Historically, aperiodic neural activity (i.e., the 1/f exponent and offset of electroencephalogram (EEG) power spectra) has been treated as neural noise. However, recent work has highlighted associations among aperiodic activity, aging, cognition, and psychopathology. While depression has been associated with cognitive deficits and dysregulation of excitatory/inhibitory neural mechanisms linked to aperiodic activity, to date, few studies have compared aperiodic activity in adults with and without depression, and none have evaluated aperiodic activity as a function of depressive chronicity. Here, individuals with depression (n=72) and healthy controls (n=34) completed a resting state EEG recording from which aperiodic activity estimates were extracted. We show significant group differences in aperiodic exponent and offset across central and posterior regions, such that, as hypothesized, these parameters were lower for depressed individuals relative to controls. Follow-up analyses clarified that this effect was driven by the number of lifetime depressive episodes experienced. Future research is needed to understand how depression heterogeneity impacts aperiodic activity.

Long COVID has been an important health concern in children and adolescents, yet factors associated with its development remain incompletely understood. Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are widely prescribed for pediatric neuropsychiatric conditions and may influence immune and autonomic pathways involved in postinfectious symptoms. Here we show associations between SSRI/SNRI use and long coronavirus disease (COVID)-related outcomes in a retrospective cohort of 110,955 children and adolescents with pre-existing neuropsychiatric conditions across 37 US health systems participating in the National Institutes of Health Researching COVID to Enhance Recovery consortium. SSRI/SNRI use was not associated with clinician-recorded long COVID diagnosis but showed heterogeneous associations with individual symptoms. Lower risks were observed for some symptoms, including fever, chills and hair loss, whereas higher risks were observed for neurological and systemic outcomes, including postural orthostatic tachycardia syndrome, cognitive dysfunction and fatigue. These findings suggest that antidepressant exposure may be associated with differing post-COVID symptom patterns in youth and warrant further investigation.

Research and clinical practice on childhood maltreatment largely rely on retrospective self-reports. However, self-reports are often considered unreliable due to concerns about memory biases and shifting subjective appraisals. Here, in a meta-analysis of 49 studies including n = 38,332 individuals followed for an average of 2.4 years (range = 2 months to 12 years), we found that retrospective self-reports of maltreatment are overall highly stable (r = 0.79). However, stability was lower in population-representative samples than in clinical or convenience samples, for neglect compared with abuse, and in children compared with adults. In children, but not adults, stability declined with longer follow-up. These findings challenge the view that retrospective self-reports are inherently unstable, although further research is needed to investigate long-term stability. Reshaping trauma-related appraisals may require deliberate intervention and might be most effective during childhood when memories appear more malleable.

Depression has been linked to reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks. However, recent meta-analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. We leveraged 23,417 participants across 6 population datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/cortical surface area associated with depression in the frontal cortex, anterior cingulate and insula, confirming previous studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions and significant depression correlates in somatomotor and visual regions. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression symptoms.

The long-term consequences of attention deficit hyperactivity disorder (ADHD) in females remain underrecognized both in research and clinical practice. Multimorbidity (two or more long-term conditions) is increasingly viewed as a key outcome yet its prevalence, complexity and sociodemographic determinants are understudied in this group. Here, in this longitudinal cohort study, we examined the combined influence of childhood socioeconomic deprivation and ADHD diagnosis on multimorbidity risk in women in early adulthood (that is, aged 18-32 years) and used latent class analysis to explore clustering patterns. Three findings emerged: females with childhood ADHD had a significantly higher risk of adult multimorbidity; childhood ADHD and socioeconomic deprivation increased this risk both in isolation and synergistically, with 39% of the multimorbidity burden attributable to their interaction; and distinct multimorbidity clusters emerged, with the most adverse marked by psychiatric complexity. These results highlight girls with ADHD from disadvantaged backgrounds as a high-risk group requiring earlier, integrated care.

The biological mechanisms underlying major neuropsychiatric disorders remain largely elusive. Given the frequent association of immune dysregulation with these conditions, we used blood-derived multi-omics data from 1,274 healthy adolescents in the IMAGEN cohort to identify transdiagnostic biomarkers and mechanisms that could inform diagnosis and treatment. We first conducted genome-wide analyses to identify single nucleotide polymorphisms associated with DNA methylation and gene expression, with findings replicated in external datasets. These quantitative trait loci were further explored through Mendelian randomization analyses across 6 neuropsychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia and insomnia), leading to the identification of 73 putatively causal CpG sites and 62 genes that were either unique to individual disorders or, as in the case of MRPL2, shared among the disorders. Notably, the identified genes were significantly enriched in pathways linked to both psychiatric and autoimmune diseases, suggesting a shared genomic architecture between autoimmune and neuropsychiatric disorders. Two-step Mendelian randomization and colocalization analyses revealed potential transdiagnostic regulatory pathways, in which the expression of three genes (MAD1L1, MRPL2 and HLA-DRB1) mediated the effects of CpG methylation on schizophrenia and insomnia. Specifically, DNA methylation at cg06770790 repressed MRPL2, which was putatively causal for insomnia (β = -0.38, P = 1.29 × 10-4) and schizophrenia (β = -0.38, P = 1 × 10-4). Conversely, increased expression of MAD1L1 and HLA-DRB1, driven by methylation at several CpG sites, was potentially causal for schizophrenia. Our findings highlight key molecular mechanisms and genes implicated in neuropsychiatric disorders, offering promising new targets for therapeutic intervention.

Although adverse childhood experiences (ACEs) increase risk for mental illness at the population level, existing ACEs screens are less helpful in forecasting individual outcomes, suggesting they may not capture significant elements of childhood adversity. We have previously identified unpredictable parental and household experiences as an ACE that portends poorer cognitive and mental health. However, the contribution of unpredictability to established ACEs in real-world settings is unknown. Here, leveraging existing ACEs screening in California, we added the five-item Questionnaire on Unpredictability in Childhood (QUIC-5) in 19 pediatric clinics spanning broad sociodemographic constituencies and compared in ~30,000 children the link of each screen with mental health diagnoses. Scores on either the ACEs or QUIC-5 associated with probabilities of depression, externalizing symptoms, sleep disorders, anxiety and somatic symptoms. Each screen provided unique contributions and combining them often doubled the strength of associations. For depression and sleep disorders, the QUIC-5 identified vulnerable individuals missed by ACEs screen, improving risk detection and facilitating future interventions.

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions. Inspired by pan-cancer and multi-organ research, we introduce the concept of "pan-disease" to investigate the heterogeneity and shared etiology in brain, eye, and heart diseases. Leveraging individual-level data from 129,340 participants, as well as summary-level data, curated from the MULTI consortium, we applied a weakly-supervised deep learning model (Surreal-GAN) to multi-organ imaging, genetic, proteomic, and RNA-seq data, identifying 11 AI-derived biomarkers, called Multi-organ AI Endophenotypes (MAEs), for the brain (Brain 1-6), eye (Eye 1-3), and heart (Heart 1-2), respectively. We found Brain 3 to be a risk factor for Alzheimer's disease (AD) progression and mortality, whereas Brain 5 was protective against AD progression. Crucially, in data from an anti-amyloid AD drug (solanezumab), heterogeneity in cognitive decline trajectories was observed across treatment groups. At week 240, patients with lower brain 1-3 expression had slower cognitive decline, whereas patients with higher expression had faster cognitive decline. A multi-layer causal pathway pinpointed Brain 1 as a mediational endophenotype linking the FLRT2 protein to migraine, exemplifying novel therapeutic targets and pathways. Additionally, genes associated with Eye 1 and Eye 3 were enriched in cancer drug-related gene sets with causal links to specific cancer types and proteins. Finally, Heart 1 and Heart 2 had the highest mortality risk and unique medication history profiles, with Heart 1 showing favorable responses to antihypertensive medications and Heart 2 to digoxin treatment. The 11 MAEs provide novel AI dimensional representations for precision medicine and highlight the potential of AI-driven patient stratification for disease risk monitoring, clinical trials, and drug discovery.

Quantum error correction (QEC) will likely be required to realize the full potential of quantum computing, but comes with daunting hardware overheads and demands low gate errors on the physical qubits1-4. These requirements can be eased by engineering qubits with a strong error hierarchy, in which the most common noise channels are also the easiest to correct. Erasure qubits can achieve this when detectable leakage errors out of the computational subspace dominate over the residual Pauli errors5-11, resulting in higher thresholds and improved scaling with code distance5,12,13. In practice, these advantages come to fruition only if the error hierarchy is preserved as much as possible throughout all gates and operations. Here we design and realize a two-qubit entangling gate for dual-rail cavity qubits, a type of erasure qubit encoded in a pair of superconducting microwave cavities7. Our experimental demonstration confirms that the error hierarchy is largely preserved during the gate. The gate is fast (about 500 ns duration) and shows low erasure rates of approximately 0.5% per gate, remaining Pauli errors below 0.1%, and a strong bias towards dephasing errors, in which bit-flips are practically non-existent at the 10-6 level. These results enable a faster path to error-corrected systems that rapidly suppress errors as they scale; a claim we support with our detailed surface code simulations.

Beneficial and maladaptive opioid effects are difficult to dissociate1-3, partly because dopamine signalling contributes to both these effect types4-13. Here we show that associative opioid-reward learning can be blocked even under conditions that elevate dopamine in the nucleus accumbens. We developed naloxoneDART, a cell-type-specific analogue of the clinical opioid receptor antagonist naloxone14,15, and delivered it to genetically defined accumbal cholinergic interneurons, selectively rendering these cells morphine-insensitive. Acquisition of morphine conditioned place preference was abolished in a target-engagement-dependent manner, without evidence of contextual or locomotor impairment: saline habituation was enhanced between sessions and unchanged within sessions, whereas morphine-evoked hyperlocomotion, sensitization and acute analgesia remained intact. Microdialysis revealed that cholinergic interneuron-specific naloxoneDART prevented morphine-induced acetylcholine reductions without detectably altering dopamine increases in the accumbens. These findings identify a cholinergic gate for associative opioid-reward learning, support an emerging dopamine-acetylcholine plasticity theory16,17, and motivate exploration of opioid-cholinergic strategies that may preserve acute analgesia while limiting early associative reward learning18-25.

Commercially relevant quantum computers will require large numbers of high-performing qubits that can be manufactured, integrated and controlled at scale. Silicon exchange-only qubits1-10 are a strong candidate modality owing to their control-signal simplicity and compatibility with advanced semiconductor manufacturing11-13, but questions remain around the achievability of sufficiently low noise and a scalable control and wiring solution13-19. Here we introduce a quantum processing unit composed of a custom-designed cryogenic complementary metal-oxide-semiconductor (CMOS) controller, a high-density superconducting ribbon cable and a low-noise exchange-only qubit device. The quantum chip features a 3-rail array of 54 exchange-coupled quantum dots, configurable to host up to 18 exchange-only qubits. We integrate and use these components to demonstrate qubit performance for both single-qubit and entangling operations that advances the exchange-only state of the art7,8,10 by an order of magnitude. We further validate this system by implementing a distance-5 repetition code20 and a distance-2 quantum error-detecting code21-26 and then make detailed comparisons with simulations. Our work facilitates the development of future utility-scale quantum computers with manageable operational and capital requirements.

Recent advances in coherent spin shuttling have made sparse semiconductor spin-qubit arrays an appealing solid-state platform to realize quantum processors1-7. The dynamic and long-range connectivity enabled by shuttling is also essential for many quantum error-correction schemes8-10. Here we demonstrate a silicon spin-qubit device comprising a shuttling bus for coherently transporting qubits that can interact at four isolated locations that we call bus stops. We dynamically populate the array and tune all single- and two-qubit operations using shuttling and quantum non-demolition spin measurements, without access to charge sensing in most of the device. We achieve universal control of the effective five-qubit processor and select the connectivity required to form a surface-code stabilizer plaquette that supports X- and Z-type parity checks up to weight four. We use the parity checks to generate multi-qubit entanglement between all qubit combinations in the array and report the genuine entanglement of a five-qubit Greenberger-Horne-Zeilinger state, constituting one of the largest such states constructed with gate-defined semiconductor spins. The protocols developed here lay the groundwork for modular calibration and operation of sparse spin-qubit arrays, and we highlight the feasibility of near-term quantum error-correction experiments with mobile spin qubits.

When cooling towards a ferroelectric phase transition, collective atomic motions (phonons) slow down (soften) until a static atomic displacement pattern forms, giving rise to spontaneous polarization throughout the material1. However, in quantum paraelectrics such as strontium titanate (SrTiO3), long-range ferroelectric order does not develop at low temperatures due to persistent quantum fluctuations of ionic positions2,3. In SrTiO3, quantum paraelectricity emerges below Tq ≈ 40 K refs. 4-6 and is preceded by anomalous phonon dynamics: a transverse acoustic phonon mode partially softens at a finite wavevector, hinting at a modulated state at the nanoscale7-12. The precise real-space structure of SrTiO3 at low temperature, however, has remained unresolved despite decades of study. Here we directly image the low-temperature polar structure of a SrTiO3 lamella using cryogenic scanning transmission electron microscopy down to 20 K. High-resolution imaging reveals a spatially fluctuating landscape of nanoscale domains. Below about 105 K, short-range polar domains initially self-organize into a periodic structure extending over tens of nanometres; however, upon entering the quantum paraelectric regime below Tq, the process reverses and the periodically ordered polar nanodomains fragment into smaller clusters. Quantum paraelectricity in SrTiO3 underlies remarkable properties, including large dielectric permittivity13,14, proximity to ferroelectricity15,16, multiferroicity17 and unconventional superconductivity18-20. Our visualizations suggest that these phenomena may be linked to complex ordering and disordering of polar nanodomains at low temperature.

Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure-function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline, R2 = 0.31; placebo, R2 = 0.22). Validation in an independent escitalopram-medicated MDD cohort confirms the biomarker's generalizability (P = 0.01) and suggests an overlap of psychopharmacological signatures across selective serotonin reuptake inhibitors. Our models highlight the right precuneus as a common key region for both sertraline and placebo responses, with the right middle frontal gyrus and left fusiform gyrus specific to sertraline and the left inferior and middle frontal gyri to placebo. We also find that structural connectivity is more predictive of sertraline response, while functional connectivity better predicts placebo response. The framework further decomposes the overall predictive patterns into three constitutive network constellations (default-mode regulatory, affective and sensory processing), which exhibit distinct generalizable structure-function covariation and treatment-specific association with personality traits and behavioral/cognitive profiles. These findings provide unique insights to the structure-function covariation in patients with MDD, its association to the heterogeneity in antidepressant response and the dissection of the intricate MDD neuropsychopharmacology, paving the way for precision medicine and development of more targeted antidepressant therapeutics. Clinicaltrials.gov registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT01407094.

Games have long been a microcosm for studying planning and reasoning in both natural and artificial intelligence, often focusing on expert-level or even super-human play1-6. But real life also pushes human intelligence along a different frontier, requiring people to flexibly navigate decision-making problems that they have never thought about before. Here we use novice gameplay to study how people reason about new problem settings. Through a series of large-scale behavioural studies with over 1,000 participants and 121 two-player strategic board games (almost all novel to our participants), we show that people are systematic and adaptively rational in how they play a game for the first time or evaluate a game (for example, how fair or how fun it is likely to be) before they have played it even once. We explain these capacities via a computational cognitive model that we call the 'Intuitive Gamer': a model based on mechanisms of fast and flat (depth-limited) goal-directed probabilistic simulation. Our work offers insights into how people rapidly evaluate, act and make suggestions when encountering novel problems, and could inform the design of more flexible and human-like artificial intelligence systems that can determine not just how to solve new tasks but also whether a task is worth thinking about at all.

Mineral desert dust is a major contributor to total atmospheric particulate matter1. Desert dust outbreaks degrade air quality and can pose adverse health effects2, including asthma exacerbation3 and increased mortality4. At some European locations, there has been a rise in the intensity and frequency of transported dust outbreaks from deserts in recent decades5-9. However, it remains unclear whether this increase is consistent across Europe and whether desertification and aridity or shifts in atmospheric circulation are the main drivers behind this rise. Here we compile a database of daily dust metal concentrations from European sites, establishing robust elemental ratios for transported dust. Using this database, we develop a machine learning model to estimate daily PM10 (particulate matter smaller than 10 μm) dust concentrations from 2012 to 2021, ranging from 2.09 ± 1.05 μg m-3 across northern and central Europe to 5.28 ± 2.65 μg m-3 across the south. In southern Europe, residents are exposed to transported dust events averaging 9.68 ± 4.85 μg m-3, linked to a 0.67 ± 0.02% rise in daily mortality. Intensified dust intrusions over the past decade are linked to shifts in atmospheric circulation. Data from an Alpine ice core record shows a 110% increase in dust concentrations since pre-industrial times, mostly associated with North African desertification. As climate change accelerates land degradation and affects weather patterns, worsening dust pollution may pose increasing risks to public health and air quality goals.

Engineering materials that control quantum many-body dynamics remains challenging, as multiparticle interactions typically produce complex emergent behaviour that is difficult to predict1,2. Here we introduce quantum statistical plasmonic metacrystals, structures in which the multiparticle dynamics mediated by optical near fields produce forbidden quantum statistical bands that enable selective transmission of different types of light. This functionality arises from a plasmonic structure composed of nanoantennas acting as meta-atoms3. Multiphoton fields with statistics within the allowed bands propagate without distortion, whereas fields in forbidden bands are suppressed or driven towards the nearest accessible statistical state. We show that these bands are determined by the geometry and collective arrangement of the meta-atoms, providing a deterministic route to engineering quantum statistical transport. This platform establishes a room-temperature quantum material intrinsically sensitive to the quantum coherence of many-body photonic systems, enabling their robust manipulation and transport4. Our results have implications for coherence-sensitive photonic materials for energy harvesting and scalable many-body quantum technologies2,5.

Stacked graphene oxide membranes (GOMs) show exceptional capabilities for high-throughput sieving of water, ions and molecules, offering transformative potential in environmental and energy sectors1-5. However, achieving GOMs with subnanometre interlayer spacing and subangstrom tunability while maintaining their structural robustness for rapid and selective ion transport remains a big challenge6-8. Here we present polydopamine-pillared composite GOMs with tunable and stable interlayer spacing, featuring controllable interlayer spacing down to 5.9 Å in the dry state, and capable of sieving hydrated rubidium (Rb+) and potassium (K+) ions differing in size by less than 0.1 Å in aqueous environments, achieving an Rb+/K+ separation factor of 5,320. These composite GOMs were fabricated by using the dopamine assembly and reaction timescale separation method. Specifically, the GOM fabrication capitalizes on the fact that nanoconfined water has a lower freezing temperature than that of bulk water, such that the interlayer spacing is regulated by the rapid assembly of dopamines into nanopillars, driven by nanoconfined liquid water while the surrounding is in bulk ice. The assembly process can be halted anytime by further lowering the temperature to tune and fix the interlayer spacing. Thereafter, the GOM is rigidified through the slower chemical reactions, including polymerization of dopamine molecules and covalent bonding at specific oxygen-containing sites on the graphene oxide surface while retaining ample graphene subnanochannels for high-flux transportation. The GOMs deliver continuous freshwater production for 30 days at a water permeance of 67.9 l m-2 h-1 bar-1, 1-2 orders of magnitude higher than conventional membranes9.

European wetlands store large carbon reserves1, but centuries of land use have eroded carbon stocks and biodiversity2. The European Union (EU) Nature Restoration Law (NRL)3 requires at least 30% of wetland ecosystems not in 'good condition' to be restored by 2030, yet spatially consistent information on wetland types and condition remains scarce. Using 10-m satellite imagery and machine learning, we map six seminatural open wetland types and land-use disturbance across 38 European countries. Wetlands are highly fragmented, with an estimated 27-33% of wetland area occurring in map-defined patches <25 ha and 7-11% in patches <1 ha, exposing many small sites missed by coarser products. We estimate that human activities affect 20.4 ± 3.4% of wetland areas (95% confidence interval), with inland wetland types most affected, and up to 5 Gt CO2-eq of soil carbon potentially lost relative to an undisturbed baseline. Translating disturbed area into NRL restoration targets, we find that several countries' pledges are broadly consistent with our 2030 estimates, whereas others lack quantified commitments despite substantial candidate areas identified by our maps. The resulting standardized, high-resolution products provide an EU-wide baseline tailored to the NRL and a reproducible template for linking satellite mapping to restoration targets, supporting progress tracking.

Extreme space weather events on Earth occur during intervals of strong solar wind driving1. The solar wind drives plasma convection and currents in the near-Earth space environment2. For low values of the driver, the Earth's response is linear, estimated by parameters such as the polar cap index based on ground magnetometer activity3. Curiously, for extreme solar wind driving, the Earth's response appears not to increase beyond a saturation limit4. Theorists have advanced a host of explanations for this saturation effect, but there is no consensus5. Here we demonstrate that this saturation is a manifestation of the regression to the mean effect6 arising from random uncertainty in the timing and magnitude of solar wind measurements. Our results reveal that data analysis underpinning the saturation theories is nonlinearly biased, thereby challenging the validity of the theories. Correcting for the uncertainties reveals that the Earth's response to solar wind driving is linear throughout, and that the impact of extreme geomagnetic storms can be twice as large as previously thought. We show that regression to the mean is a fundamental property of the relationship between measurement and the truth, where the truth corresponding to the measurement is closer to the mean. This effect is particularly pronounced for uncertain measurements of extreme values and is likely to manifest across various fields, from extreme climate studies to chronic medical pain.