be used for prevention and control. The well-known SIR (Susceptible, Infected, and Recovered) compartment model and spatial and spatio-temporal statistical models are common choices for studying problems of this kind. This paper proposes a spatio-temporal modeling framework to characterize infectious disease dynamics by integrating the SIR compartment and log-Gaussian Cox process (LGCP) models. The method's performance is assessed via simulation using a combination of real and synthetic data for a region in São Paulo, Brazil. We also apply our modeling approach to analyze COVID-19 dynamics in Cali, Colombia. The results show that our modified LGCP model, which takes advantage of information obtained from the previous SIR modeling step, leads to a better forecasting performance than equivalent models that do not do that. Finally, the proposed method also allows the incorporation of age-stratified contact information, which provides valuable decision-making insights. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02354-4.

Cortical profiles of numerous psychiatric disorders and normal development share a common pattern.

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Recalibrating single-study effect sizes using hierarchical Bayesian models.

Introduction: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. Methods: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. Results: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. Discussion: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents.

BACKGROUND AND AIMS: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. DESIGN: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. SETTING AND PARTICIPANTS: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. MEASUREMENTS: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). FINDINGS: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = -0.0142, 95% confidence interval (CI) = -0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = -0.0164, 95% CI = -0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = -0.0141, 95% CI = -0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405, 95% CI = -0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = -0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131, 95% CI = -0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = -0.0362, 95% CI = -0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = -0.0011, 0.0038; P-value = 0.048). CONCLUSIONS: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group.

BACKGROUND: Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. METHODS: Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. RESULTS: The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). CONCLUSIONS: Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.

Characterizing reward system neural trajectories from adolescence to young adulthood.

Mixed findings exist in studies comparing brain responses to reward in adolescents and adults. Here we examined the trajectories of brain response, functional connectivity and task-modulated network properties during reward processing with a large-sample longitudinal design. Participants from the IMAGEN study performed a Monetary Incentive Delay task during fMRI at timepoint 1 (T1; n = 1304, mean age=14.44 years old) and timepoint 2 (T2; n = 1241, mean age=19.09 years). The Alcohol Use Disorders Identification Test (AUDIT) was administrated at both T1 and T2 to assess a participant's alcohol use during the past year. Voxel-wise linear mixed effect models were used to compare whole brain response as well as functional connectivity of the ventral striatum (VS) during reward anticipation (large reward vs no-reward cue) between T1 and T2. In addition, task-modulated networks were constructed using generalized psychophysiological interaction analysis and summarized with graph theory metrics. To explore alcohol use in relation to development, participants with no/low alcohol use at T1 but increased alcohol use to hazardous use level at T2 (i.e., participants with AUDIT≤2 at T1 and ≥8 at T2) were compared against those with consistently low scores (i.e., participants with AUDIT≤2 at T1 and ≤7 at T2). Across the whole sample, lower brain response during reward anticipation was observed at T2 compared with T1 in bilateral caudate nucleus, VS, thalamus, midbrain, dorsal anterior cingulate as well as left precentral and postcentral gyrus. Conversely, greater response was observed bilaterally in the inferior and middle frontal gyrus and right precentral and postcentral gyrus at T2 (vs. T1). Increased functional connectivity with VS was found in frontal, temporal, parietal and occipital regions at T2. Graph theory metrics of the task-modulated network showed higher inter-regional connectivity and topological efficiency at T2. Interactive effects between time (T1 vs. T2) and alcohol use group (low vs. high) on the functional connectivity were observed between left middle temporal gyrus and right VS and the characteristic shortest path length of the task-modulated networks. Collectively, these results demonstrate the utility of the MID task as a probe of typical brain response and network properties during development and of differences in these features related to adolescent drinking, a reward-related behaviour associated with heightened risk for future negative health outcomes.

Restrained Eating Is Associated with Lower Cortical Thickness in the Inferior Frontal Gyrus in Adolescents.

Some eating patterns, such as restrained eating and uncontrolled eating, are risk factors for eating disorders. However, it is not yet clear whether they are associated with neurocognitive differences. In the current study, we analyzed whether eating patterns can be used to classify participants into meaningful clusters, and we examined whether there are neurocognitive differences between the clusters. Adolescents (n = 108; 12 to 17 years old) and adults (n = 175, 18 to 40 years old) completed the Three Factor Eating Questionnaire, which was used to classify participants according to their eating profile using k means clustering. Participants also completed personality questionnaires and a neuropsychological examination. A subsample of participants underwent a brain MRI acquisition. In both samples, we obtained a cluster characterized by high uncontrolled eating patterns, a cluster with high scores in restrictive eating, and a cluster with low scores in problematic eating behaviors. The clusters were equivalent with regards to personality and performance in executive functions. In adolescents, the cluster with high restrictive eating showed lower cortical thickness in the inferior frontal gyrus compared to the other two clusters. We hypothesize that this difference in cortical thickness represents an adaptive neural mechanism that facilitates inhibition processes.