Background Smoking is a concerning medical and social problem, yet how the brain links stress to continued smoking is still not well understood. This coordinate-based meta-analysis identified convergent activations for smoking cues, uncertain threat, and reward anticipation, and examined co-activation patterns to clarify whether smoking-cue activity in smokers relates to threat and reward activity in non-addicted controls. Methods We conducted a coordinate-based activation likelihood estimation (ALE) meta-analysis of 102 fMRI studies (N = 3,068), including 30 studies on smoking cue reactivity (n = 945), 48 on reward anticipation (n = 1,413), and 24 on uncertain threat processing (n = 1,621). We performed single, conjunction, and contrast analyses, followed by meta-analytic connectivity modeling (MACM) of key regions. Results Single analysis revealed smoking engaged bilateral ACC (-1.1, 46.2, -1.1), uncertain threat engaged bilateral insula (left = -33.6, 22, 4.3, right = 41.3, 22, 1), reward anticipation activated thalamus (0.9, 1.3, -3.1) and medial frontal gyrus (2.7, 6.6, 52.1). Conjunction and contrast analyses showed shared or unique activation for each task in its respective regions. MACM showed ACC co-activation with thalamus and medial frontal gyrus, while insula co-activated with ACC and inferior frontal gyrus. Conclusions Each process converged in a separate region, with no overlap between the smoking-cue map and either the threat or reward map. The ACC nonetheless co-activated with reward-related regions and shared network membership with the threat-related insula. On this basis we hypothesize a shift in motivation from stress-driven reward toward cue-driven craving, to be tested within subjects, and identify candidate neuromodulation targets for preventing stress-precipitated relapse.
Attention deficit hyperactivity disorder (ADHD) is hypothesized to be associated with delayed brain maturation rather than atypical brain development. While this developmental delay hypothesis has gained widespread recognition, research progress has stagnated, and the field lacks a consolidated overview of existing findings and challenges. This narrative review integrates multi-dimensional findings across behavioral manifestations, electroencephalography (EEG), structural,diffusion, and functional magnetic resonance imaging (MRI), and genetics, supporting a regionally specific and multi-trajectory heterogeneous delay model. Based on a critical discussion of the literature and recent advancements, four critical unresolved challenges are identified: (1) incomplete explanatory power of delay phenomena; (2) whether temporal delays lead to reduced peak amplitudes in developmental trajectories; (3) whether delayed trajectories can achieve developmental "catch-up" prior to brain maturation; and (4) heterogeneity in delayed developmental pathways. To address these challenges, we propose four strategies: (1) establishing longitudinal high-density tracking cohorts; (2) constructing multimodal brain-age prediction models; (3) identifying neurodevelopmental delay subtypes through heterogeneity modeling; and (4) quantifying ADHD-related developmental delays via disease progression modeling. These approaches aim to lay a foundation for precise diagnosis, treatment, and early intervention in ADHD. This review integrates existing evidence on the developmental delay hypothesis to help focus future efforts, while highlighting current challenges and potential solutions to inspire innovative directions for research into the neurodevelopmental mechanisms underlying ADHD.
BACKGROUND:Often emerging in early adolescence, social anxiety disorder (SAD) is a mental health condition characterised by persistent fears of negative evaluation. Neuroimaging studies implicate disruptions in the default mode network (DMN) in SAD, however, it remains unclear whether specific DMN regions are driving broader network alterations during this developmental period. METHODS:Thirty-six adolescents and young adults (aged 16-25) with SAD and 66 age- and gender-matched controls underwent resting-state functional magnetic resonance imaging. Using spectral dynamic causal modelling, we examined effective connectivity between core DMN nodes, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and bilateral inferior parietal cortex (IPL). Parametric empirical Bayes modelling was used to investigate between-group differences in these connectivity parameters. RESULTS:Compared to controls, SAD patients demonstrated increased excitatory connectivity from the PCC to the right IPL as well as excitatory connectivity from the left to right IPL. We also observed reduced PCC and MPFC self-inhibition and increased right IPL self-inhibition within SAD patients. Furthermore, leave-one-out cross-validation revealed that altered PCC self-inhibition was associated with social anxiety severity (r = .18, p = 0.039). CONCLUSIONS:Our findings suggest that SAD is associated with alterations in DMN effective connectivity, with converging evidence implicating the PCC as a dysfunctional hub. This is further highlighted by the association between PCC self-connectivity and social anxiety symptom severity. Future studies should determine whether modulating PCC connectivity could have therapeutic utility for those with SAD.
BACKGROUND:Psychotic-like experiences (PLEs) and autistic traits across development are associated with poor mental health outcomes. However, the relationship between PLEs and autistic traits, and their links with clinical and biological predictors of longitudinal outcomes, remain poorly understood. METHODS:PLEs and autistic traits were measured across a three year period using the Prodromal Questionnaire Brief and Child Behavior Checklist in 9,963 adolescents from the Adolescent Brain Cognitive Development Study®. Separate Growth Mixture Models identified trajectories for PLEs and autistic traits. Associations between trajectories with longitudinal internalizing and externalizing symptoms, cognition, environmental adversity, and harmonized diffusion measures of baseline fractional anisotropy (FA), were examined. RESULTS:Four trajectories were found for both PLEs and autistic traits: persistently low, persistently elevated, increasing, and decreasing. Persistently low trajectories were associated with favorable profiles across measures. Increasing or persistently elevated PLEs were associated with lower baseline FA, cognition, and socioeconomic disadvantage and linked to worsening internalizing symptoms and environmental adversity. Persistently elevated autistic traits were associated with lower cognition, more environmental adversity, and reduced FA. Overlapping trajectories of co-elevated or increasing PLEs and autistic traits conferred the highest risk, with impairments across measures. Compared to only-elevated autistic traits, only-elevated PLEs were associated with lower psychopathology, higher family conflict and life events, lower cognition, and specific white matter alterations. CONCLUSIONS:Adolescents with distinct longitudinal PLEs and autistic traits trajectories differed on psychopathology, cognitive performance, environmental adversity, and white matter microstructure. Examining trajectory-specific differences highlights heterogeneity in early adolescent neurodevelopment and may support the advancement of early-detection strategies for at-risk youth.
The transition from childhood to adolescence heralds a marked escalation in pediatric mental health risk, as well as major developmental shifts in sleep. Poor sleep health is a common, causal, and modifiable transdiagnostic mental health symptom and risk factor in youth. Yet, unraveling the sleep-mental health risk relationship over adolescence has proven to be deceptively challenging. Sleep health arises from complex biopsychosocial processes and can be measured across multiple methods and time scales. The interplay between profound developmental shifts in sleep over adolescence and the high-dimensional nature of sleep measurement often leads to significant data heterogeneity. As a result, computational approaches are necessary to parse out typical variation from at-risk patterns that may reflect warning signs of emerging mental illness. In this review, we propose sleep signatures as a strategy to characterize sleep health and accurately predict psychiatric outcomes in adolescence. Sleep signatures are within-person combinations of multiple sleep features that more holistically characterize individual-level patterns of sleep health. We propose the multidimensional sleep health framework as a basis for sleep signature development and discuss unique complexities in sleep measurement for adolescents, highlighting classic sleep measurement methods and new opportunities provided by modern wearable and smartphone-based sleep monitoring. Next, we review computational techniques to derive multidimensional, multimodal sleep signatures in a developmental context, focusing on variable-centered (factor analysis) and person-centered (clustering) approaches. Finally, we offer a roadmap for leveraging these approaches to identify sleep signatures salient to adolescent mental health through the ongoing Pediatric Precision Sleep Network project.
