Neuroscience Research

Objectives To compare hospital claims and costs for major tobacco associated diseases with ICD 10 F17 tobacco dependence coding in Indonesian national health insurance claims and to assess whether the insurer records tobacco addiction or mainly pays for its complications. Design Retrospective claims based observational study using routinely collected administrative claims reported according to STROBE and the RECORD extension. Setting Indonesian national health insurance scheme Jaminan Kesehatan Nasional including referral hospital and primary care claims from 2015 to 2023. Participants A national mental health claims sample of 54820 members with at least one ICD 10 mental or behavioral F code diagnosis weighted to 1032022 members and 2074277 referral hospital visits. Primary and secondary outcome measures The primary outcome was verified claim costs in USD for hospital visits with a primary diagnosis of chronic obstructive pulmonary disease J44 or tracheal bronchial or lung cancer C33 to C34 or ischemic heart disease I20 to I25 or stroke I60 to I69. Secondary outcomes were counts of ICD 10 F17 tobacco dependence coding and the disease to F17 coding ratio. Results The four tobacco associated disease groups accounted for 13946 visits among 5223 patients and USD 4.20 million in verified costs representing 6.0 percent of hospital spending in the sample. Weighted costs were USD 74.7 million of which cardiovascular and cerebrovascular disease accounted for 95 percent. F17 appeared in only 51 referral hospital encounters and 26 primary care encounters. Only 2 of 5223 patients with these tobacco associated diseases or 0.04 percent were ever coded with F17. Conclusions The Indonesian national insurer paid substantially for tobacco associated morbidity while tobacco dependence was almost never coded. Smoking related diseases were reimbursed but tobacco dependence treatment was not captured as a financed care target. Embedding brief cessation care reimbursable pharmacotherapy and routine F17 coding into primary care could help shift tobacco related expenditure from downstream complications toward addiction care. Keywords tobacco dependence smoking cessation F17 coding health expenditure administrative claims Indonesia

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.

Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising treatment for tobacco use disorder (TUD). Although at a group level, active stimulation outperforms sham, at an individual level, variability exists in clinical response. The behavioral and neurobiological factors that differentiate those who respond to rTMS from those who do not remain unclear. To explore individual factors that influence acute responses to rTMS, N = 60 human participants received one session of rTMS to the dorsolateral prefrontal cortex (DLPFC) and to a control region (visual cortex; V5) in a randomized order. They completed behavioral assessments and neuroimaging before and after rTMS sessions. Hypotheses involving behavioral and neuroimaging predictors of response were pre-registered prior to completion of data collection. rTMS to the DLPFC led to significant reductions in self-reported cigarette craving compared with rTMS to a control brain region (p = 0.0006) and participants were classified as n = 38 responders and n = 22 nonresponders. Responders used significantly more cigarettes per day (M = 11.441) compared to nonresponders (M = 7.952), reported higher levels of cigarette craving (d = 1.059), and more severe nicotine withdrawal (d = 0.803) prior to rTMS. Neuroimaging analyses based on preregistered hypotheses indicated that DLPFC-frontoparietal and insula whole-brain functional connectivity did not differ significantly between responders and nonresponders. However, exploratory analyses revealed that responders had reduced pre-rTMS functional connectivity between the insula and nucleus accumbens, precuneus, and occipital pole. These findings suggest that response to rTMS for TUD is associated with greater baseline cigarette consumption, craving, and withdrawal, in addition to distinct functional connectivity patterns related to salience, reward, and self-referential processes, providing candidate behavioral and neural markers for personalized rTMS interventions for TUD.

Abnormal states such as erroneous behaviors are generally difficult to represent from neural data. However, such states are also known to have specific spatiotemporal features, indicating a feasibility of developing a method to focus on them. If a method can highlight these spatiotemporal features, it may effectively represent such abnormal states, helping evaluate abnormal brain functions. In the present study, we proposed the hierarchy of supported modules (HSM) to highlight spatiotemporal features that can represent abnormal states. HSM spatiotemporally transforms multidimensional neural time-series based on their spatiotemporal context. We evaluated HSM through decoding and similarity analyses using multiple publicly available datasets. In the HSM results, decoding accuracies were higher for erroneous behaviors than for normal behaviors, and similarities were lower between erroneous behaviors and normal behaviors than between normal behaviors, demonstrating the ability of HSM to capture the spatiotemporal features of erroneous behaviors. Surprisingly, many parts of these results were also present even before HSM learning, showing the virtue of HSM as a simple-to-use method. The proposed HSM method may help elucidate the mechanisms underlying erroneous behaviors.

Background: Cannabis use is highly prevalent among emerging adults (18-25 years), a developmental period marked by ongoing neurodevelopment and heightened risk for cannabis use disorder (CUD). Structural alterations in the orbitofrontal cortex (OFC) and medial prefrontal/anterior cingulate cortex (mPFC/ACC) have been linked to cannabis use, though findings remain inconsistent in directionality. To address this, we examined cortical thickness and surface area of the OFC and mPFC/ACC subregions using the high-resolution Glasser atlas, allowing for more granular characterization of associations with CUD severity. Method: One hundred eleven emerging adults (41% male, aged=20.6{+/-}1.1 years) reporting significant alcohol and/or cannabis use completed clinical assessments and structural MRI. The OFC and mPFC/ACC were segmented into seven and six subregions per hemisphere, respectively. Multiple linear regressions tested associations between cortical thickness or surface area and DSM-5 CUD symptom count, controlling for alcohol use and intracranial volume. Subregions surviving false discovery rate correction were examined in relation to depression, trauma-related symptoms, impulsivity, and cannabis use motives. Results: Greater CUD severity was associated with lower cortical surface area and greater cortical thickness in OFC and mPFC/ACC subregions. Lower OFC surface area was correlated with coping- and enhancement-related cannabis use motives. Lower mPFC/ACC surface area and greater thickness were associated with more severe depression, trauma-related symptoms, and impulsivity. Conclusion: In high-risk emerging adults, greater CUD symptom burden is associated with lower surface area and greater thickness in OFC and mPFC/ACC subregions. Using the high-resolution Glasser atlas, these findings provide a more precise characterization of structural correlates of CUD and highlight potential neurobiological markers linked to affective and motivational processes underlying cannabis use.

This study examined the extent to which goodness of pronunciation (GoP) scores and phonological posterior probabilities capture perceptual ratings of speech severity in individuals with motor speech disorders (MSD). Speech recordings of the word catastrophe were obtained from 489 participants, including 333 neurologically typical controls and 156 individuals with MSD. GoP scores were derived using traditional acoustic features and self-supervised speech representations, including WavLM and XLS-R, across multiple modeling approaches, while phonological posterior probabilities were extracted using Phonet. Model performance was evaluated using Kendall's rank correlations, regression, and receiver operating characteristic analyses against speech-language pathologists' perceptual ratings of sound distortion and intelligibility. Both GoP and phonological posterior probabilities were significantly associated with perceptual ratings. Self-supervised speech representations substantially outperformed traditional acoustic features, with WavLM-based GoP using k-nearest neighbors achieving the strongest performance. Across correlation, regression, and classification analyses, GoP consistently outperformed phonological posterior probabilities for both sound distortion and intelligibility. Age and gender had minimal influence on model-derived measures or their relationships with perceptual ratings. These findings demonstrate the value of self-supervised GoP as an objective measure of speech impairment while highlighting the complementary role of phonological posterior probabilities in characterizing articulatory aspects of motor speech disorders.

CRISPR-Cas genome editing toolkits have expanded the scope of genetic studies in various emerging model organisms. However, their applications are limited mainly to knockout experiments due to technical difficulties in establishing knock-in strains, which enable in vivo molecular tagging-based experiments. Here, we investigated knock-in strategies in the harlequin ladybug Harmonia axyridis, a model insect for evolutionary developmental biology, which shows more than 200 color pattern variations within a species. We tested several knock-in strategies using synthetic DNA templates. We found that ssDNA templates generated founder knock-in strains efficiently (2.5-11%), whereas the 5 regions of ssDNA templates were frequently deleted when the insert length exceeded [~]40 bases. To overcome this limitation, we designed several 3 extended DNA templates. Fast-annealed 3-extended double-stranded DNA templates, which were designed for tagging endogenous proteins with epitope tags, showed high founder generation efficiency (9.9-20.9%) and accuracy (30.8-85.7%). This strategy is also applicable to the two-spotted cricket Gryllus bimaculatus, suggesting that the fast-annealed 3-extended dsDNA template is a versatile DNA template for generating knock-in strains in emerging model insects for developmental genetic studies. Summary statementFast-annealed 3-extended dsDNA templates facilitate efficient CRISPR-Cas9-mediated knock-in in emerging model insects.

Defining the direct postsynaptic targets of selected neuronal populations remains a major challenge for neural circuit mapping. Vesicular stomatitis virus (VSV) spreads efficiently in the anterograde direction, but replication-competent VSV undergoes multistep spread and therefore cannot distinguish direct from indirect downstream targets. Here, we developed a glycoprotein-deleted VSV (VSVdG)-based strategy for one-step anterograde tracing using AAV-mediated trans-complementation with several adaptations. In this system, VSVdG was engineered to encode Cre, allowing a Cre-dependent AAV to express VSV-G only after VSVdG infected the same cells, thereby limiting VSV-G expression to a short time window. To reduce VSV-M-mediated cytotoxicity, we introduced the M33A/M51R double-mutant VSV-Md variant. Using the basal ganglia circuit as a model, these adaptations enabled VSVdG spread from the striatum to expected downstream targets in mice of both sexes. Efficient VSVdG-based one-step spread required loss of type I interferon signaling in IFNAR1-knockout mice and additional suppression of cytokine-mediated antiviral responses that were independent of type I and type II interferon signaling. This was achieved either by AAV-mediated delivery of rabies virus phosphoprotein from the CVS-N2c strain or by a cytokine-blocking antibody cocktail. Although cells labeled by VSV transmission were confined to expected brain regions, the downstream labeled cells included both neurons and glia, revealing an important limitation for interpreting this approach as strictly neuron-to-neuron monosynaptic anterograde spread. Overall, this study provides a proof-of-concept VSVdG strategy for one-step anterograde circuit tracing and defines viral toxicity, innate immunity, and cell-type specificity constraints that must be addressed to develop a monosynaptic anterograde viral tracer. Significance StatementMapping direct downstream targets of defined neuronal populations is essential for understanding neural circuit function, but reliable monosynaptic anterograde viral tracers remain limited. We developed a VSVdG-based strategy that uses AAV-mediated trans-complementation to restrict VSV-G expression to starter cells in a short time window and incorporates a VSV-M variant to reduce toxicity. In the mouse basal ganglia, this system enabled one-step spread from the striatum to expected output regions when innate antiviral barriers were suppressed. Our results identify type I interferon and additional type I/type II interferon-independent cytokine signaling as major restrictions on VSVdG spread. This study establishes proof of principle for VSV-based one-step anterograde tracing while defining viral toxicity, innate immunity, and cell-type specificity constraints for further improvement.

Regular cannabis use has been associated with alterations in reward-related neural processes, yet findings remain inconsistent and the relationship between neural activity and behavioural performance is not fully understood. The present study aimed to characterise neural and behavioural correlates of reward processing in regular cannabis users (CU) compared with matched non-users (NU) using the Monetary Incentive Delay Task (MIDT). Firstly, we assessed behavioural performance through reaction times, accuracy and monetary earnings to determine whether potential neural alterations were reflected in task performance. Secondly, focusing on reward-related brain regions, we examined group differences in BOLD functional MRI activity during anticipation and outcome phases separately for monetary win and loss conditions. Finally, we explored the association between behavioural performance and neural activation. Our findings indicate that regular cannabis use is associated with altered engagement of key nodes within the mesocorticolimbic circuit during both anticipatory and outcome phases of reward processing, accompanied by impaired behavioural performance. Particularly, compared with NU, CU showed (I) lower striatal activity during anticipation of monetary win and higher ventral striatum and frontal pole activity during anticipation of monetary loss; (II) greater VTA activation during outcome of successful monetary win and loss avoidance and lower frontal pole activity during outcome of unsuccessful loss avoidance; (III) impaired behavioural performance, reflected in lower monetary rewards and a trend towards slower reaction times and reduced accuracy; (IV) disrupted brain-behaviour coupling. Results from this study may help inform future research on the neurobiological mechanisms underlying changes in reward function and the resultant behavioural consequences of cannabis use.

Neural systems exhibit multiple firing states that reflect an organism's internal state and modulate the relationship between external environmental stimuli and behavior. Several studies have inferred these latent states by supplementing the traditional hidden Markov Model (HMM) with generalized linear models (GLMs) with non-Poisson behavioral observations. However, understanding the relationship between internal brain states and behavior also requires modeling the neural activity. Nonetheless, fitting multi-neuron GLM-HMMs is non-trivial due to high sparsity, collinearity, and low trial counts in neuronal datasets. Therefore, we built a robust multi-neuron GLM-HMM framework that uncovers latent states from population activity while incorporating the influence of time-stamped task variables and spike histories. To obtain reliable model parameters, we employ a modified expectation-maximization procedure. Specifically, we show that incorporating neuron-adaptive penalization in the maximization step overcomes the covariate co-linearity issues typical of time-stamped events and sparse spiking, yielding stable estimates of Poisson GLM coefficients. Furthermore, we incorporate a trust-region algorithm to ensure stable M-step convergence in the presence of ill-conditioned Hessians that can lead to unstable Newton-Raphson updates. We further demonstrate the utility of leave-one-out cross-validation analysis for evaluating model performance on datasets with low trial counts and without breaking their temporal structure. We evaluate our framework on three electrophysiological datasets from primates and rodents as they perform a decision-making task, demonstrate stable model convergence, and discuss the behavioral relevance of the inferred states.

Leucine-Rich Repeat Kinase 2 (LRRK2) is a signaling molecule involved in Parkinsons disease pathomechanisms. In disease, the LRRK2 protein displays both a toxic gain of kinase function and a loss of phosphorylation at heterophosphosites found in an extended loop of the LRR domain. RAB GTPases, such as RAB29, have been identified as upstream activators of LRRK2. Indeed, co-expression of LRRK2 with RAB29 induces a hyperactivation of LRRK2 kinase activity, however the role of the LRRK2 heterologous phosphorylation status in its activation remains unknown. Here, our aim was to determine the role of LRRK2 heterologous phosphorylation on its activation by RAB29. Using single and compound phosphodead or phosphomimetic mutants of LRRK2 we show differential sensitivity of LRRK2 phosphomutants to activation by RAB29, with phosphodead mutants being more susceptible to be activated than phosphomimetic mutants. Interestingly, we find that the single phosphodead S910A LRRK2 mutant displays an activation of LRRK2 kinase activity similar to that observed for the compound phosphodead 6xS>A LRRK2 mutant (S860A/S910A/S935A/S955A/S973A/S976A). Time-course analysis revealed that phosphodead mutants displayed higher but also faster activation by RAB29. In addition, both physical interaction between LRRK2 and RAB29 as well as RAB29-induced recruitment of LRRK2 to the trans-Golgi network (TGN) was enhanced by phosphodead compared to phosphomimetic mutants. To confirm effects on native LRRK2, we tested a panel of ten nanobodies targeting LRRK2 that stabilized LRRK2 phosphorylation at varying levels. Nanobodies stabilizing LRRK2 at low S935 phosphorylation levels showed enhanced RAB29-induced activation compared to nanobodies not affecting pS935 LRRK2. Finally, we tested whether LRRK2 heterologous phosphorylation could affect centrosome cohesion deficits, a phenotype that has been linked to LRRK2 hyperactivation, and found that both the phosphodead LRRK2 as well as a nanobody stabilizing dephosphorylated LRRK2 enhanced the centrosome cohesion deficit. Our findings indicate that hyperactivability of LRRK2 is directly related to its heterologous phosphorylation status, with dephosphorylation leading to strong hyperactivation of LRRK2 by upstream activating RABs, and phosphorylated LRRK2 showing the opposite. This implies that strategies favoring LRRK2 phosphorylation will have therapeutic benefit.

Sleep supports episodic memory consolidation, yet it remains unclear how naturalistic post-encoding sleep quality relates to the neural reinstatement of episodic representations across adulthood. The present study examined whether sleep discontinuity during the retention interval predicted delayed context memory and encoding-retrieval similarity (ERS) of EEG in younger and older adults. Participants completed an object-scene context memory task with immediate and delayed retrieval, while EEG was recorded during encoding and retrieval. Actigraphy was used to measure sleep across the post-encoding retention period, and principal component analysis identified sleep discontinuity and sleep time components. Behavioral results showed that greater post-encoding sleep discontinuity, but not sleep time, was associated with poorer delayed memory accuracy for mismatching object-context pairs across age. ERS analyses further showed that greater sleep discontinuity was associated with reduced ERS for correctly rejected mismatching pairs across frontal and posterior spatiotemporal clusters. Age moderated sleep-ERS associations: greater sleep discontinuity was generally related to lower ERS in younger adults, whereas some spatiotemporal clusters showed positive associations in older adults, potentially reflecting compensatory or effortful retrieval-related processing in poorer sleepers. Together, these findings suggest that sleep continuity during the post-encoding retention interval is important for preserving high-fidelity episodic representations needed for later context discrimination. More broadly, the results demonstrate that naturalistic sleep fragmentation is linked to both behavioral memory outcomes and neural reinstatement across adults.

BackgroundAdolescence is characterized by profound neurodevelopmental changes that shape large-scale brain network organization and may confer vulnerability to risk-taking behaviors, including alcohol use. While cross-sectional and prospective studies have examined functional connectivity (FC) alterations before and after consumption, there is little evidence of how networks evolve during adolescence. MethodsThe present longitudinal study investigated electrophysiological FC trajectories during alcohol initiation using resting-state magnetoencephalography (MEG). 61 alcohol-naive adolescents (mean age at baseline = 14.4) were assessed and re-evaluated two years later (mean age = 16.4). ResultsAt baseline, stronger FC in theta (4-8 Hz), alpha (8-12 Hz), and high-beta (20-30 Hz) bands predicted greater alcohol consumption at follow-up, replicating previous findings. Longitudinal analyses with linear mixed-effects models revealed significant stage x SAUs interactions across all three frequency bands. Adolescents with low-to-moderate alcohol use showed normative increases in FC over time, consistent with typical neurodevelopmental maturation. In contrast, heavier drinkers exhibited stabilization or reduction of FC, suggesting a divergence from normative trajectories. Notably, theta-band hyperconnectivity persisted after alcohol initiation and remained positively associated with current alcohol consumption, particularly across anteroposterior connections. ConclusionThese findings indicate heterogeneous neurodevelopmental trajectories associated with alcohol use severity. Elevated pre-consumption connectivity, especially in the theta band, may reflect a vulnerability marker rather than solely a consequence of alcohol exposure. Overall, results highlight the importance of considering individual variability in brain maturation when examining adolescent substance use and suggest that early hyperconnectivity may signal increased risk for heavier alcohol involvement.

Long-term, automated tracking of group-housed social animals using RFID (radio frequency identification) is a promising approach in ethological neuroscience. However, low-frequency (LF) RFID, while long-established in the field, is constrained by its inherent low data rates, which lead to two critical limitations: (1) compromised spatiotemporal resolution, and (2) the inability to identify multiple tags (animals) simultaneously. To address these limitations, we developed eeeHive, a high-frequency (HF) RFID-based animal tracking system with a fully custom hardware architecture that enables high-speed, multiplexed antenna polling and concurrent multi-tag reading. The polling time per antenna in eeeHive was 5.9 ms, with an additional 8.2 ms read time per tag. We applied the system to track 24 mice for one week, and six common marmosets for seven weeks. The system successfully tracked individuals even within dense clusters, revealing complex behavioral traits characterized by spatial utilization, temporal dynamics, behavioral regularity, and inter-individual relationships. Additional tests with Japanese fire-bellied newts and Nile tilapia juveniles demonstrated comparable tracking performance in aquatic environments. Taken together, eeeHive overcomes the inherent limitations of conventional LF RFID, establishing a powerful HF RFID-based platform for fine-scale behavioral tracking of group-housed animals across terrestrial and aquatic species.

Tomato yellow leaf curl virus (TYLCV) is a devastating viral pathogen threatening agricultural crops globally. In this study, we identified a novel TYLCV isolate (TYLCV-YN6244), which caused viral epidemic in resistant tomato cultivars at Yuanmo county, Yunnan Province of China. We determined the complete genome of TYLCV-YN6244 and found it encoded six viral proteins characteristic of Geminivirus. We identified its V2 protein as a potent viral suppressor of RNA silencing (VSR), and generated infectious clone of wildtype TYLCV-YN6244, or V2-defective TYLCV-YN6244 (TYLCV-YN6244-{Delta}V2) in which V2 was deleted. Both of infectious clones were capable of systemically infecting tobacco and tomato. However, TYLCV-YN6244 but not TYLCV-YN6244-{Delta}V2 could cause disease symptoms in wildtype tobacco or tomato plants, and viral accumulation was drastically reduced in plants infected with TYLCV-YN6244-{Delta}V2 compared to TYLCV-YN6244 while the efficiency of virus-derived small interfering RNAs (vsiRNAs) biogenesis was conversely increased in plants infected with TYLCV-YN6244-{Delta}V2. Surprisingly, small RNA profiling indicated that 21nt and 22nt rather than 24nt vsiRNAs were predominantly produced in tomato plants infected with either TYLCV-YN6244 or TYLCV-YN6244-{Delta}V2. Furthermore, transcriptome analyses revealed that TYLCV-YN6244 or TYLCV-YN6244-{Delta}V2 infection differentially modulated metabolism and defense-related pathways in tomato, probably underlying distinct viral pathogenicity and disease symptoms induced in plants. Overall, our research not only identified a novel pathogenic TYLCV isolate but also characterized molecular biology and host response in tomato with infectious clones firstly developed, with implications in untangling virus-host interaction for developing novel resistance in crop tomato.

Importance: Problematic use of the internet (PUI) behaviors, including problematic gaming, social media use, smartphone use, and general internet use, have been increasingly studied worldwide. So far, it is unclear what the global prevalence of PUI is. Objective: To critically appraise existing systematic reviews and meta-analyses on the prevalence of PUI behaviors and generate aggregated global prevalence estimates across different manifestations and definitions. Data Sources: MEDLINE (Ovid), Embase (Ovid), Scopus, Web of Science, CINAHL, and the Cochrane Review Library were searched for relevant articles from database inception to the most recent available search prior to manuscript preparation. Searches targeted systematic reviews and meta-analyses reporting prevalence for PUI-related behaviors. Study Selection: Systematic reviews and meta-analyses of observational studies reporting prevalence estimates for problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, or sexting were included. Scoping reviews were retained for descriptive synthesis only. Data Extraction and Synthesis: An umbrella review methodology was used. Data extraction and methodological appraisal were conducted using AMSTAR-2 to assess the quality of included systematic reviews up to February 2026. Primary studies included in each review were extracted and pooled using random-effects meta-analysis. Analyses were conducted to estimate pooled prevalence with 95% confidence intervals (CIs) and heterogeneity across non-overlapping primary studies. Small-study effects were examined. Main Outcomes and Measures: Global pooled prevalence estimates for PUI behaviors, including problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, and sexting. Results: Eleven reviews, including 10 systematic reviews and 1 scoping review, met inclusion criteria, representing data from 3,145,428 individuals, of whom 3,030,023 were included in pooled prevalence analyses. Across regions, pooled prevalence estimates were 6% (95% CI, 5%-7%) for problematic gaming, 16% (95% CI, 15%-17%) for problematic internet use, 32% (95% CI, 28%-35%) for problematic smartphone use, and 23% (95% CI, 19%-28%) for problematic social media use. Substantial heterogeneity (I2 > 99%) was observed across primary studies, reflecting variation in study methodologies, sampled populations, and definitions of PUI behaviors. Conclusions and Relevance: PUI behaviors appear to affect a substantial proportion of the global population. However, methodological concerns were common, with 9 of 10 systematic reviews rated as having low or critically low confidence according to AMSTAR-2. Evidence remains concentrated in East Asia and Europe, and many reviews combine heterogeneous populations and sampling strategies. Additional high-quality epidemiological research, including studies in underrepresented regions, is needed to refine prevalence estimates, clarify risk factors, and support the development of standardized criteria for PUI behaviors.

The axon initial segment (AIS) undergoes structural plasticity and refines neuronal excitability, yet the underlying mechanisms remain unclear. We here developed an in vivo CRISPR/Cas9 knockout platform using an all-in-one triple-guide RNA vector introduced via electroporation and employed this approach to seek molecules that regulate the developmental shortening of AIS in the chicken nucleus magnocellularis. We have targeted fourteen molecules associated with microtubules and found that knockouts of glycogen synthase kinase 3{beta} (GSK3{beta}) and Tau disabled the AIS shortening. Conversely, overexpression of constitutively active form of GSK3{beta} facilitated the AIS shortening in vivo. This extensive shortening was replicated in slice cultures, which was occluded by stabilization of microtubules. These results suggested that microtubule remodeling by GSK3{beta} activity contributed to the AIS shortening. This study thus provides a genetic approach suitable for genetic screening that allows identifying regulators of the AIS plasticity in the chicken brain.

Adolescent use of alcohol, nicotine, and marijuana remains a major public health concern in the United States. Early identification of youth at elevated risk is critical for prevention before use begins or escalates. We developed and evaluated a longitudinal machine learning framework to predict alcohol, nicotine, and marijuana use at the next observed assessment wave. Data came from the Adolescent Brain Cognitive Development (ABCD) Study Release 6.0. The models incorporated predictors from multiple domains, including demographics, friends, family and community context, mental health, physical health, and prior substance-related behaviors. To reduce information leakage across individuals, we implemented a leakage-aware stacked ensemble. This ensemble combined diverse base learners through out-of-fold predictions and an elastic-net meta-learner. Across all three substances, the lagged stacked ensemble outperformed the cross-sectional stack and all single base learners. Adolescents identified as highest risk showed substantially higher observed rates of substance use than would be expected under random screening. Feature-importance analyses showed that the full longitudinal models were strongly influenced by developmental timing and prior-use history. Analyses restricted to current-wave features revealed distinct substance-specific risk patterns beyond prior-use history and developmental timing. Bootstrap stability analyses identified top-ranked features showing consistent positive predictive relevance across resampled adolescents. These findings suggest that longitudinal, leakage-aware machine learning can generate substance-specific risk estimates to support targeted prevention and screening in adolescent populations.

The acquisition of sensorimotor skills critically depends on basal ganglia (BG)-thalamo-cortical circuits. Prevailing theories propose that the BG optimize motor output through reinforcement learning (RL), using internal performance evaluations to approximate stochastic gradient ascent. However, this framework struggles in non-convex performance landscapes, where local optima hinder efficient learning. Songbirds provide a compelling biological example of robust sensorimotor learning, mastering complex vocalizations through trial-and-error within a specialized BG-thalamo-cortical architecture. Here, we present a computational model constrained by the anatomy, physiology, and developmental trajectory of the zebra finch song system. The model combines a BG-driven RL pathway with a parallel cortical motor pathway that progressively consolidates successful motor patterns via Hebbian plasticity. In addition, we incorporate synaptic volatility within the BG pathway, introducing structured variability across learning. Through simulations of vocal learning using both a biophysical syrinx model and synthetic performance landscapes, we demonstrate that this dual-pathway architecture reliably converges to global optima and outperforms standard and noise-annealed RL approaches. The model reproduces key experimental features of song learning, including non-monotonic learning trajectories, a gradual reduction in motor variability, and the developmental transfer of motor control from subcortical to cortical circuits. Mechanistically, delayed maturation of the cortical pathway provides an implicit regulation of the exploration-exploitation trade-off, while synaptic volatility enables escape from local optima. These results highlight the importance of neural circuit architecture and dynamics in efficient learning, and suggest biologically inspired design principles for improving the robustness and sample efficiency of artificial RL systems in complex sensorimotor domains.

Neurological and neuropsychiatric conditions affect 43% of the global population, many shaped by modifiable lifestyle exposures, yet their relationship to cortical haemodynamics is poorly characterised. The dorsolateral prefrontal cortex (dlPFC) is a particularly tractable target: it underpins executive function, is disrupted across neuropsychiatric and age-related conditions, and lies on the cortical surface, within reach of scalable, wearable-grade optical neuroimaging. We present LUCID, a longitudinal study of 92 healthy adults combining consumer wearable sleep and physical activity metrics with task-evoked dlPFC haemodynamics, measured by time-domain functional near-infrared spectroscopy (TD-fNIRS). Log-transformed peak dlPFC activation was negatively associated with reaction time (RT) across the 2N-Back and Stroop tasks and both hemispheres (r = -0.37 to -0.53), greater activation accompanying faster responses, consistent with a capacity/recruitment account. Activation showed moderate test-retest reliability (intraclass correlation coefficient, ICC = 0.56-0.71), with between-person variance exceeding within-person fluctuation, indicating stable individual differences. Demographic and lifestyle features incrementally predicted activation, with age the strongest predictor and modest contributions from sleep and physical activity. These findings establish TD-fNIRS dlPFC activation as a longitudinally stable, behaviourally relevant functional neural marker for scalable tracking of modifiable risk.