Association of Heavy Drinking With Deviant Fiber Tract Development in Frontal Brain Systems in Adolescents

How Do Anger and Impulsivity Impact Fast-Food Consumption in Transitional Age Youth?

Introduction

Consumption of fast food has been linked to psychiatric distress, violent behaviors, and impulsivity in adolescents. The relationship between eating fast food, anger, and impulsivity has not been widely investigated. The National Consortium on Alcohol and Neurodevelopment in Adolescence community-based cohort consists of 831 youth, half at elevated risk factors for substance use disorders during adolescence, followed annually.

Methods

Impulsivity using Urgency, Premeditation, Perseverance, and Sensation Seeking Impulsive Behavior scale from annual assessments was examined in relation to self-reported fast-food consumption frequency and mobile application questions of anger. This study tested the hypotheses that youth anger may be predicted by fast-food consumption frequency and impulsivity using multiple regression, in addition to whether adolescent fast-food consumption frequency may be predicted by anger and impulsivity.

Results

Among youth, higher anger levels and impulsivity predicted greater frequency of fast-food consumption, and greater fast-food consumption frequency and impulsivity predicted higher anger levels.

Conclusions

This study's longitudinal findings are consistent with those of other studies that have found fast-food consumption and anger associated with impulsivity and also reveal a bidirectional link between anger and fast-food consumption. These results may point attention to food selection considerations for those at risk of anger and poorer psychiatric outcomes.

Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life

Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRI and diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging with and without Alcohol Use Disorder

BACKGROUND

Postural instability and brain white matter hyperintensities (WMH) are both noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to sway path/WMH relations in individuals with AUD and healthy control participants.

METHOD

The data comprised 404 balance platform sessions, yielding sway path length and MRI acquired cross-sectionally or longitudinally, in 102 control and 158 AUD participants, ages 25-80 years. Balance sessions were typically conducted on the same day as MRI FLAIR acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to relations between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race.

RESULTS

Initial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path/WMH relations. Inclusion of these factors as predictors in multiple regression analysis substantially attenuated the sway/WMH relations in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, possibly a reflection of the younger age of the Black sample.

DISCUSSION

Longer sway paths were related to larger WMH volumes in healthy men and women, with and without AUD. Critically, however, age nearly fully accounted for these relations.

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.

Consequences of adolescent drug use

Substance use in adolescence is a known risk factor for the development of neuropsychiatric and substance use disorders in adulthood. This is in part due to the fact that critical aspects of brain development occur during adolescence, which can be altered by drug use. Despite concerted efforts to educate youth about the potential negative consequences of substance use, initiation remains common amongst adolescents world-wide. Additionally, though there has been substantial research on the topic, many questions remain about the predictors and the consequences of adolescent drug use. In the following review, we will highlight some of the most recent literature on the neurobiological and behavioral effects of adolescent drug use in rodents, non-human primates, and humans, with a specific focus on alcohol, cannabis, nicotine, and the interactions between these substances. Overall, consumption of these substances during adolescence can produce long-lasting changes across a variety of structures and networks which can have enduring effects on behavior, emotion, and cognition.

Effects of emerging alcohol use on developmental trajectories of functional sleep measures in adolescents

STUDY OBJECTIVES

Adolescence is characterized by significant brain development, accompanied by changes in sleep timing and architecture. It also is a period of profound psychosocial changes, including the initiation of alcohol use; however, it is unknown how alcohol use affects sleep architecture in the context of adolescent development. We tracked developmental changes in polysomnographic (PSG) and electroencephalographic (EEG) sleep measures and their relationship with emergent alcohol use in adolescents considering confounding effects (e.g. cannabis use).

METHODS

Adolescents (n = 94, 43% female, age: 12-21 years) in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study had annual laboratory PSG recordings across 4-years. Participants were no/low drinkers at baseline.

RESULTS

Linear mixed effect models showed developmental changes in sleep macrostructure and EEG, including a decrease in slow wave sleep and slow wave (delta) EEG activity with advancing age. Emergent moderate/heavy alcohol use across three follow-up years was associated with a decline in percentage rapid eye movement (REM) sleep over time, a longer sleep onset latency (SOL) and shorter total sleep time (TST) in older adolescents, and lower non-REM delta and theta power in males.

CONCLUSIONS

These longitudinal data show substantial developmental changes in sleep architecture. Emergent alcohol use during this period was associated with altered sleep continuity, architecture, and EEG measures, with some effects dependent on age and sex. These effects, in part, could be attributed to the effects of alcohol on underlying brain maturation processes involved in sleep-wake regulation.

White matter microstructural integrity continues to develop from adolescence to young adulthood in mice and humans: Same phenotype, different mechanism

As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

Changes in Electroencephalography Alpha Associated With Childhood Neglect and Adolescent Alcohol Use

The present pilot study is interested in the relationship between childhood neglect, brain function, and alcohol use in adolescence. The goal is to guide future prevention and intervention efforts related to alcohol use following childhood neglect. This pilot study comprised 53 adolescents (12-14 years at baseline) recruited from the Department of Social Services (DSS). Self- and DSS-reported neglect, electroencephalography (EEG) alpha power, and alcohol use behaviors were measured over 1 year. Higher DSS neglect severity in year 1 was related to lower self-efficacy to alcohol use temptation in year 2. Lower EEG alpha power in the parietal region in year 1 was linked to lower self-efficacy to the temptation of alcohol use in year 2. This pilot project has value for using tools, such as EEG, in child maltreatment and alcohol use studies, including with underrepresented adolescents, to better understand brain-related mechanisms in home-based research.

Alcohol use disorder: Neuroimaging evidence for accelerated aging of brain morphology and hypothesized contribution to age-related dementia

Excessive alcohol use curtails longevity by rendering intoxicated individuals vulnerable to heightened risk from accidents, violence, and alcohol poisoning, and makes chronically heavy drinkers vulnerable to acceleration of age-related medical and psychiatric conditions that can be life threatening (Yoon, Chen, Slater, Jung, & White, 2020). Thus, studies of factors influencing age-alcohol interactions must consider the potential that the alcohol use disorder (AUD) population may not represent the oldest ages of the unaffected population and may well have accrued comorbidities associated with both AUD and aging itself. Herein, we focus on the aging of the brains of men and women with AUD, keeping AUD contextual factors in mind. Knowledge of the potential influence of the AUD-associated co-factors on the condition of brain structure may lead to identifying modifiable risk factors to avert physical declines and may reverse or arrest further AUD-related degradation of the brain. In this narrative review, we 1) describe quantitative, controlled studies of brain macrostructure and microstructure of adults with AUD, 2) consider the possibility of recovery of brain integrity through harm reduction with sustained abstinence or reduced drinking, and 3) speculate on the ramifications of accelerated aging in AUD as contributing to age-related dementia.

Assessing cross-lagged associations between depression, anxiety, and binge drinking in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study

BACKGROUND

Between 20 % and 30 % of teens suffer from depression or anxiety before reaching adulthood, and up to half also use or misuse alcohol. Although theories suggest bidirectional links between harmful alcohol use (e.g., binge drinking) and internalizing symptoms (i.e., depression and anxiety), empirical evidence to-date has been mixed. Systematic reviews have attributed mixed findings to limitations in study design, such as the utilization of between-person analyses and the focus on unidirectional effects. The goal of this study was to address these limitations by assessing bidirectional within-person associations between internalizing symptoms and binge drinking over the course of 5 years in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) sample, a large cohort recruited at ages 12-21 and followed annually on substance use and psychiatric functioning.

METHODS

We used latent curve models with structured residuals to examine within-person lagged associations between depression, anxiety, and past month counts of binge drinking using NCANDA data (N = 831). Analyses were supplemented with post-hoc power simulations.

RESULTS

We found marginal evidence linking binge drinking with subsequent depression symptoms one year later among females. We found no evidence that depression or anxiety predicted subsequent binge drinking despite sufficient power.

CONCLUSIONS

Social and cognitive consequences of binge drinking may predict later depression symptoms in adolescence and young adulthood for young women, though there was little evidence favoring self-medication models for binge drinking. We note several moderating variables and common factor mechanisms that may better explain this link.

[Binge drinking in the young population: Lost memory after initial ethanol exposure via neuroinflammation and epigenetic]

Binge drinking (BD) in young adults/adolescents can lead to cognitive deficits in the adult probably through neuroinflammation and epigenetic. However, the mode of action of alcohol during the initial exposure is less known while it may be the origin of the deficits seen in adults. Recent studies in adolescent rat hippocampus revealed that loss of memory occurred since the very first exposure to BD with similar mechanisms than those highlighted for longer alcohol exposure. Thus, initiation to BD in the young is responsible for cognitive deficits that will be probably entertained by repeated BD behavior. These kind of data may serve to reinforce the prevention campaigns towards the young population who practice BD.

Evidence from "big data" for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples

We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen's d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome

The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.

Earlier Bedtime and Effective Coping Skills Predict a Return to Low-Risk of Depression in Young Adults during the COVID-19 Pandemic

To determine the persistent effects of the pandemic on mental health in young adults, we categorized depressive symptom trajectories and sought factors that promoted a reduction in depressive symptoms in high-risk individuals. Specifically, longitudinal analysis investigated changes in the risk for depression before and during the pandemic until December 2021 in 399 young adults (57% female; age range: 22.8 ± 2.6 years) in the United States (U.S.) participating in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The Center for Epidemiologic Studies Depression Scale (CES-D-10) was administered multiple times before and during the pandemic. A score ≥10 identified individuals at high-risk for depression. Self-reported sleep behavior, substance use, and coping skills at the start of the pandemic were assessed as predictors for returning to low-risk levels while controlling for demographic factors. The analysis identified four trajectory groups regarding depression risk, with 38% being at low-risk pre-pandemic through 2021, 14% showing persistent high-risk pre-pandemic through 2021, and the remainder converting to high-risk either in June 2020 (30%) or later (18%). Of those who became high-risk in June 2020, 51% were no longer at high-risk in 2021. Logistic regression revealed that earlier bedtime and, for the older participants (mid to late twenties), better coping skills were associated with this declining risk. Results indicate divergence in trajectories of depressive symptoms, with a considerable number of young adults developing persistent depressive symptoms. Healthy sleep behavior and specific coping skills have the potential to promote remittance from depressive symptoms in the context of the pandemic.

Self-supervised learning of neighborhood embedding for longitudinal MRI

In recent years, several deep learning models recommend first to represent Magnetic Resonance Imaging (MRI) as latent features before performing a downstream task of interest (such as classification or regression). The performance of the downstream task generally improves when these latent representations are explicitly associated with factors of interest. For example, we derived such a representation for capturing brain aging by applying self-supervised learning to longitudinal MRIs and then used the resulting encoding to automatically identify diseases accelerating the aging of the brain. We now propose a refinement of this representation by replacing the linear modeling of brain aging with one that is consistent in local neighborhoods in the latent space. Called Longitudinal Neighborhood Embedding (LNE), we derive an encoding so that neighborhoods are age-consistent (i.e., brain MRIs of different subjects with similar brain ages are in close proximity of each other) and progression-consistent, i.e., the latent space is defined by a smooth trajectory field where each trajectory captures changes in brain ages between a pair of MRIs extracted from a longitudinal sequence. To make the problem computationally tractable, we further propose a strategy for mini-batch sampling so that the resulting local neighborhoods accurately approximate the ones that would be defined based on the whole cohort. We evaluate LNE on three different downstream tasks: (1) to predict chronological age from T1-w MRI of 274 healthy subjects participating in a study at SRI International; (2) to distinguish Normal Control (NC) from Alzheimer's Disease (AD) and stable Mild Cognitive Impairment (sMCI) from progressive Mild Cognitive Impairment (pMCI) based on T1-w MRI of 632 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI); and (3) to distinguish no-to-low from moderate-to-heavy alcohol drinkers based on fractional anisotropy derived from diffusion tensor MRIs of 764 adolescents recruited by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Across the three data sets, the visualization of the smooth trajectory vector fields and superior accuracy on downstream tasks demonstrate the strength of the proposed method over existing self-supervised methods in extracting information related to brain aging, which could help study the impact of substance use and neurodegenerative disorders. The code is available at https://github.com/ouyangjiahong/longitudinal-neighbourhood-embedding.

Learning disentangled representations in the imaging domain

Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare. In this tutorial paper, we motivate the need for disentangled representations, revisit key concepts, and describe practical building blocks and criteria for learning such representations. We survey applications in medical imaging emphasising choices made in exemplar key works, and then discuss links to computer vision applications. We conclude by presenting limitations, challenges, and opportunities.

Neuroplasticity, the Prefrontal Cortex, and Psychopathology-Related Deviations in Cognitive Control

A basic survival need is the ability to respond to, and persevere in the midst of, experiential challenges. Mechanisms of neuroplasticity permit this responsivity via functional adaptations (flexibility), as well as more substantial structural modifications following chronic stress or injury. This review focuses on prefrontally based flexibility, expressed throughout large-scale neuronal networks through the actions of excitatory and inhibitory neurotransmitters and neuromodulators. With substance use disorders and stress-related internalizing disorders as exemplars, we review human behavioral and neuroimaging data, considering whether executive control, particularly cognitive flexibility, is impaired premorbidly, enduringly compromised with illness progression, or both. We conclude that deviations in control processes are consistently expressed in the context of active illness but operate through different mechanisms and with distinct longitudinal patterns in externalizing versus internalizing conditions.

The impact of levamisole and alcohol on white matter microstructure in adult chronic cocaine users

Previous brain imaging studies with chronic cocaine users (CU) using diffusion tensor imaging (DTI) mostly focused on fractional anisotropy to investigate white matter (WM) integrity. However, a quantitative interpretation of fractional anisotropy (FA) alterations is often impeded by the inherent limitations of the underlying tensor model. A more fine-grained measure of WM alterations could be achieved by measuring fibre density (FD). This study investigates this novel DTI metric comparing 23 chronic CU and 32 healthy subjects. Quantitative hair analysis was used to determine intensity of cocaine and levamisole exposure-a cocaine adulterant with putative WM neurotoxicity. We first assessed the impact of cocaine use, levamisole exposure and alcohol use on group differences in WM integrity. Compared with healthy controls, all models revealed cortical reductions of FA and FD in CU. At the within-patient group level, we found that alcohol use and levamisole exposure exhibited regionally different FA and FD alterations than cocaine use. We found mostly negative correlations of tract-based WM associated with levamisole and weekly alcohol use. Specifically, levamisole exposure was linked with stronger WM reductions in the corpus callosum than alcohol use. Cocaine use duration correlated negatively with FA and FD in some regions. Yet, most of these correlations did not survive a correction for multiple testing. Our results suggest that chronic cocaine use, levamisole exposure and alcohol use were all linked to significant WM impairments in CU. We conclude that FD could be a sensitive marker to detect the impact of the use of multiple substances on WM integrity in cocaine but also other substance use disorders.

Alcohol and the Adolescent Brain: What We've Learned and Where the Data Are Taking Us

This article is part of a Festschrift commemorating the 50th anniversary of the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Established in 1970, first as part of the National Institute of Mental Health and later as an independent institute of the National Institutes of Health, NIAAA today is the world's largest funding agency for alcohol research. In addition to its own intramural research program, NIAAA supports the entire spectrum of innovative basic, translational, and clinical research to advance the diagnosis, prevention, and treatment of alcohol use disorder and alcohol-related problems. To celebrate the anniversary, NIAAA hosted a 2-day symposium, "Alcohol Across the Lifespan: 50 Years of Evidence-Based Diagnosis, Prevention, and Treatment Research," devoted to key topics within the field of alcohol research. This article is based on Dr. Tapert's presentation at the event. NIAAA Director George F. Koob, Ph.D., serves as editor of the Festschrift.

Differential effects of alcohol-drinking patterns on the structure and function of the brain and cognitive performance in young adult drinkers: A pilot study

INTRODUCTION

This study was aimed to determine how different patterns of alcohol consumption drive changes to brain structure and function and their correlation with cognitive impairments in young adult alcohol drinkers.

METHODS

In this study, we enrolled five groups participants and defined as: long-term abstinence from alcohol (LA), binge drinking (BD), long-term low dosage alcohol consumption but exceeding the safety drinking dosage (LD), long-term alcohol consumption of damaging dosage (LDD), and long-term heavy drinking (HD). All participants underwent magnetic resonance imaging (MRI) and functional MRI (fMRI) to acquire data on brain structure and function, including gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), functional connectivity (FC), and brain network properties. The cognitive ability was evaluated with the California Verbal Learning Test (CVLT), intelligence quotient (IQ), and short delay free recall (SDFR).

RESULTS

Compared to LA, GMV significantly decreased in the brain regions in VN, SMN, and VAN in the alcohol-drinking groups (BD, LD, LDD, and HD). ReHo was significantly enhanced in the brain regions in VN, SMN, and VAN, while fALFF significantly increased in the brain regions in VN and SMN. The number of intra- and inter-modular connections within networks (VN, SMN, sensory control network [SCN], and VAN) and their connections to other modules were abnormally changed. These changes adversely affected cognition (e.g., IQ, CVLT, SDFR).

CONCLUSION

Despite the small sample size, this study provides new evidence supporting the need for young people to abstain from alcohol to protect their brains. These findings present strong reasoning for updating anti-alcohol slogans and guidelines for young people in the future.

Growth trajectories of cognitive and motor control in adolescence: How much is development and how much is practice?

OBJECTIVE

Executive control continues to develop throughout adolescence and is vulnerable to alcohol use. Although longitudinal assessment is ideal for tracking executive function development and onset of alcohol use, prior testing experience must be distinguished from developmental trajectories.

METHOD

We used the Stroop Match-to-Sample task to examine the improvement of processing speed and specific cognitive and motor control over 4 years in 445 adolescents. The twice-minus-once-tested method was used and expanded to four test sessions to delineate prior experience (i.e., learning) from development. A General Additive Model evaluated the predictive value of age and sex on executive function development and potential influences of alcohol use on development.

RESULTS

Results revealed strong learning between the first two assessments. Adolescents significantly improved their speed processing over 4 years. Compared with boys, girls enhanced ability to control cognitive interference and motor reactions. Finally, the influence of alcohol use initiation was tested over 4 years for development in 110 no/low, 110 moderate/heavy age- and sex-matched drinkers; alcohol effects were not detected in the matched groups.

CONCLUSIONS

Estimation of learning effects is crucial for examining developmental changes longitudinally. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Adolescent Binge Drinking Is Associated With Accelerated Decline of Gray Matter Volume

The age- and time-dependent effects of binge drinking on adolescent brain development have not been well characterized even though binge drinking is a health crisis among adolescents. The impact of binge drinking on gray matter volume (GMV) development was examined using 5 waves of longitudinal data from the National Consortium on Alcohol and NeuroDevelopment in Adolescence study. Binge drinkers (n = 166) were compared with non-binge drinkers (n = 82 after matching on potential confounders). Number of binge drinking episodes in the past year was linked to decreased GMVs in bilateral Desikan-Killiany cortical parcellations (26 of 34 with P < 0.05/34) with the strongest effects observed in frontal regions. Interactions of binge drinking episodes and baseline age demonstrated stronger effects in younger participants. Statistical models sensitive to number of binge episodes and their temporal proximity to brain volumes provided the best fits. Consistent with prior research, results of this study highlight the negative effects of binge drinking on the developing brain. Our results present novel findings that cortical GMV decreases were greater in closer proximity to binge drinking episodes in a dose-response manner. This relation suggests a causal effect and raises the possibility that normal growth trajectories may be reinstated with alcohol abstinence.

Trajectories of brain development reveal times of risk and factors promoting resilience to alcohol use during adolescence

Alcohol use disorder (AUD) is recognized as harmful for the developing brain. Numerous studies have sought environmental and genetic risk factors that predict the development of AUD, but recently identified resilience factors have emerged as protective. This chapter reviews normal processes of brain development in adolescence and emerging adulthood, delineates disturbed growth neurotrajectories related to heavy drinking, and identifies potential endogenous, experiential, and time-linked brain markers of resilience. For example, concurrent high dorsolateral prefrontal activation serving inhibitory control and low nucleus accumbens activation serving reward functions engender positive adaptation and low alcohol use. Also discussed is the role that moderating factors have in promoting risk for or resilience to AUD. Longitudinal research on the effects of all levels of alcohol drinking on the developing brain remains crucial and should be pursued in the context of resilience, which is a promising direction for identifying protective biomarkers against developing AUDs.

Alcohol and Cannabis Use and the Developing Brain

PURPOSE

Alcohol and cannabis are the most commonly used substances during adolescence and are typically initiated during this sensitive neurodevelopmental period. The aim of this review is to provide a comprehensive overview of the most recent literature focused on understanding how these substances affect the developing brain.

SEARCH METHODS

Articles included in this review were identified by entering 30 search terms focused on substance use, adolescence, and neurodevelopment into MEDLINE, Embase, PsycINFO, ProQuest Central, and Web of Science. Studies were eligible for inclusion if they longitudinally examined the effect of adolescent alcohol and/or cannabis use on structural or functional outcomes in 50 or more participants.

SEARCH RESULTS

More than 700 articles were captured by the search, and 43 longitudinal studies met inclusion criteria, including 18 studies focused on alcohol use, 13 on cannabis use, and 12 on alcohol and cannabis co-use.

DISCUSSION AND CONCLUSIONS

Existing studies suggest heavy alcohol and cannabis use during adolescence are related to small to moderate disruptions in brain structure and function, as well as neurocognitive impairment. The effects of alcohol use include widespread decreases in gray matter volume and cortical thickness across time; slowed white matter growth and poorer integrity; disrupted network efficiency; and poorer impulse and attentional control, learning, memory, visuospatial processing, and psychomotor speed. The severity of some effects is dependent on dose. Heavy to very heavy cannabis use is associated with decreased subcortical volume and increased frontoparietal cortical thickness, disrupted functional development, and decreased executive functioning and IQ compared to non-using controls. Overall, co-use findings suggest more pronounced effects related to alcohol use than to cannabis use. Several limitations exist in the literature. Sample sizes are relatively small and demographically homogenous, with significant heterogeneity in substance use patterns and methodologies across studies. More research is needed to clarify how substance dosing and interactions between substances, as well as sociodemographic and environmental factors, affect outcomes. Larger longitudinal studies, already underway, will help clarify the relationship between brain development and substance use.

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

BACKGROUND

Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States.

OBJECTIVE

This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System.

METHODS

The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey.

RESULTS

Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level.

CONCLUSIONS

Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.

Representation Disentanglement for Multi-modal Brain MRI Analysis

Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning analysis can benefit from explicitly disentangling anatomical (shape) and modality (appearance) information into separate image presentations. In this work, we challenge mainstream strategies by showing that they do not naturally lead to representation disentanglement both in theory and in practice. To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities. To enable robust training, we further use a conditional convolution to design a single model for encoding images of all modalities. Lastly, we propose a fusion function to combine the disentangled anatomical representations as a set of modality-invariant features for downstream tasks. We evaluate the proposed method on three multi-modal neuroimaging datasets. Experiments show that our proposed method can achieve superior disentangled representations compared to existing disentanglement strategies. Results also indicate that the fused anatomical representation has potential in the downstream task of zero-dose PET reconstruction and brain tumor segmentation.