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.
BACKGROUND:Adolescence is a critical developmental period during which subthreshold depression (StD) is common yet highly heterogeneous in its outcomes. Dysfunctional neural processing of reward and loss anticipation has been implicated in adolescent depression. However, it remains unclear whether these neural markers predict clinical trajectories in pre/early adolescents with StD. METHODS:Using data from the longitudinal cohort of Adolescent Brain Cognitive Development (ABCD) Study, we tracked pre/early adolescents over three years and classified them into four outcome groups: conversion to probable major depressive disorder (MDD), persistence of StD, remission of StD, and stable healthy controls (HC). Baseline functional magnetic resonance imaging (fMRI) data from the Monetary Incentive Delay (MID) task were used to quantify activation in key motivational regions during reward and loss anticipation of varying salience. Multinomial logistic regression models assessed whether baseline activation predicted later outcomes, with odds ratios (ORs) estimating the risk of MDD onset and symptom persistence. RESULTS:The results showed a 6.6-fold higher risk of developing probable MDD in pre/early adolescents with StD, as compared to HC. Relative to HC, pre/early adolescents with StD showed altered activation in striatal and midbrain regions during reward anticipation. Specifically, left nucleus accumbens (NAcc) activation showed a context-dependent predictive pattern: lower activation during high-reward anticipation predicted conversion to probable MDD, whereas lower activation during loss anticipation predicted symptom remission. CONCLUSION:Left NAcc activation may have context-dependent prognostic relevance for divergent parent-reported depressive symptom trajectories in pre/early adolescents.
Clinical decision-making in psychiatry has traditionally relied on rating scales and clinical impressions documented in the electronic health record (EHR). Yet, clinical interviews contain rich behavioral signals that remain underutilized in psychiatric care. Recent advances in artificial intelligence (AI) now enable quantification of these signals, and prior work demonstrates that computational measures of speech, language, and facial expression can inform diagnosis and estimate symptom severity. Despite this progress, most prediction efforts remain confined to single modalities and individual diagnoses and focus on diagnostic classification rather than clinically actionable outcomes such as treatment discontinuation or the need for crisis care. Here, we contextualize advances in behavioral quantification and multimodal data fusion, and present the Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR) study, a prospective cohort study of 2100 patients entering outpatient mental health care. PREDiCTOR is designed to develop and validate dynamic, multimodal prediction signatures that predict treatment discontinuation, emergency department visits, and hospitalizations over a one-year follow-up period. The study audiovisual recordings of clinical encounters, EHR data, cognitive assessments, smartphone passive sensing, therapeutic alliance measures, and audio/text diaries within a Contextual Bandit framework that continuously updates individualized outcome estimates as new data become available. Both interpretable features and learned embeddings are leveraged, with large language models serving as feature extractors rather than clinical decision-makers. We describe the study design, data collection, and processing pipelines, hybrid predictive modeling approach, and prospective validation strategy, and discuss the potential for translating dynamic behavioral quantification into individualized clinical prognostics.
Background Motivation arises from the integration of anticipated reward value and perceived control over effort allocation, yet the neural mechanisms through which these computations shape motivated behavior, and how they are disrupted in depression, remain poorly understood. Methods Here, we examined how neural tracking of value and control across key nodes of the mesocorticolimbic network (ventral tegmental area [VTA], nucleus accumbens [NAc], ventromedial prefrontal cortex [vmPFC], anterior insula) predicts individual differences in motivation in healthy adults (N=36) and individuals with major depressive disorder (MDD, N=39). Model-derived indices of external and internal motivation were estimated based on externally-controlled and internally-controlled effort selection for a range of monetary rewards. Combined with 7-Tesla multi-echo functional MRI optimized to dissociate signal across midbrain, subcortical and cortical regions, we thus parsed the contributions of neural processing of value versus control evaluation, on external and internal motivation. Results Across participants, stronger NAc value-tracking was associated with greater external motivation only. During control processing, VTA recruitment predicted both types of motivation, whereas anterior insula recruitment selectively associated with greater internal motivation, while vmPFC recruitment was associated with external motivation. This vmPFC recruitment during control evaluation was disrupted in MDD, and associated with heightened depression symptom severity and negative affect. Conclusions: Together, these findings identify dissociable neural pathways through which value and control computations shape motivated behavior and reveal a specific vmPFC-centered disruption in MDD.
Background Understanding how exposure-based trauma treatment modifies neural representations of trauma memory may lead to optimized and targeted interventions for posttraumatic stress disorder. Although exposure therapies reliably reduce symptoms, the neural mechanisms through which repeated trauma memory retrieval and processing reshape representational structure, and whether such changes can be bolstered through pharmacological agents shown to enhance extinction learning, remain poorly understood. Methods Women with PTSD related to interpersonal violence (N=80) completed two days of repeated exposure to an individualized trauma and neutral narrative during fMRI. Immediately after Day 1 exposures, participants were randomized to placebo (n=37) or 100mg Levodopa (L-DOPA; n=43), targeting dopaminergic enhancement of memory consolidation. Support vector machines characterized multivariate neural representations of distinct aversive cognitive states: painful stimulus delivery, trauma memory recall, self-reported anxiety during memory recall. To test generalization of representational changes across contexts, the Day 2 narratives were presented in either the same or different sensory context compared to Day 1. Results Across participants, medial prefrontal cortex (mPFC) network representations for each aversive state reduced from Day 1 to Day 2. There was no impact of L-DOPA on self-reported anxiety to the trauma memory or on neural representations of trauma memory recall or painful stimulus delivery. L-DOPA was associated with attenuated anxiety representations in the mPFC under a changed context, potentially consistent with enhanced generalization of representational updating. Conclusions These findings highlight that trauma memory representations in the mPFC are malleable with repeated exposure. The augmenting effect of L-DOPA was mostly null, except for decreased mPFC representations for anxiety in a changed context.
BACKGROUND:Posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) commonly co-occur and are associated with deficits in auditory processing and attention. To better understand the underpinnings of deficits in directed auditory attention, we examined whether individual PTSD symptom domains or mTBI severity were associated with distinct anomalies during early, middle, and late neural responses to auditory stimuli. METHODS:The Clinician Administered PTSD Scale (CAPS-IV) and Minnesota Blast Exposure Screening Tool were used to evaluate symptoms of previously deployed U.S. combat Veterans (N = 128). Linear mixed effects models were used to test whether PTSD symptom severity and mTBI blast injury severity were associated with atypical N1, Midlatency, and P300 event-related potentials during a directed attention oddball task. RESULTS:Greater Avoidance symptomatology was associated with larger N1 amplitude, suggesting increased reflexive, sensory attention allocation. Greater Dysphoria symptomatology was associated with smaller Midlatency amplitude, relevant to novelty and target detection. Greater Hyperarousal symptomatology was associated with smaller P300 amplitudes for rare, attended target stimuli, indicating blunted responsiveness to behaviorally relevant stimuli. There was no significant main effect of mTBI blast severity on neural responses or task performance and few effects related to categorical diagnoses of PTSD and mTBI. CONCLUSION:Atypical neural responses during the directed auditory attention oddball task are predicted primarily by PTSD symptomatology, not mTBI blast severity. Avoidance is tied to exaggerated early reflexive responses, dysphoria is related to muted novelty detection, and hyperarousal is associated with difficulty distinguishing between stimuli relevant and irrelevant to behavior.
