Distributed arrays of wireless neural interfacing chips with 1–2 channels each, known as “neural dust,” could enhance brain machine interfaces (BMIs) by removing wired connections through the scalp and increasing biocompatibility with their submillimeter size. Although several neural dust designs have emerged, currently reported procedures for implanting them in batches place the chips directly inside the brain, which can damage or displace large numbers of neurons. Therefore, a procedure for safely implanting neural dust in batches such that only ultrasmall microwire elements enter the brain is needed. Here, we demonstrate the feasibility of implanting batches of wireless motes that rest on the cortical surface and reach 1 mm brain depths via penetrating carbon fiber electrodes (6.8–8.4 μm diameter) without employing disruptive insertion shuttles. To simulate their implantation, we assembled over 230 mechanically-equivalent carbon fiber motes and affixed them to insertion tools with polyethylene glycol (PEG), a quickly dissolvable and biocompatible material. Then, we implanted batches into rat cortex in vivo and evaluated insertion success and their arrangement on the brain surface. When positioning motes for insertion, we discovered that they readily aggregated in molten PEG such that average array pitches were 5% longer than an individual mote’s dimensions (240 × 240 μm). Overall, 187/214 (87%) motes tightly-packed in 4 × 4 (N = 4) and 5 × 5 (N = 6) square grid configurations successfully inserted into rat cortex. After implantation, measurements of how much motes tilted (22 ± 9°, X̄ ± S) and had been displaced from their original positions were smaller than those measured in the literature for devices implanted inside the brain. Collectively, these data establish the mechanical viability of assembling and safely implanting motes with ultrasmall electrodes and epicortically-situated chips, motivating the use of arrays with similar geometries in future BMIs.
Seeing clearly with CLARI-O: a window into cellular architecture, interactions, and morphology of organoid models
Cortical organoids (COs) represent a powerful in vitro model system that recapitulates key aspects of human brain development, enabling the study of neurodevelopmental processes, cellular diversity, and disease mechanisms in a physiologically relevant 3D environment. However, traditional histological analysis of COs relies on tissue sectioning, which limits the ability to capture the full spatial complexity of organoid architecture. In this study, we establish a framework for applying CLARI-O, an improved tissue-clearing technique, for intact COs and organoid-based systems, enabling comprehensive 3D visualization and analysis of 3D organizational features. Using CLARI-O in combination with high-resolution imaging, we demonstrate the utility of tissue clearing for studying glial populations, including oligodendrocytes and microglia, considered to be underrepresented in COs, and their interactions with neurons. Additionally, we apply this method to forebrain assembloids (FAs) to visualize cellular heterogeneity and the interface between ventral and dorsal regions. Finally, we use CLARI-O to study mouse brains containing xenotransplanted COs (MB-COs) to evaluate human cell integration, migration, vascularization, and structural connectivity. This is the first study to demonstrate how tissue clearing can be used after functional assays such as calcium imaging to correlate neural activity with post hoc structural analysis in MB-COs. Together, this work establishes CLARI-O as a powerful tool for advancing 3D structural and functional interrogation of human CO-derived systems, enhancing their value for disease modeling, drug screening, and translational neuroscience.
EEG oscillatory changes following rehabilitative tactile and thermal stimulation in disorders of consciousness: a longitudinal and pilot study
IntroductionRecent advances in the understanding of sensory processing in disorders of consciousness (DoC) have highlighted the potential of exteroceptive stimuli, particularly tactile stimulation. Unlike auditory or visual inputs, tactile stimuli engage primary somatosensory pathways that may be preserved even in severely brain injured patients. The aim of our study is to identify cortical neurophysiological markers for diagnosis, prognosis, and structured rehabilitative planning.MethodsSeven patients were included. EEG was recorded at baseline (T0) and after 1 month of repeated tactile and thermal stimulation administered in a randomized manner to both hands (T1). Spectral power changes were computed by contrasting stimulation-related activity with resting state and analyzed across frequency bands and cortical regions.ResultsAt T0, sensory stimulation elicited weak and spatially limited EEG modulation, predominantly involving slow-frequency activity, consistent with reduced cortical responsiveness. Following the stimulation period (T1), EEG responses showed increased amplitude and spatial extent, characterized by a relative suppression of slow oscillations and enhanced modulation of higher-frequency activity over fronto-central and parietal regions.ConclusionRepeated tactile and thermal stimulation were associated with longitudinal changes in EEG oscillatory patterns, reflecting possible modulation of cortical responsiveness associated with sensory stimulation. The assessment of stimulation-induced EEG oscillatory changes may represent exploratory neurophysiological indicators of residual cortical responsiveness that could support future studies on sensory processing in DoC patients.
RutiSafeNet: a behavioral risk and nursing workload monitoring tool for open-door acute inpatient mental health units
IntroductionOpen-door policies for acute inpatient mental health units (AIMHU) have shown promising results in reducing coercive measures, but concerns remain among patients and staff regarding increased workload, constant surveillance, and potential safety risks associated with these policies. This study aims to develop a monitoring tool to facilitate safety management in AIMHUs by monitoring behavioral risks and nursing workload.MethodsThis study employed a qualitative approach using the content analysis method. Data were collected through three in-depth interviews and two focus groups involving staff (n=19) from the AIMHU of the hospital.Results34 items were identified to define behavioral risks related to self-harm and suicide, aggressiveness, and absconding, alongside factors affecting nursing workload. These items were categorized into three levels of risk: low, moderate, and high.DiscussionRutiSafeNet is a preliminary, observation-based monitoring prototype intended to support, rather than to predict, structured risk and workload monitoring in acute inpatient mental health units. As a qualitatively developed instrument, it requires psychometric validation, including inter-rater reliability, construct and criterion validity, predictive value, and clinical feasibility, before it can be implemented as a validated scale in clinical practice.
Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand’s Integrated Data Infrastructure
IntroductionIn mental health research involved with sensitive features and privacy issues, using integrated data offers an efficient alternative to traditional approaches such as interviews or in−person data collection. These conventional methods frequently face logistical barriers, including low response rates among study populations. Using integrated data from multiple existing administrative and survey sources provides protection for participants and economic savings for researchers, despite being constrained by the limitations of the original data sources. Our research questions were: 1) Can we conduct path and network analyses of school absenteeism, psychosocial factors, and mental health outcomes using the integrated survey data from New Zealand’s Integrated Data Infrastructure (IDI)? and 2) How can we account for the complex design of the General Social Survey (GSS) to ensure representative inference?Materials and methodsThe study population was New Zealand youth enrolled in school, aged 15 years and older in 2018, who participated in the 2018 GSS. The study analyzed the Ministry of Education data integrated with the 2018 GSS survey data from New Zealand’s IDI. To explore the relationship between outcomes of including the WHO-5, “perceived life worthwhileness”, “perceived general health”, and predictors including school absenteeism, psychosocial factors, and family factors, quantile mixed−effects regression, path analysis, and Bayesian network (BN) analysis were used. The complex survey design was calibrated using replicated weights from the GSS, a design-based method for complex sampling, in path and regression analyses, and bootstrap resampling, in BN analysis.ResultsIn descending order from the weighted path model, overall life satisfaction (standardized path coefficient: 0.36), perceived health condition (0.34), ease in accepting cultural identity (0.12), and trust in the education system (0.07) were all significantly (positively) related to students’ mental health well-being (WHO-5). Perceived life worthwhileness correlated with the WHO-5 but was only connected to overall life satisfaction. Similar factors were identified for perceived health, with additional attributing factors, such as fear of crime in the area and the family’s overall well-being.ConclusionThe integration of Bayesian networks and path analysis offers rigorous methods for detecting complex interrelationships among integrated mental health outcomes from population data and variables from a complex survey design.
The relationship between trait mindfulness and psychotic-like experiences in a brief AI-generated music listening context: the roles of presence, perceived interactivity, and emotional arousal
Background and objectiveWithin the interdisciplinary field of cyberpsychology and mental health, trait mindfulness has been associated with lower levels of subclinical anomalous symptoms, such as Psychotic-Like Experiences (PLEs), has gained increasing attention. However, in the context of daily digital human-computer interactions (e.g., listening to AI-generated music), the specific pathways through which Mindfulness operates (the involvement with Presence and Perceived Interactivity) and the boundary conditions of physiological arousal, remain to be clarified. This study aims to explore the direct predictive relationship between mindfulness and individuals’ PLEs, and to investigate the multipath effect of brief AI-generated music listening context (with Presence and Perceived Interactivity), along with the moderating effect of Arousal.MethodsWith a cross-sectional survey design, self-reported multimodal data were collected from 527 Chinese participants. Structural equation modeling (SEM) was conducted using Mplus 8.3 to empirically test the main effects (path coefficients) of the theoretical hypotheses and the moderation model.ResultsBoth the measurement and structural models demonstrated good fit. The path analysis results indicated that: (1) trait trait mindfulness was significantly and negatively associated with PLEs (p < 0.001); (2) regarding the main effect paths of brief AI-generated music listening context, mindfulness significantly and positively predict individuals’ Presence and Perceived Interactivity, while both significantly and negatively predict PLEs; (3) Arousal played a significant moderating role in the relationship between mindfulness and brief AI-generated music listening context, exhibiting a synergistic enhancement effect. Higher levels of Arousal significantly amplified the positive prediction of Mindfulness on both Presence and Perceived Interactivity (p < 0.01).ConclusionsWith the help the SEM, this study maps out the underlying multipath network through which mindfulness is associated with lower PLEs within a brief AI-generated music listening context. The observed associations suggest that presence and perceived interactivity may function as pivotal correlational nodes relevant to mental health correlates, while these results also nuance classic cognitive load assumptions by indicating a potential synergistic association between trait mindfulness and emotional arousal. These results provide a solid empirical foundation and prospective insights, for the mental health-oriented design of AI music products, such as the immersive acoustic environment construction and dynamic, arousal-based interaction recommendations.
Psychometric properties of the Chinese version of the school refusal assessment scale–revised in a clinical sample of adolescents and their caregivers with depressive disorders
School refusal behavior (SRB) is a prevalent and functionally heterogeneous problem among children and adolescents that can lead to serious academic, social, and psychological consequences. The School Refusal Assessment Scale–Revised (SRAS-R) is the most widely used instrument for identifying the functional motivations underlying school refusal, yet its psychometric properties have not been examined in Chinese clinical populations. The present study aimed to translate and culturally adapt the SRAS-R into Chinese and to evaluate its psychometric properties in a clinical sample of adolescents with depressive disorders. A total of 171 adolescent outpatients (age range 12–19 years; M = 15.5, SD = 1.90; 67.3% female) diagnosed with DSM-5 depressive disorders and meeting criteria for school refusal behavior completed both child and parent versions of the Chinese SRAS-R. Confirmatory factor analysis (CFA) using diagonally weighted least squares estimation was conducted. After removing Items 20 and 24, the four-factor model yielded strong CFI and RMSEA values for both parent (CFI = 0.99, RMSEA = 0.021, SRMR = 0.094) and child reports (CFI = 0.98, RMSEA = 0.028, SRMR = 0.097), although SRMR values were marginally above the prespecified threshold. Internal consistency reliability ranged from marginal to good across subscales (Cronbach’s α: parent = 0.664–0.815; child = 0.700–0.864), with parent-reported Factor 4 showing the weakest reliability. Factor 1 (avoidance of aversive school situations) obtained the highest mean scores for both informants, consistent with the depression-related negative affectivity characteristic of this clinical sample. Cross-informant discrepancy analyses revealed that children reported significantly higher scores than parents on Factor 2 (escape from social/evaluative situations; p = .017, d = 0.18) and Factor 4 (pursuit of tangible reinforcement; p <.001, d = 0.27), suggesting that parents may underestimate internally driven motivations. Intraclass correlation coefficients indicated fair to good parent–child agreement (ICC = 0.45–0.62), with the highest agreement for Factor 1 and the lowest for Factor 4. The findings provide initial internal-structure and reliability evidence for the Chinese SRAS-R in this single-site clinical sample and underscore the need for multi-informant assessment, while future studies should examine convergent, discriminant, criterion-related, and predictive validity in more diverse samples.
Effects of maternal autoantibody exposure at the cellular level: implications for the developing brain
Maternal autoimmunity during pregnancy can have significant impacts on fetal development. In the case of maternal autoantibody-related autism (MARA), autoantibodies (aABs) reactive to fetal brain proteins in the circulation of pregnant mothers have been strongly associated with an offspring autism diagnosis. Clinical studies have identified the protein targets for the MARA aABs as well as maternal aAB patterns that are more commonly observed in mothers of children with autism, but not in mothers of neurotypical children. Additionally, rodent studies show that gestational exposure to MARA aABs alters offspring brain development and behavior. How MARA aABs cause changes at the cellular level is an area of active investigation. In this review, we discuss the potential mechanisms by which MARA aABs interact with key neuronal and non-neuronal cells as well as their target autoantigens in the developing brain. Additionally, we highlight the potential impacts of these interactions on the developmental trajectories of targeted cells, resulting in an autism diagnosis.
EU relaxes rules for gene-edited crops
Nature Biotechnology, Published online: 28 July 2026; doi:10.1038/s41587-026-03264-4
The new laws break away from 20-year-old restrictive GMO directives to give an official nod to plants made with new genomic techniques.
Reducing the diagnostic odyssey in rare disease: why screening is not the only answer
Nature Medicine, Published online: 28 July 2026; doi:10.1038/s41591-026-04542-z
The timely detection of rare diseases is crucial, and using a range of approaches will be key to reducing the diagnostic odyssey.