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

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.

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.

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.

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.

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.

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.

Objective: The COVID-19 pandemic markedly altered social activity patterns, healthcare utilization, and the epidemiology of infectious diseases. However, its long-term impact on emergency otolaryngologic conditions remains incompletely understood. This study investigated long-term trends in emergency otolaryngologic conditions before, during, and after the COVID-19 pandemic using comprehensive data from a large urban emergency clinic in Osaka, Japan. Methods: All new otolaryngologic outpatients who visited the Chuo Emergency Medical Clinic (CEMC) in Osaka City between 2019 and 2024were retrospectively analyzed. Annual trends in absolute numbers and relative proportions of emergency otolaryngologic conditions were examined by anatomical region and disease category, using 2019 as the pre-pandemic baseline. Results: A total of 99,324 new otolaryngologic outpatients were analyzed. Overall emergency visits declined sharply to approximately half of baseline in 2020, followed by a gradual but incomplete recovery toward pre-pandemic levels by 2024. Most anatomical categories declined to 45-61% of baseline in 2020 and exhibited gradual yet incomplete recovery through 2023; in stark contrast, laryngeal conditions diverged sharply, surging beyond pre-pandemic levels after 2022. Acute infectious otorhinolaryngologic diseases fell to 23-50% of baseline in 2020 and showed variable recovery (69-103%) by 2024. Notably, laryngitis exceeded the baseline, reaching 132% in 2023, whereas epiglottic edema exhibited only a transient increase approaching the baseline in 2021. Non-infectious emergency conditions generally showed only a marginal decrease in 2020 and remained relatively stable throughout the study period, except for sudden sensorineural hearing loss (SSNHL), which dropped sharply to 39% of the baseline in 2020 and remained persistently reduced through 2024. Traumatic emergencies declined variably to 53-81% of the baseline in 2020, followed by an incomplete recovery, reaching only 55-69% by 2024. Conclusion: Emergency otolaryngologic conditions demonstrated heterogeneous recovery trajectories following the COVID-19 pandemic. While most infectious and traumatic conditions gradually but incompletely normalized, laryngeal conditions showed a distinct post-pandemic surge, and SSNHL remained persistently suppressed. These findings reveal heterogeneous, condition-specific recovery trajectories that reflect both genuine shifts in community pathogen burden, true traumatic incidence, and persistent alterations in healthcare-seeking behaviors, insights essential for resource allocation during future public health emergencies.

Motor unit (MU) decomposition using high-density surface electromyography (HD-sEMG) has been widely used to characterize MU behavior in neurophysiology and to build neural-machine interfaces for wearable robots. Recently, many open-source software tools for MU decomposition have been made available on GitHub, which could reduce the effort of researchers in the field. However, the consistency among these open-source tools has never been studied, making researchers hesitate to use them. In this study, we collected 7 open-source software tools on GitHub and applied them to decompose MUs from an open-source HD-sEMG dataset (including 11 isometric contraction trials) to investigate the consistency among these tools. To create a comprehensive MU pool for reference, we combined all unique MUs identified by seven tools, visually inspected and removed bad MUs, and manually edited all remaining MU spike trains. Across 7 tools for 11 trials, the number of identified MUs ranges from 167 to 736. The number of valid MUs after expert inspection ranges from 29 to 210, which is 10% to 72% of the reference pool. The rate of agreement between the raw MUSTs and the manually edited MUSTs ranges from 0.86 to 0.94, and the averaged number of edits per MU to correct misalignments ranges from 14 to 39. The results show inconsistency in the implementation and procedures of each tool, which results in an inconsistent number of identified MUs and valid MUs (29 vs 210). In general, a substantial amount of effort is required to process the raw MUSTs from each tool to conduct further research analysis. This study provided a guideline for using open-source software tools for MU decomposition and indicated that it would be beneficial to develop tools to automatically edit the MUSTs.

Objective: To evaluate the efficacy, engagement, and usability of Tune Out, an unguided, self-paced online tinnitus management program, for reducing tinnitus severity in adults with tinnitus. Design: A two-arm, parallel-group randomised controlled trial was conducted with Australian adults reporting diagnosed or self-reported tinnitus. Participants were randomised to immediate access to Tune Out or a waitlist control group. Outcomes were assessed at baseline, 6 weeks, and 12 weeks. The primary outcome was tinnitus severity measured using the Tinnitus Functional Index (TFI). Secondary outcomes included tinnitus handicap, psychological symptoms, program engagement, self-efficacy, and usability. Results: Eighty-eight participants were randomised: 43 to the intervention group and 45 to the waitlist control group. The primary outcome analysis included 63 participants at 12 weeks. A significant Group x Time interaction was observed for TFI total score, indicating greater reductions in tinnitus severity over time in the intervention group compared with waitlist control, F(2, 102.57) = 5.95, p = .004, partial 2= .104. Significant effects were also observed for tinnitus handicap, F(2, 106.76) = 4.12, p = .019, partial 2 = .072. Effects on psychological symptoms were less consistent, although anxiety showed a significant Group x Time interaction, F(2, 116.85) = 3.63, p = .030, partial 2 = .059. At 12 weeks, 23.1% of intervention participants achieved a clinically meaningful reduction in tinnitus severity compared with 5.4% of controls. Program use was highly variable, with a median use of 1.10 hours, and 25.6% of intervention participants recording no use. Usability ratings were favourable among respondents, with a mean System Usability Scale score of 73.13. Conclusions: Tune Out demonstrated preliminary efficacy for reducing tinnitus severity and tinnitus handicap compared with waitlist control. Effects on broader psychological symptoms were less consistent. Although usability was rated positively, low and variable engagement highlights the need for strategies to support uptake and sustained use in unguided digital tinnitus interventions.

Natural physical environments constantly fluctuate across multiple timescales, often following a scale-free (1/f ) pattern where = 0.5 governs the fractional adaptation dynamics (Drew and Abbott 2006, Lundstrom et al. 2008). Here, we demonstrate how a multi-timescale sensory model successfully tracks these long-term trends to maintain stable encoding. Using an event-based Generalized Leaky Integrate-and-Fire (GLIF) paradigm, we found that a fast-adapting, single-exponential model with a short time constant{tau} [≤] 31.6 ms quickly crashes into complete refractory saturation when faced with large, low-frequency environmental shifts. In contrast, introducing a deep fractional memory tail of 1000.0 ms acts as an automated, high-pass balancing mechanism that continuously tracks and subtracts slow environmental variance. This predictive balancing prevents sensory collapse, anchors the mean firing rate to a steady homeostatic baseline, and maximizes coding efficiency for rapid, localized signals. Our results show that while a simple single-pole exponential model fails to retain history, a parallel bank of physiological relaxation processes converging on a target fractional profile t-0.5 provides the necessary historical memory to safely navigate natural stimulus fluctuations. Comfortingly, even a simplified three-pole approximation captures the bulk of this homeostatic benefit, making efficient fractional adaptation biologically viable at the sensory periphery without requiring infinite historical storage.

BackgroundHeterochromatic long arm of mouse Y chromosome harbors the multicopy species-specific sequences Ssty, Sly, Asty and Orly that are transcribed in testis and have known functions in male fertility. Of these Ssty and Sly encode proteins - yet all the transcripts are not translated. To investigate the roles of these Y-heterochromatic transcripts further, we analyzed them. MethodsMice with 2/3rd deletion of the Y-chromosome (XYRIIIqdel) and its wild type (XYRIII) were used in this study. Bioinformatic approaches, small RNA northern blots, Electrophoretic Mobility Shift Assays, Luciferase reporter assays, dPCR analysis, RT-qPCR assays and western blotting techniques were used to identify piRNAs that regulate autosomal genes. ResultsWe demonstrate that the multicopy gene families from mouse Y-long arm generate piRNAs predominantly in testis. We observed sequences homologous to these piRNAs in the UTRs of a few autosomal genes, which are differentially expressed in the sperms of XYRIIIqdel mice. Furthermore, the Endogenous Retrovirus Element (ERV) LTR, found in the Orly1 transcript identified piRNAs in the database, showed homology to UTRs and associated genomic regions of a few autosomal genes. Orly1 showed a reduction in genomic copy number by digital PCR in XYRIIIqdel mice. One of the four autosomal genes containing the ERV segment in their UTRs, showed a differential testicular protein expression in the mutant mice. ConclusionsThus, we further elucidate that different classes of repeats from Y-chromosome regulate autosomal gene expression via piRNAs. Besides, this study also identified novel roles for a Y-derived ERV in autosomal gene regulation in testis.

Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, (i) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; (ii) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify three classes of the main insects visiting sunflower on these images (non-Bombus bees, bumble bees, lepidopterans); (iii) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; (iv) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of {+/-}10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.

Background: Alcohol-involved drinking episodes vary in whether they involve hazardous alcohol consumption alone, near-miss sexual risk, or sexual risk behavior, but the within-event mechanisms underlying this variability remain unclear. Methods: Guided by syndemic theory, we conducted a qualitative event-level analysis using modified grounded theory among adults in the San Francisco Bay Area who reported hazardous alcohol consumption, defined as an Alcohol Use Disorder Identification Test score [≥]16. In-depth interviews elicited narratives of recent heavy drinking episodes and yielded 64 discrete drinking events across 22 participants. We focused on 35 events with evidence of within-event interaction between biopsychosocial and contextual factors. Using constant comparison, we identified escalation pathways, characterized interruption, and examined how events diverge into three outcomes: hazardous alcohol consumption only, hazardous alcohol consumption with near-miss sexual risk (when risk was plausible but not enacted), and hazardous alcohol consumption with sexual risk behavior. Results: Two primary escalation pathways emerged. Dose-driven escalation involved cumulative alcohol or substance exposure that progressively impaired awareness and self-regulation. Meaning-driven escalation involved prioritizing connection, intimacy, or belonging despite awareness of risk. Time-driven continuation extended exposure across contexts and amplified both pathways. Hazardous alcohol consumption-only events more often followed dose-driven pathways, whereas events involving sexual risk behavior more often followed meaning-driven pathways. Near-miss events occurred across both pathways and illustrated how interruption before the escalation constraint point, when the capacity to modify behavior became reduced, could redirect escalation before sexual risk behavior occurred. Across events with similar levels of intoxication narratives, outcomes diverged according to when the interruption occurred and whether it altered escalation. Conclusion: Hazardous drinking episodes diverge into different outcomes based on escalation pathways and the timing and effectiveness of interruption. Early and effective interruption before the escalation constraint point may represent a key target for harm-reduction strategies to prevent progression to sexual risk behavior.

Acoustic cues such as pitch and spatial location allow listeners to attend to a target speaker and ignore competing talkers, aiding speech recognition in background noise. Diminished ability to utilize acoustic cues for speech stream segregation may thus contribute to older adults' challenges hearing in noise. Adults aged 18-74 completed a speech-in-speech identification task with three conditions containing 1) only pitch cues (fundamental frequency), 2) only spatial cues (interaural time differences; ITDs), and 3) both pitch and spatial cues for segregating a target talker from competing talkers. Hearing thresholds at standard and extended high frequencies (EHFs), auditory brainstem responses (ABRs), and digit span scores were acquired to examine the influence of sensory and cognitive factors on use of each acoustic cue for speech-in-speech recognition. Significant differences were observed between cue condition scores indicating that use of the available cue(s) drove performance. ABR metrics were not a significant predictor but digit span scores significantly predicted scores on all three cue conditions. Working memory abilities therefore set a baseline for participants' speech-in-speech recognition regardless of the acoustic content. Hearing thresholds at standard frequencies significantly predicted scores on the Pitch condition. EHF hearing thresholds better predicted Spatial and Both Cue condition performance, suggesting that EHF thresholds represent auditory processing important for coding ITDs. Age group analysis revealed that older adults (aged 40+) performed significantly more poorly on all cue conditions of the speech-in-speech recognition task relative to younger adults. Age-related changes in auditory sensory processing may therefore impair older adults' speech-in-noise perception by reducing their ability to use acoustic cues for segregating target and competing speech.

Learning new information in the presence of distracters and changing conditions requires the ability to adapt. In the brain, this adaptive capability has been linked to dynamic interactions between attention and working memory, which enable the selective filtering of irrelevant input while preserving behaviorally relevant information. Specific neural oscillations have been implicated in this process. Here, we introduce a phenomenological data-driven framework for oscillatory network modeling that learns condition-dependent coupling laws directly from neural recordings and enables inference of condition-dependent directed pathways. We apply our approach to magnetoen-cephalography (MEG) data collected while participants performed a working-memory task with and without distracters. Recall dynamics in the non-distracter condition are first modeled using a linear oscillatory network in which each region of interest is represented by two alpha-band harmonic oscillators. We use universal differential equations (UDE), an extension of neural differential equations, to capture distracter-induced changes in coupling laws. Symbolic regression is then used to interpret the modifications identified by UDE as nonlinear functions, and an additional method is proposed to identify the directed pathway from the newly emerging nonlinear terms in the dynamics of brain regions of interest. Despite inter-subject variability, working memory recall data from all four participants examined under distraction showed the emergence of a pathway from the dorsolateral prefrontal cortex (dlPFC) to the primary visual cortex (V1). This finding is consistent with the established role of the dlPFC in cognitive control and suggests that distracter processing recruits a directed interaction from prefrontal to visual regions. More broadly, our results illustrate that combining linear models whose parameters are learned from the data with universal differential equations augmented by interpretability methods enables the identification of condition-dependent coupling laws, their representation as interpretable mathematical functions, and the discovery of candidate directed pathways underlying adaptive changes in oscillatory networks without requiring strong prior assumptions about the underlying mechanisms.

Background: Individuals with attention-deficit hyperactivity disorder (ADHD) are at increased risk for cannabis misuse, with increasing prevalence among young adults. Existing evidence suggests that cannabis can have therapeutic effects on ADHD symptoms, and continued use may be partly driven by perceived improvements in symptom-related deficits. To investigate the neural evidence for these associations, we integrated functional neuroimaging and Allen Human Brain Atlas transcriptomic data to assess neural correlates of ADHD in regions targeted by cannabinoids as predictors of cannabis use. We hypothesized that greater ADHD symptoms would lead to higher cannabis use frequency through associations of ADHD symptoms with functional deficits in cannabinoid receptor type 1 (CB1R; encoded by the CNR1 gene) expressing brain regions. Methods: We tested 466 college students (ages 18-19) with varying ADHD symptom severity and cannabis use, self-reported at baseline and three yearly-follow up questionnaires. ADHD-related neural deficits were tested in a subset of 144 participants using an fMRI stop-signal task at baseline. Growth mixture modelling categorized participants with similar cannabis use into three latent classes. The covariance between the CNR1 gene expression map and differences in stop-signal task activation were tested as a mediator linking ADHD symptoms and cannabis use. Results: Greater ADHD symptoms significantly predicted reduced activation within CNR1-expressing regions, which predicted higher-use cannabis class membership. Conclusions: Our results add support for the self-medication hypothesis for higher rates of cannabis use among individuals with greater ADHD symptoms, which may be mechanistically linked through CB1R-enriched attention and inhibitory networks, highlighting neural targets for prevention and treatment.

This step-by-step protocol provides a cross-species, non-surgical approach that enables prenatal gene delivery to the developing nervous system in rats and marmosets. Under transabdominal ultrasound guidance, intracerebroventricular injection of recombinant adeno-associated virus vectors into the fetal brain achieves robust and long-term transduction from prenatal stages into adulthood. This approach can be adapted to other species and target sites outside nervous system, enabling safe and selective intrauterine manipulation and the generation of diverse experimental models for basic and preclinical research. For complete details on the use and execution of this protocol, please refer to Ribeiro Gomes et al (2026)1. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/737050v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@696364org.highwire.dtl.DTLVardef@fc3c7forg.highwire.dtl.DTLVardef@1e7c7caorg.highwire.dtl.DTLVardef@1edcef0_HPS_FORMAT_FIGEXP M_FIG C_FIG Before you beginExperimental procedures during gestation allow researchers to study developmental processes, including how manipulations of the fetus and its intrauterine environment influence biological outcomes. Ultrasound imaging guidance greatly facilitates such interventions by providing safe and targeted access to fetal compartments, including for prenatal gene delivery to developing neural cell populations. Critically, delivery of recombinant adeno-associated viruses (rAAVs) into the cerebrospinal fluid (CSF) of developing animals enables widespread gene transfer across the brain. The efficiency and distribution of transduction are strongly influenced by developmental stage, making the timing of delivery an important experimental variable. In altricial species such as mice, major developmental processes, including cortical lamination and the establishment of long-range connections, begin prenatally but continue throughout early postnatal life. In primates, however, development is more advanced at birth, and many equivalent developmental events are shifted to the prenatal period. Consequently, developmental stages that can be targeted postnatally in mice require prenatal access in primates. Here, we present a step-by-step protocol for ultrasound-guided fetal intracerebroventricular viral injection (FIVI) of rAAV in marmosets (Callithrix jacchus) and rats (Rattus norvegicus). The procedure was initially developed and optimized in rats before being translated to marmosets, small New World primates that share key developmental, anatomical, and functional characteristics with humans. Together, these models illustrate the cross-species applicability of the approach, while providing gene delivery strategies for both a genetically tractable rodent model and a translationally relevant nonhuman primate. FIVI enables broad gene transfer and stable, long-term transgene expression in wild type animals, facilitating the generation of complementary quasi-transgenic models for research and translational applications from prenatal development through adulthood.