Biology subjects

Mewton, L.

Publications and source records attributed to Mewton, L..

5 recordsLinked to original sources

bioRxiv2026

The Brain Age Gap as a Predictor of Alcohol Initiation in Adolescence

BackgroundGrowing evidence suggests regional and network-level brain imaging features in late childhood are predictive of alcohol use in adolescence. However, the directionality of these effects (i.e. whether they reflect accelerated or delayed neuromaturation) are mixed. We applied a Brain Age Gap Estimation (BrainAGE) model to examine whether overall deviations from typical brain aging trajectories are predictive of (1) alcohol initiation and (2) use behaviour (experimentation versus binge drinking) in adolescence. MethodsData from the Adolescent Brain Cognitive Development study release 6.0 were used. Baseline (ages 9-11) structural imaging features (cortical volume, area, and subcortical volume) were used to estimate BrainAGE. Alcohol use was determined using self-report data from the Substance Use Interview and Timeline Follow-Back across follow-ups (waves 1-6; ages 10-17). Logistic generalized mixed effects models examined whether BrainAGE predicted group status between (1) non-initiators (n=3,639) and initiators (n=1,176), and; (2) experimentation (at least one full drink, no binge episodes; n=461) and binge drinking (at least one episode; n=438). ResultsWhen adjusting for age, sex, and pubertal status, a one-standard-deviation decrease in BrainAGE (equivalent to 1.64 years) at baseline was associated with a 9.5% increase in odds of alcohol initiation in adolescence. However, this effect did not survive adjustment for sociodemographic and prior alcohol exposure covariates. Further, BrainAGE did not discriminate between experimentation and binge drinking. ConclusionsFindings suggest BrainAGE in late childhood may reflect potential risk for alcohol initiation, but not behaviours, in adolescence. However, this association likely reflects complex interactions between brain structure and contextual factors, warranting further investigation.

neuroscience↗

bioRxiv2026

Unified Multi-Cohort Harmonisation and Normative Modelling of Neuroimaging Data via Hierarchical GAMLSS

Large-scale neuroimaging studies increasingly pool data across multiple cohorts, scanners, and acquisition protocols, introducing technical between-cohort variation that must be addressed before meaningful biological inference can be drawn. Existing harmonisation methods, particularly ComBat-based approaches, have been widely adopted for this purpose. However, they remain limited by Gaussian assumptions and by their focus on location or location-scale correction. In this study, we propose a unified hierarchical Generalised Additive Models for Location, Scale and Shape (GAMLSS) framework for multi-cohort harmonisation and normative modelling of structural neuroimaging data. The framework models cohort effects directly within all fitted distributional parameters, accommodates any parametric family for which exact inverse mapping is available, and returns harmonised values on the original measurement scale through centile-based quantile mapping. Normative deviation scores are obtained as a direct by-product of the same fitted model, enabling harmonisation and normative inference to be conducted jointly. The method was evaluated in a pooled longitudinal dataset comprising 88,126 observations across 237 structural neuroimaging features from six cohorts spanning childhood to late life: ABCD, IMAGEN, NCANDA, LIFE, UK Biobank, and MAS. Harmonisation performance was compared with ComBat, ComBat-GAM, and ComBat-LS using complementary criteria assessing data retention, residual batch effects, preservation of age-related and sex-related biological signal, and coherence of post-harmonisation lifespan trajectories. GAMLSS achieved near-complete removal of residual cohort effects, retained almost all valid observations post-harmonisation, and showed the strongest overall preservation of biological signal across validation metrics. In particular, it better preserved biologically plausible age trajectories for distributionally complex features such as white matter hypointensity volume, while simultaneously providing harmonised native-scale values and normative deviation scores within a single framework. These findings suggest that hierarchical GAMLSS offers a flexible and practical alternative to existing ComBat-based methods for large-scale neuroimaging harmonisation, particularly for features with non-Gaussian residual distributions and settings where cohort effects extend beyond differences in mean and variance.

neuroscience↗

bioRxiv2025

Longitudinal Neurocognitive Trajectories in a Large Cohort of Youth Who Use Cannabis: Combining Self-Report and Toxicology

Adolescents experience extensive neurocognitive development, with cannabis use potentially impacting developmental trajectories. Here, we comprehensively assess the influence of adolescent cannabis use onset on neurocognitive trajectories and consider how recent delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) may influence neurocognition. We use the large, diverse longitudinal Adolescent Brain Cognitive Development (ABCD) Study dataset, combining self-reported substance use with objective toxicological tests (hair, urine, breath, oral fluid). Longitudinal mixed methods of the full cohort (n=11,036, ages 9-17; 47% Female/53% Male) investigate time-varying cannabis onset on neurocognitive performance. Primary model covariates include sociodemographics, family history of substance use disorder, prenatal substance exposure, early psychopathology, other substance use, and nesting for participant ID, study site, and family ID. Secondarily, in participants with repeat toxicological hair testing (n=645; 38% Female/62% Male) at ages 12-16, we consider the influence of THC v. CBD v. Controls. Primary models included false discovery rate corrections (FDR-p<.05) while secondary models were interpreted at p<.01. Cannabis group interacted with age to show altered neurocognitive trajectories across domains (immediate recall and delayed memory, processing speed, inhibitory control, visuospatial processing, language, and working memory; {beta}s=-0.11- - 0.52). Secondary models indicated hair-identified THC exposure*age predicted worse episodic memory than in Controls ({beta}=-0.60, p=.007), with no difference between CBD exposed and Controls. Data suggest those who use cannabis show likely pre-existing better cognitive performance during late childhood, with reduced improvement or flattened trajectories over time. These neurocognitive trajectories in youth (ages 9-17) who initiate cannabis use were demonstrated after accounting for within-person change and numerous known confounds and improving accuracy in identifying cannabis use through incorporating toxicological measures. Continued monitoring of this cohort will clarify cannabinoid-cognition relationships into young adulthood, including the impact of timing of cannabis use initiation.

neuroscience↗

bioRxiv2025

Age- and alcohol-related differences in adolescent neurometabolite levels

Adolescence is a critical period for neurometabolite maturation as well as the onset of alcohol use, yet remains underexplored despite its significance for long-term neurodevelopmental outcomes. We used 3T proton magnetic resonance spectroscopy (MRS) to examine age- and alcohol-related associations with six neurometabolites in dorsal anterior cingulate cortex (dACC) that are involved in key neural functions: glutamate + glutamine (Glx), GABA plus macromolecules (GABA+), total N-acetylaspartate (tNAA), total choline (tCho), total creatine (tCr), and myo-inositol (mI). Participants were 84 adolescents (ages 17 - 22; 67% female) who completed MRS scans and self-reported past-60-day alcohol use via a modified Timeline Followback survey. Alcohol use variables included total drinking days, total binge drinking days, total number of drinks, and drinks per drinking day. Older adolescents had higher levels of GABA+, tNAA, tCho, and mI, and lower levels of Glx and Glx/GABA+; tCr was not associated with age. More alcohol use - specifically more drinking days, binge drinking days, and number of drinks - was associated with lower tNAA levels. Findings suggest age-related variation in dACC neurometabolites, which potentially reflect ongoing neuronal maturation, myelination, and shifts in excitatory and inhibitory neurotransmission. Lower tNAA among heavier drinkers may reflect associations between alcohol exposure and neuronal damage. Broader neurometabolic effects may emerge only with heavier or prolonged alcohol use.

neuroscience↗

bioRxiv2025

Structural Covariance Network Properties Predictive of Early Adolescent Alcohol Initiation

ImportanceEarly alcohol initiation (before age 15) is associated with adverse outcomes. Understanding mechanisms behind early alcohol initiation is essential for informing prevention efforts. ObjectiveTo examine whether structural covariance network properties at ages 9-10 years predict early alcohol initiation. DesignCase-control, population-based study design. SettingData from the Adolescent Brain Cognitive Development study were used. Baseline structural brain imaging data (ages 9-10) were used for generation and comparison of structural covariance networks. Data from baseline to 4-year follow-up ([≤]age 15) assessments were used to determine alcohol initiation. ParticipantsParticipants were excluded if they reported consuming a full drink of alcohol at baseline, or did not meet imaging inclusion criteria. Controls were excluded if they had not yet been assessed or were missing substance use data at 4-year follow-up. In total, 3,878 participants met study criteria, of which 182 participants initiated alcohol. Structural covariance network properties were compared between the full sample and a 1:1 propensity-matched sample based on age, sex, race, ethnicity, religion, parental education, prenatal alcohol exposure, and baseline alcohol sipping. Main Outcomes and MeasuresStructural covariance networks were estimated using regional cortical thickness and volume measurements. Measures of network segregation (modularity, clustering coefficient), integration (characteristic path length, global efficiency), and resilience (degree assortativity) were compared between groups. Early alcohol initiation was defined as consuming a full drink between baseline and 4-year follow-up ResultsAlcohol initiators (n=182, median[IQR] age, 10.3[9.9-10.8]; 101 female[55.5%]) demonstrated lower network segregation (modularity: area-under-the-curve[AUC] difference[95%CI]=-0.017[-0.017,-0.007], p=0.030; clustering coefficient: AUC[95%CI]=-0.026[-0.027,-0.012], p=0.0495) and higher network integration (characteristic path length: AUC[95%CI]=-0.106[-0.099,-0.046], p=0.020; global efficiency: AUC[95%CI]=0.011[0.005,0.011], p=0.010), compared to non-initiators (n=3,696, median[IQR] age, 9.9[9.4-10.4]; 1750 female[47.4%]) when controlling for age, sex, and mean cortical thickness. Within the matched sample, only differences in network integration were preserved (characteristic path length: AUC[95%CI]=-0.044[-0.032,0.035], p=0.010; global efficiency: AUC[95%CI]=0.003[-0.003,0.003], p=0.040). There were no differences between full or matched samples when comparing cortical volume structural covariance networks. Conclusions and RelevanceDifferences in cortical thickness structural covariance network properties at ages 9-10 predicted alcohol initiation before age 15. These findings suggest cortical thickness network topology may reflect a neuroanatomical risk marker for early alcohol initiation. Key pointsO_ST_ABSQuestionC_ST_ABSDo structural covariance network properties at age 9-10 years predict alcohol initiation prior to age 15? FindingsIn this case-control study of 3,878 participants, early adolescent alcohol initiators demonstrated differences in cortical thickness network integration and segregation compared to their non-initiating peers. MeaningAlcohol-naive adolescents who initiate alcohol use early in life demonstrate differences in structural brain network organization compared to their abstinent peers, which may reflect a neuroanatomical risk marker for early alcohol use.

neuroscience↗