National Consortium On Alcohol And NeuroDevelopment In Adolescence Research Articles

Multi-dimensional predictors of first drinking initiation and regular drinking onset in adolescence: A prospective longitudinal study

Early adolescent drinking onset is linked to myriad negative consequences. Using the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) baseline to year 8 data, this study (1) leveraged best subsets selection and Cox Proportional Hazards regressions to identify the most robust predictors of adolescent first and regular drinking onset, and (2) examined the clinical utility of drinking onset in forecasting later binge drinking and withdrawal effects. Baseline predictors included youth psychodevelopmental characteristics, cognition, brain structure, family, peer, and neighborhood domains. Participants (N=538) were alcohol-naïve at baseline. The strongest predictors of first and regular drinking onset were positive alcohol expectancies (Hazard Ratios [HRs]=1.67–1.87), easy home alcohol access (HRs=1.62–1.67), more parental solicitation (e.g., inquiring about activities; HRs=1.72–1.76), and less parental control and knowledge (HRs=.72–.73). Robust linear regressions showed earlier first and regular drinking onset predicted earlier transition into binge and regular binge drinking (βs=0.57–0.95). Zero-inflated Poisson regressions revealed that delayed first and regular drinking increased the likelihood (Incidence Rate Ratios [IRR]=1.62 and IRR=1.29, respectively) of never experiencing withdrawal. Findings identified behavioral and environmental factors predicting temporal paths to youthful drinking, dissociated first from regular drinking initiation, and revealed adverse sequelae of younger drinking initiation, supporting efforts to delay drinking onset.

Differences in Brain Network Topology Based on Alcohol Use History in Adolescents.

Approximately 6 million youth aged 12 to 20 consume alcohol monthly in the United States. The effect of alcohol consumption in adolescence on behavior and cognition is heavily researched; however, little is known about how alcohol consumption in adolescence may alter brain function, leading to long-term developmental detriments. In order to investigate differences in brain connectivity associated with alcohol use in adolescents, brain networks were constructed using resting-state functional magnetic resonance imaging data collected by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) from 698 youth (12-21 years; 117 hazardous drinkers and 581 no/low drinkers). Analyses assessed differences in brain network topology based on alcohol consumption in eight predefined brain networks, as well as in whole-brain connectivity. Within the central executive network (CEN), basal ganglia network (BGN), and sensorimotor network (SMN), no/low drinkers demonstrated stronger and more frequent connections between highly globally efficient nodes, with fewer and weaker connections between highly clustered nodes. Inverse results were observed within the dorsal attention network (DAN), visual network (VN), and frontotemporal network (FTN), with no/low drinkers demonstrating weaker connections between nodes with high efficiency and increased frequency of clustered nodes compared to hazardous drinkers. Cross-sectional results from this study show clear organizational differences between adolescents with no/low or hazardous alcohol use, suggesting that aberrant connectivity in these brain networks is associated with risky drinking behaviors.

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

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. 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 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. Graph theory metrics of segregation and integration were used to summarize SCN. 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. 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.

Associations between alcohol use and sex-specific maturation of subcortical gray matter morphometry from adolescence to adulthood: Replication across two longitudinal samples

Subcortical brain morphometry matures across adolescence and young adulthood, a time when many youth engage in escalating levels of alcohol use. Initial cross-sectional studies have shown alcohol use is associated with altered subcortical morphometry. However, longitudinal evidence of sex-specific neuromaturation and associations with alcohol use remains limited. This project used generalized additive mixed models to examine sex-specific development of subcortical volumes and associations with recent alcohol use, using 7 longitudinal waves (n = 804, 51% female, ages 12–21 at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). A second, independent, longitudinal dataset, with up to four waves of data (n = 467, 43% female, ages 10–18 at baseline), was used to assess replicability. Significant, replicable non-linear normative volumetric changes with age were evident in the caudate, putamen, thalamus, pallidum, amygdala and hippocampus. Significant, replicable negative associations between subcortical volume and alcohol use were found in the hippocampus in all youth, and the caudate and thalamus in female but not male youth, with significant interactions present in the caudate, thalamus and putamen. Findings suggest a structural vulnerability to alcohol use, or a predisposition to drink alcohol based on brain structure, with female youth potentially showing heightened risk, compared to male youth.

Prospective associations between cannabis use and depressive symptoms across adolescence and early adulthood

Cannabis use and occurrences of depression during adolescence are common. However, the temporal relationship between the two is less understood. Does depression lead to cannabis use, or does cannabis use lead to depression, or is it a combination of both? Furthermore, this directionality is confounded by other substance use, specifically binge drinking, which is common during adolescence. This study aimed to examine the temporal directionality of cannabis use and depression among a prospective, longitudinal, sequential cohort of 15 to 24-year-olds. Data were drawn from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study. The final sample included 767 participants. Multilevel regression models were run to assess concurrent (at the same time point) and prospective (1 year later) associations between cannabis use and depression. When measured concurrently, depressive symptoms did not significantly predict past-month cannabis use but did significantly predict more days of use among cannabis users. Prospective associations indicated that depressive symptoms significantly predicted cannabis use 1 year later and cannabis use significantly predicted depressive symptoms 1 year later. We found no evidence that these associations varied by age or binge drinking. Overall, the relationship between cannabis use and depression appears to be complex and not unidirectional.

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

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). 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. 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. 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.

Morning perception of sleep, stress, and mood, and its relationship with overnight physiological sleep: findings from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study.

This cross-sectional study investigated objective-subjective sleep discrepancies and the physiological basis for morning perceptions of sleep, mood, and readiness, in adolescents. Data collected during a single in-laboratory polysomnographic assessment from 137 healthy adolescents (61 girls; age range: 12-21 years) in the United States National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study were analysed. Upon awakening, participants completed questionnaires assessing sleep quality, mood, and readiness. We evaluated the relationship between overnight polysomnographic, electroencephalographic, sleep autonomic nervous system functioning measures, and next morning self-reported indices. Results showed that older adolescents reported more awakenings, yet they perceived their sleep to be deeper and less restless than younger adolescents. Prediction models including sleep physiology measures (polysomnographic, electroencephalographic, and sleep autonomic nervous system) explained between 3% and 29% of morning sleep perception, mood, and readiness indices. The subjective experience of sleep is a complex phenomenon with multiple components. Distinct physiological sleep processes contribute to the morning perception of sleep and related measures of mood and readiness. More than 70% of the variance (based on a single observation per person) in the perception of sleep, mood, and morning readiness is not explained by overnight sleep-related physiological measures, suggesting that other factors are important for the subjective sleep experience.

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

BackgroundBetween 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. MethodsWe 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. ResultsWe 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. ConclusionsSocial 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.

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.

Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach.

Cortical thickness changes dramatically during development and is associated with adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying spatially heterogeneous effects of alcohol. In this study, adolescents (n = 657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study who endorsed little to no alcohol use at baseline were assessed with structural magnetic resonance imaging and followed longitudinally at four yearly intervals. Seven unique spatial patterns of covarying cortical thickness were obtained from the baseline scans by applying an unsupervised machine learning method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants' longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. We found in six NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts. Among moderate drinkers the decline was faster in the younger adolescent cohort and slower in the older cohort. Among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort. The findings suggested that unsupervised machine learning successfully delineated spatially coordinated patterns of vertex-level cortical thickness variation that are unconstrained by neuroanatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers, an effect that is strongest among heavy drinkers.

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.

Prior test experience confounds longitudinal tracking of adolescent cognitive and motor development

BackgroundAccurate measurement of trajectories in longitudinal studies, considered the gold standard method for tracking functional growth during adolescence, decline in aging, and change after head injury, is subject to confounding by testing experience.MethodsWe measured change in cognitive and motor abilities over four test sessions (baseline and three annual assessments) in 154 male and 165 female participants (baseline age 12–21 years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. At each of the four test sessions, these participants were given a test battery using computerized administration and traditional pencil and paper tests that yielded accuracy and speed measures for multiple component cognitive (ion, Attention, Emotion, Episodic memory, Working memory, and General Ability) and motor (Ataxia and Speed) functions. The analysis aim was to dissociate neurodevelopment from testing experience by using an adaptation of the twice-minus-once tested method, which calculated the difference between longitudinal change (comprising developmental plus practice effects) and practice-free initial cross-sectional performance for each consecutive pairs of test sessions. Accordingly, the first set of analyses quantified the effects of learning (i.e., prior test experience) on accuracy and after speed domain scores. Then developmental effects were determined for each domain for accuracy and speed having removed the measured learning effects.ResultsThe greatest gains in performance occurred between the first and second sessions, especially in younger participants, regardless of sex, but practice gains continued to accrue thereafter for several functions. For all 8 accuracy composite scores, the developmental effect after accounting for learning was significant across age and was adequately described by linear fits. The learning-adjusted developmental effects for speed were adequately described by linear fits for ion, Emotion, Episodic Memory, General Ability, and Motor scores, although a nonlinear fit was better for Attention, Working Memory, and Average Speed scores.ConclusionThus, what appeared as accelerated cognitive and motor development was, in most cases, attributable to learning. Recognition of the substantial influence of prior testing experience is critical for accurate characterization of normal development and for developing norms for clinical neuropsychological investigations of conditions affecting the brain.

Detecting negative valence symptoms in adolescents based on longitudinal self-reports and behavioral assessments

BackgroundGiven the high prevalence of depressive symptoms reported by adolescents and associated risk of experiencing psychiatric disorders as adults, differentiating the trajectories of the symptoms related to negative valence at an individual level could be crucial in gaining a better understanding of their effects later in life. MethodsA longitudinal deep learning framework is presented, identifying self-reported and behavioral measurements that detect the depressive symptoms associated with the Negative Valence System domain of the NIMH Research Domain Criteria (RDoC). ResultsApplied to the annual records of 621 participants (age range: 12 to 17 years) of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), the deep learning framework identifies predictors of negative valence symptoms, which include lower extraversion, poorer sleep quality, impaired executive control function and factors related to substance use. LimitationsThe results rely mainly on self-reported measures and do not provide information about the underlying neural correlates. Also, a larger sample is required to understand the role of sex and other demographics related to the risk of experiencing symptoms of negative valence. ConclusionsThese results provide new information about predictors of negative valence symptoms in individuals during adolescence that could be critical in understanding the development of depression and identifying targets for intervention. Importantly, findings can inform preventive and treatment approaches for depression in adolescents, focusing on a unique predictor set of modifiable modulators to include factors such as sleep hygiene training, cognitive-emotional therapy enhancing coping and controllability experience and/or substance use interventions.

0192 Effects of emerging alcohol use on developmental trajectories of functional sleep measures in adolescents

Abstract Introduction Adolescence is characterized by developmental changes in sleep timing and architecture as well as alcohol use initiation. While the effects of acute and chronic alcohol use on sleep in adults are well-documented, much less is known in adolescents. We used longitudinal data from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) to examine how emerging alcohol use affected sleep architecture in adolescents. Methods Overnight polysomnographic recordings were made each year, for 4 years, in 94 adolescents (12–21 years at baseline, 43% female) from the NCANDA cohort. All participants were no or low (youth adjusted Cahalan score of zero) alcohol users at baseline. These data were used to examine developmental trajectories of sleep macro-architecture and sleep electroencephalographic (EEG) measures using linear mixed effect models (LMMs), considering age, sex, family history of alcohol use, body mass index, ethnicity, and alcohol use class (i.e., no-to-low, moderate or heavy) at each annual assessment. Results There were strong developmental changes in sleep macro-structure and EEG, most notably, a decrease in slow wave sleep percentage and slow wave (delta) EEG activity with advancing age (p=0.02)​. Compared to those who remained no-to-low drinkers, participants who became moderate/heavy drinkers during the follow-up period, had different sleep trajectories, especially those older at baseline at baseline, including higher slow wave activity (p = 0.04), higher REM sleep percentage (p = 0.03), poorer sleep efficiency​ (p=0.003), and longer latency to sustained sleep (p = 0.03). The effects of alcohol use depended on sex, with male heavy drinkers having more REM sleep than female heavy drinkers (p = 0.04). Overall, a positive family history of alcohol use was associated with less NREM sleep and shorter sleep duration. Conclusion Our results present novel findings showing that emerging alcohol use during adolescence exerts complex effects on sleep macro- and micro-structure, over and above normal developmental changes in sleep. These effects could, in part, be alcohol effects on brain maturation processes underlying sleep regulation. Support (If Any) AA021696, AA021690

A Longitudinal Examination of Alcohol-Related Blackouts as a Predictor of Changes in Learning, Memory, and Executive Function in Adolescents.

IntroductionIn adolescents, the relationship between alcohol-related blackouts (ARBs) and distinct cognitive changes lasting beyond intoxication is unclear. We examined ARBs as a predictor of persistent changes in the development of learning, memory, and executive function in participants from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study.MethodsDescriptive analyses of the NCANDA sample (N = 831, 50.9% female, 12–21 years at baseline) identified ARB patterns within participants with an ARB history (n = 106). Latent growth curve modeling evaluated ARB-related performance changes on four neuropsychological measures across five years, excluding baseline data to reduce the magnitude of practice effects over time (n = 790). Measures included the Penn Conditional Exclusion Test (PCET), Penn Letter N-back Test (PLBT), Penn Facial Memory Test immediate (PFMTi), and delayed (PFMTd) recognition trials, and the Rey Complex Figure Test copy (RCFTc), immediate recall (RCFTi), and delayed recall (RCFTd) trials. Multivariate models were fit for raw accuracy scores from each measure, with ARB history (i.e., presence of past-year ARBs) as the main independent variable. Age, sex, race, socioeconomic status, assessment site, and alcohol use (i.e., past-year frequency) were included as covariates. Interaction effects between ARB history and alcohol use frequency were tested.ResultsBy year five, 16% of participants had experienced at least one ARB (59% of whom reported > 1 ARB and 57% of whom had an ARB lasting > 1 h). After controlling for demographics and alcohol use, ARB history predicted attenuated PFMTd performance growth at year one. Interaction effects between ARB history and alcohol use frequency predicted attenuated PFMTd performance growth at years one and two. ARB history predicted attenuated RCFTi and RCFTd performance growth by year four, but not PCET or PLBT performance over time. By contrast, greater past-year alcohol use predicted attenuated PFMTi and PFMTd performance growth between years two and four in adolescents without an ARB history.ConclusionWe found that ARBs predict distinct, lasting changes in learning and memory for visual information, with results suggesting that the developing brain is vulnerable to ARBs during adolescence and emerging adulthood.

Self-reported sleep and circadian characteristics predict alcohol and cannabis use: A longitudinal analysis of the National Consortium on Alcohol and Neurodevelopment in Adolescence Study.

Growing evidence indicates that sleep characteristics predict future substance use and related problems. However, most prior studies assessed a limited range of sleep characteristics, studied a narrow age span, and included few follow-up assessments. Here, we used six annual assessments from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study, which spans adolescence and young adulthood with an accelerated longitudinal design, to examine whether multiple sleep characteristics in any year predict alcohol and cannabis use the following year. The sample included 831 NCANDA participants (423 females; baseline age 12-21years). Sleep variables included circadian preference, sleep quality, daytime sleepiness, the timing of midsleep (weekday/weekend), and sleep duration (weekday/weekend). Using generalized linear mixed models (logistic for cannabis; ordinal for binge severity), we tested whether each repeatedly measured sleep characteristic (years 0-4) predicted substance use (alcohol binge severity or cannabis use) the following year (years 1-5), covarying for age, sex, race, visit, parental education, and previous year's substance use. Greater eveningness, more daytime sleepiness, later weekend sleep timing, and shorter sleep duration (weekday/weekend) all predicted more severe alcohol binge drinking the following year. Only greater eveningness predicted a greater likelihood of any cannabis use the following year. Post-hoc stratified exploratory analyses indicated that some associations (e.g., greater eveningness and shorter weekend sleep duration) predicted binge severity only in female participants, and that middle/high school versus post-high school adolescents were more vulnerable to sleep-related risk for cannabis use. Our findings support the relevance of multiple sleep/circadian characteristics in the risk for future alcohol binge severity and cannabis use. Preliminary findings suggest that these risk factors vary based on developmental stage and sex. Results underscore a need for greater attention to sleep/circadian characteristics as potential risk factors for substance use in youth and may inform new avenues to prevention and intervention.

Did the acute impact of the COVID-19 pandemic on drinking or nicotine use persist? Evidence from a cohort of emerging adults followed for up to nine years

ObjectiveThis study examined the impact of the COVID-19 pandemic on drinking and nicotine use through June of 2021 in a community-based sample of young adults. MethodData were from 348 individuals (49% female) enrolled in a long-term longitudinal study with an accelerated longitudinal design: the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) Study. Individuals completed pre-pandemic assessments biannually from 2016 to early 2020, then completed up to three web-based, during-pandemic surveys in June 2020, December 2020, and June 2021. Assessments when individuals were 18.8–22.4 years old (N = 1,458) were used to compare drinking and nicotine use pre-pandemic vs. at each of the three during-pandemic timepoints, adjusting for the age-related increases expected over time. ResultsCompared to pre-pandemic, participants were less likely to report past-month drinking in June or December 2020, but there was an increase in drinking days among drinkers in June 2020. By June 2021, both the prevalence of past-month drinking and number of drinking days among drinks were similar to pre-pandemic levels. On average, there were no statistically significant differences between pre-pandemic and during-pandemic time points for binge drinking, typical drinking quantity, or nicotine use. Young adults who reported an adverse financial impact of the pandemic showed increased nicotine use while their peers showed stable or decreased nicotine use. ConclusionInitial effects of the pandemic on alcohol use faded by June 2021, and on average there was little effect of the pandemic on nicotine use.

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.

Multiple Instance Neuroimage Transformer.

For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1-weighted (T1w) MRIs. We first present several variants of transformer models adopted for neuroimages. These models extract non-overlapping 3D blocks from the input volume and perform multi-headed self-attention on a sequence of their linear projections. MINiT, on the other hand, treats each of the non-overlapping 3D blocks of the input MRI as its own instance, splitting it further into non-overlapping 3D patches, on which multi-headed self-attention is computed. As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). The learned attention maps highlight voxels contributing to identifying sex differences in brain morphometry. The code is available at https://github.com/singlaayush/MINIT.

Risk for depression tripled during the COVID-19 pandemic in emerging adults followed for the last 8 years.

The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic. Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms. The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms. The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.