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.

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.

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; β = -0.212, p = .031), verbal memory (F = 5.404, p = .022; β = -0.255, p = .009) and fine motor speed (F = 9.038, p = .003; β = -0.319, p = .001) was observed. Levels of TNFα and fibrinogen were inversely related to inhibitory control (F = 5.055, p = .027; β = -0.226, p = .021) and verbal memory (F = 4.732, p = .032; β = -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.

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.

Alterations in Brain Network Organization in Adults With Obesity as Compared With Healthy-Weight Individuals and Seniors

OBJECTIVE

Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting medical disorders that might compromise the normal course of aging. The aim of the current study of brain connectivity patterns was to examine whether adults with obesity would show signs of premature aging, such as lower segregation, in large-scale networks.

METHODS

Participants with obesity (n = 30, mean age = 32.8 ± 5.68 years) were compared with healthy-weight controls (n = 33, mean age = 30.9 ± 6.24 years) and senior participants who were stroke-free and without dementia (n = 30, mean age = 67.1 ± 6.65 years) using resting-state magnetic resonance imaging and graph theory metrics (i.e., small-world index, clustering coefficient, characteristic path length, and degree).

RESULTS

Contrary to our hypothesis, participants with obesity exhibited a higher clustering coefficient compared with senior participants (t = 5.06, p < .001, d = 1.23, 95% CIbca = 0.64 to 1.88). Participants with obesity also showed lower global degree relative to seniors (t = -2.98, p = .014, d = -0.77, 95% CIbca = -1.26 to -0.26) and healthy-weight controls (t = -2.92, p = .019, d = -0.72, 95% CIbca = -1.19 to -0.25). Regional degree alterations in this group were present in several functional networks.

CONCLUSIONS

Participants with obesity displayed greater network clustering than did seniors and also had lower degree compared with seniors and individuals with normal weight, which is not consistent with the notion that obesity is associated with premature aging of the brain. Although the cross-sectional nature of the study precludes causal inference, the overly clustered network patterns in obese participants could be relevant to age-related changes in brain function because regular networks might be less resilient and metabolically inefficient.

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.

Allostatic load and executive functions in overweight adults

BACKGROUND/OBJECTIVE

Overweight is linked to inflammatory and neuroendocrine responses potentially prompting deregulations in biological systems harmful to the brain, particularly to the prefrontal cortex. This structure is crucial for executive performance, ultimately supervising behaviour. Thus, in the present work, we aimed to test the relationship between allostatic load increase, a surrogate of chronic physiological stress, and core executive functions, such as cognitive flexibility, inhibitory control, and working memory.

METHOD

Forty-seven healthy-weight and 56 overweight volunteers aged from 21 to 40 underwent medical and neuropsychological examination.

RESULTS

Overweight subjects exhibited a greater allostatic load index than healthy-weight individuals. Moreover, the allostatic load index was negatively related to inhibitory control. When separated, the link between allostatic load index and cognitive flexibility was more marked in the overweight group.

CONCLUSIONS

An overweight status was linked to chronic physiological stress. The inverse relationship between the allostatic load index and cognitive flexibility proved stronger in this group. Set-shifting alterations could sustain rigid-like behaviours and attitudes towards food.

Effect of the catechol-O-methyltransferase Val 158 Met polymorphism on theory of mind in obesity

Obesity is often accompanied with psychosocial adjustment problems, such as difficulties in social interactions and social withdrawal. A key aspect of social cognition is theory of mind, which allows inferring mental states, feelings, motivations, and beliefs of others and to use this information to predict their future behaviour. Theory of mind is highly dependent on prefrontal dopaminergic neurotransmission, which is regulated by catechol-O-methyltransferase (COMT) activity. We aimed at determining whether theory of mind is altered in obesity and if this ability is modulated by COMT. Fifty patients with obesity and 47 normal-weight individuals underwent the Reading the Mind in the Eyes Test, the Wisconsin Card Sorting Test, and the Vocabulary subscale of the Wechsler Adult Intelligence Scale. The genotype for the COMT Val ¹⁵⁸ Met functional polymorphism was determined for all subjects. Patients with obesity obtained significantly lower scores in the negative items of the Reading the Mind in the Eyes Test than normal-weight subjects. Further, an interaction effect was observed between group and COMT genotype. Specifically, the presence of the Met allele was associated to a better identification of negative mental states only in patients with obesity. Our results indicate that obesity is accompanied with difficulties in theory of mind and that this ability is influenced by the COMT genotype.

Allostatic load and disordered white matter microstructure in overweight adults

Overweight and stress are both related to brain structural abnormalities. The allostatic load model states that frequent disruption of homeostasis is inherently linked to oxidative stress and inflammatory responses that in turn can damage the brain. However, the effects of the allostatic load on the central nervous system remain largely unknown. The current study aimed to assess the relationship between the allostatic load and the composition of whole-brain white matter tracts in overweight subjects. Additionally, we have also tested for grey matter changes regarding allostatic load increase. Thirty-one overweight-to-obese adults and 21 lean controls participated in the study. Our results showed that overweight participants presented higher allostatic load indexes. Such increases correlated with lower fractional anisotropy in the inferior fronto-occipital fasciculi and the right anterior corona radiata, as well as with grey matter reductions in the left precentral gyrus, the left lateral occipital gyrus, and the right pars opercularis. These results suggest that an otherwise healthy overweight status is linked to long-term biological changes potentially harmful to the brain.

Allostatic Load Is Linked to Cortical Thickness Changes Depending on Body-Weight Status

Objective: Overweight (body mass index or BMI ≥ 25 kg/m²) and stress interact with each other in complex ways. Overweight promotes chronic low-inflammation states, while stress is known to mediate caloric intake. Both conditions are linked to several avoidable health problems and to cognitive decline, brain atrophy, and dementia. Since it was proposed as a framework for the onset of mental illness, the allostatic load model has received increasing attention. Although changes in health and cognition related to overweight and stress are well-documented separately, the association between allostatic load and brain integrity has not been addressed in depth, especially among overweight subjects.

Method: Thirty-four healthy overweight-to-obese and 29 lean adults underwent blood testing, neuropsychological examination, and magnetic resonance imaging to assess the relationship between cortical thickness and allostatic load, represented as an index of 15 biomarkers (this is, systolic and diastolic arterial tension, glycated hemoglobin, glucose, creatinine, total cholesterol, HDL and LDL cholesterol, triglycerides, c-reactive protein, interleukin-6, insulin, cortisol, fibrinogen, and leptin).

Results: Allostatic load indexes showed widespread positive and negative significant correlations (p < 0.01) with cortical thickness values depending on body-weight status.

Conclusion: The increase of allostatic load is linked to changes in the gray matter composition of regions monitoring behavior, sensory-reward processing, and general cognitive function.