Sleep difficulties represent a health priority for autistic adults, yet scalable real-world sleep measurement tools remain limited. Remote measurement technologies (RMT), including wearable and smartphone data, offer low‑burden approaches but feasibility in autistic populations is unclear. We evaluated a 28-day protocol combining passive Fitbit sleep staging and smartphone sensor data, and active daily sleep quality ratings in autistic (n = 34) and nonautistic (n = 39) participants (14–35 years). Median data availability was over 70% across modalities. However, usable data (days with valid sleep period and quality rating) were lower (< 60%), particularly for autistic participants, whose tactile sensitivity associated with reduced usable Fitbit data. Autistic participants reported lower sleep quality; however, few differences in passively derived sleep features emerged. Several features (e.g., sleep efficiency, duration, REM proportion) were related to subjective sleep quality, as were passively-derived sleep profile clusters. This study provides foundational work for developing RMT suitable for sleep measurement in autistic populations.

Qualitative EEG abnormalities are common in Autism Spectrum Disorder (ASD) and hypothesized to reflect disrupted excitation/inhibition (E/I) balance. To test this, we recently introduced a functional measure of network-level E/I ratio (fE/I). Here, we applied fE/I and other EEG measures to alpha oscillations from source-reconstructed data in the EU-AIMS dataset (267 ASD, 209 controls). We analyzed these measures alongside qualitative EEG abnormalities ranging from slowing of activity to epileptiform patterns, aiming to replicate the findings from the SPACE-BAMBI study. Contrary to our previous report, we did not observe increased fE/I variability in ASD compared to controls. EEG abnormalities were rare in adults and could not be statistically assessed. ASD children-adolescents with EEG abnormalities exhibited lower relative alpha power and fE/I compared to those without. However, EEG-abnormality scoring did not stratify the behavioral heterogeneity of ASD using clinical measures. Surprisingly, several controls also exhibited qualitative EEG abnormalities with a strikingly similar anatomical distribution of reduced fE/I, reflecting inhibition-dominated network dynamics in sensory processing regions. The robustness of this association between EEG abnormalities and reduced fE/I was further supported by re-analysis of the SPACE-BAMBI study in source space. Stratification by the presence of EEG abnormalities and their effects on network activity may help understand neurodevelopmental physiological heterogeneity and the difficulties in implementing E/I targeting treatments in unselected cohorts.

BACKGROUND:Early adversities before and after birth can impact children's cognitive and socioemotional development by altering critical brain maturational and functional processes. Some of these processes may be linked to later neurodevelopmental and/or mental health conditions. While most research is conducted in high-income countries, the majority of children live in low- and middle-income countries (LMICs) where they are more frequently exposed to poverty-related adversities. To address this gap, well-characterised longitudinal pregnancy cohorts in LMICs are needed to track trajectories of neurodevelopmental and mental health conditions in children exposed to cumulative environmental adversities. The Safe Passage Study (SPS) originally enrolled 7060 pregnant women from socioeconomically disadvantaged peri-urban communities in Cape Town, South Africa, to investigate the association between prenatal alcohol, multiple environmental risk factors and pregnancy outcome. The Safe Passage-Biomarkers of Neurodevelopmental Outcomes (BONO) study aims to follow up 2000 SPS children, aged 4-16 years, to assess the role of pre- and postnatal environmental factors in cognitive, neurodevelopmental and mental health outcomes. This report outlines the design and results of a feasibility study with 100 children, primarily aimed to confirm recruitment, assess participant retention, select measures and establish criteria for a deep-phenotyping visit. METHODS:Between March and October 2019, 100 SPS children were screened during a "broad-phenotyping visit" for adverse childhood experiences and protective factors, autistic traits and socioemotional and behavioural symptoms, and they completed cognitive tests and eye-tracking and electroencephalography assessments to measure brain function. Criteria for a second "deep-phenotyping" visit were established based on autistic traits, internalising/externalising scores and/or cognitive difficulties, to assess children and their mothers in terms of clinical and neurocognitive profile. RESULTS:Recruitment was adequate with a 96% retention rate for the deep-phenotyping visit. Feasibility study participants resembled the larger SPS cohort in most demographic and prenatal factors, except for higher prenatal depression and overcrowding indices. Most clinical and experimental measures were deemed suitable with minor modifications, and acquisition rates were high. The nature and length of visits were acceptable to families and testers. Threshold scores were adjusted to include 30% of participants for deep phenotyping. CONCLUSIONS:The feasibility study fulfilled progression criteria for the planned multimodal study.

Background Autism is characterized by social-communicative difficulties, with sex differences in symptom presentation. Social functioning is inherently dynamic, however, many neuroimaging studies rely on static, time-averaged approaches that obscure time-varying network interactions, potentially limiting our ability to capture the dynamic processes underlying social cognition. The fusiform gyrus (FFG), central to face and social perception, shows differences in functional connectivity in autism, yet is rarely examined dynamically or as a spatially heterogeneous structure. Here, we investigate the dynamic functional connectivity of FFG subregions in terms of their large-scale network configurations as a function of diagnosis and sex. Methods We applied micro co-activation patterns analysis (μCAPs) to resting-state fMRI data from 286 autistic individuals (208:78 males:females) and 228 non-autistic individuals (146:82 males:females), aged 6-30 years, from the EU-AIMS LEAP dataset. μCAPs were identified using k-means clustering with FFG as the seed, and connectopic mapping positioned each μCAP along the principal connectivity gradient. We quantified μCAPs occurrence and further examined dwell time, transition probabilities, and spatial extent, along with associations with social functioning. Results Six μCAPs mapped onto distinct FFG subregions along a posterior-anterior axis. A significant sex-by-diagnosis interaction emerged for a default mode network (DMN)-related μCAP. Non-autistic females exhibited significantly more frequent occurrences, longer dwell times and distinct transition dynamics compared to males, while no sex difference was observed in autism. The spatial extent of this μCAP showed a reversal of typical sex effects. Conclusions Autism is associated with an attenuation and reversal of typical sex differences in the functional configuration and spatial extent of FFG-DMN coupling, indicating that neural signatures of social-cognitive functions are sex-specific and dynamic. These findings suggest that sex is a neurobiologically meaningful dimension of heterogeneity in autism, expressed in dynamic network organization.

The excitatory/inhibitory (E/I) imbalance theory suggests that excitatory and inhibitory alterations underlies autism characteristics. However, genetic underpinnings of this imbalance and its impact on brain function and behavior remains unclear. We explored causal links between glutamate and GABA gene-set polygenic scores (PGS) for autism and core autism characteristics, putting particular attention on the restricted- and repetitive behaviors (RRBs) domain by including functional activity (fMRI) during inhibitory control (in the anterior cingulate cortex (ACC) and striatum). Causal links between genes, brain and behavior was evaluated using Bayesian Constraint-based Causal Discovery (BCCD) algorithms, to build causal models of these relationships in a discovery sample (LEAP cohort: autistic = 343, neurotypical = 253) and two generalization cohorts with partially overlapping measures (TACTICS cohort: autistic = 60, neurotypical = 100, Simon Simplex Collection: autistic = 2756). In the discovery sample, we found a causal link between glutamate PGS and core clinical characteristics of autism, particularly the communication domain (Autism Diagnostic Interview-Revised) in autistic participants, with 95% reliability. We did not find links between functional activity during inhibitory control and other measures. For one generalization cohort, we further report on the impact of 1H-MRS measures of glutamate, identifying a causal link between GABA autism PGS on ACC glutamate concentrations. Not all links were identified in the generalization cohorts, which may be due to clinical and genetic differences between the cohorts. While our results reinforce the previously found association between glutamate genes and core clinical autism behaviors, task-based functional activity may not be causally related to RRBs.

Sleep problems are common among autistic individuals; however, reliable sleep assessment methods suitable for everyday life are lacking. Remote measurement technologies (RMT), including wearable sensors, passive smartphone data, and brief active self-reports, offer low-burden, scalable approaches. However, their feasibility has not been investigated in autistic adolescents and adults. This study assessed the feasibility of a 28-day multimodal RMT protocol for sleep measurement in autistic and nonautistic participants, examined differences in passive sleep features and active sleep quality scores between groups, and explored associations between them. Autistic and nonautistic participants, aged 14-35 years, completed a 28-day multimodal observation protocol involving Fitbit devices, RADAR-base passive sensing and active reporting apps. Passive sleep features were extracted using Fitbit sleep staging and steps, and smartphone accelerometer, ambient light, and app usage data. The features comprised sleep onset and offset time, sleep preparation period, wake after sleep onset, number of awakenings, latency to arising, total sleep time, sleep efficiency, and sleep-stage proportions. The feasibility assessment considered modality-specific data availability and the number of eligible analysis days (defined as those with both an identifiable primary sleep period and a sleep quality score) and examined correlations with participant characteristics. Linear mixed-effects regression models evaluated group differences and correlations between passive measures and active sleep quality scores. We used agglomerative clustering to explore whether nights could be meaningfully grouped based on passive sleep features, and whether these groups associated with active reports. Feasibility analyses were based on 34 autistic and 39 nonautistic participants who enrolled in the study. Median Fitbit wear time, passive smartphone data availability, and active sleep rating availability were similar across groups and exceeded 75%. However, only 447 of 952 autistic participant-days (47.0%) and 645 of 1092 nonautistic participant-days (59.1%) were usable. Among autistic participants, greater tactile sensitivity was associated with lower availability of the primary sleep period and fewer eligible days. Autistic participants reported lower sleep quality than nonautistic participants (β = .447, P = .011), had shorter total sleep time (β = .408, P = .005), and had shorter sleep preparation periods (β = .444, P = .005). In both groups, higher sleep efficiency, longer total sleep duration, greater proportion of REM sleep, and later sleep offset time were associated with higher sleep quality ratings. Agglomerative clustering yielded three passive sleep feature profiles associated with significantly different active ratings. This study demonstrates favourable feasibility of multimodal RMT sleep assessment for most autistic and nonautistic participants, whilst also identifying specific challenges for some. Passive sleep features and derived profiles corresponded with active daily sleep quality ratings, supporting their utility for further refinement and adaptation in pursuit of low-burden, ecologically valid sleep assessment for autistic populations. RR2-10.2196/71145

Group-mean comparisons often identify atypical functional connectivity in autism, but it remains unclear whether these findings consistently manifest at the individual level. Here we use normative modeling to quantify the interindividual heterogeneity of atypical functional connectivity across multiple brain scales using multicenter resting-state functional magnetic resonance imaging data from 1,824 participants (796 autistic individuals and 1,028 neurotypical controls) in a cross-sectional study across 32 sites. We find that no single functional connectivity estimate showed extreme deviation from normative expectations in more than 4% of people in either group. However, these deviations converged on common regions and networks in autistic people, who showed up to double the level of overlap compared with controls. Specifically, autistic participants demonstrated convergent hypoconnectivity in sensorimotor and attention regions and convergent hyperconnectivity between frontoparietal and default mode networks. Functional connectivity deviation patterns significantly predicted social and cognitive abilities. These findings demonstrate that autism exhibits scale-dependent heterogeneity, characterized by normative variability at the connection level but significant convergence at regional and network scales. These convergent regions and networks may be used to identify targets for individualized therapeutic development.

Background Autism frequently co-occurs with attention-deficit/hyperactivity disorder (ADHD), which significantly impacts developmental outcomes and quality of life. While executive functions (EF) and reward processing differences are commonly observed in both conditions, it remains unclear whether early neurocognitive variation can predict emergence of ADHD traits in preschool children. This study aimed to identify transdiagnostic neurocognitive profiles in a preschool sample and evaluate their clinical, neurobiological, and prognostic relevance. Method This study utilised data from a well-characterised cohort of 240 autistic and neurotypical preschoolers aged 34–52 months. Indices from a novel touchscreen battery assessing inhibitory control, sustained attention, and reward learning were entered into a density-based clustering algorithm. The resulting subgroups were externally characterised on concurrent clinical and neuroanatomical characteristics, and as predictors of ADHD features after 12–15 months. Results The clustering analysis identified four neurocognitive subgroups: EF+Reward difficulties, showing challenges across all assessed domains alongside elevated autistic and ADHD traits but typical-range IQ; Low Completion, marked by task completion difficulties, lower IQ, and moderate ADHD traits; and two subgroups with relatively intact profiles, exhibiting typical neurocognitive performance and low ADHD traits. The EF+Reward difficulties and Low Completion subgroups showed widespread reductions in frontal and parietal cortical surface area relative to the intact groups. Subgroup membership also predicted ADHD traits over time, supporting the prognostic value of these neurocognitive profiles. Limitations: External replication was not possible due to the absence of comparable publicly available preschool cohorts. Multiple internal and external validation strategies nonetheless support subgroup robustness. Additional limitations include reliance on parent-reported ADHD measures and a small EF+Reward difficulties subgroup. Conclusions Neurocognitive subtypes, derived from scalable innovative tools, can parse heterogeneity in early development in autistic and neurotypical sample and predict early ADHD features, mapping onto relevant neuroanatomy. Our study highlights the potential of early transdiagnostic cognitive profiling for identifying children at an increased likelihood of co-occurring autistic and ADHD traits and informing targeted early interventions.

Phelan-McDermid syndrome (PMS) is a relatively frequent cause of syndromic intellectual disability (ID) and autism spectrum disorder (ASD). It is typically caused by genetic alterations in the 22q13 chromosomal region, most often involving heterozygous deletions or mutations in the SHANK3 gene. More than half of affected individuals exhibit functional impairments in speech, cognition, motor skills, and behavior. Despite multiple ongoing therapeutic programs, objective and scalable liquid biomarkers to support patient stratification and to monitor disease course or treatment response are still lacking. Here, in a pilot study involving 23 individuals with PMS, we identified two biomarkers that are significantly altered compared to a control group and are associated with symptom severity. First, SHANK3 protein was detectable in peripheral blood mononuclear cells (PBMCs) and was markedly reduced in PMS (mean -77% vs. controls), consistent with SHANK3 haploinsufficiency; lower PBMC SHANK3 levels were associated with the presence of developmental regression, supporting its potential utility as a target-engagement/monitoring biomarker rather than a diagnostic screen. Additionally, plasma levels of beta-synuclein, a neuron-specific synaptic protein, were elevated in PMS and positively correlated with the severity of speech impairment. Both biomarkers were successfully back-translated in a Shank3 transgenic mouse model, where beta-synuclein levels were normalized through modulation of the mGlu5 receptor. Together, these results provide initial evidence for SHANK3 in PBMCs and plasma beta-synuclein as complementary liquid biomarkers to aid prognosis and enable objective monitoring of therapeutic response in PMS, warranting validation in larger and pediatric longitudinal cohorts.

Background Growing emphasis on participatory and community-informed research has increased interest in the representation of neurodivergent researchers within the research workforce. This exploratory study investigated the presence of neurodivergent researchers within a large European autism research consortium and examined associations with selected demographic, occupational, and mental health characteristics. Methods An anonymous survey was distributed to active contributors within the consortium. Participants reported autism diagnosis status, self-identification, other neurodevelopmental conditions, demographic characteristics, career stage, and lifetime mental health diagnoses. Results 33% percent of participants met the study definition of neurodivergence, including researchers with a formal autism diagnosis, those undergoing diagnostic assessment, self-identified autistic researchers, and researchers reporting other neurodevelopmental conditions. Neurodivergent researchers were represented across all career stages within the consortium. Neurodivergence was associated with increased odds of reporting a lifetime mental health diagnosis after adjustment for age and gender. Bayesian sensitivity analyses produced consistent findings. Conclusions These findings provide an initial characterisation of neurodivergent researchers within a large autism research consortium and underscore the importance of considering neurodivergent researchers as part of broader discussions on inclusion in autism research. Further research across multiple research settings is needed to better understand workforce experiences, disclosure, and support needs.

Abstract Sensory processing differences are a core feature of autism, affecting 60–95% of individuals, yet the associated neural mechanisms remain unclear. An excitation–inhibition (E/I) imbalance in brain circuits has been proposed, but in vivo evidence linking genetic variation in E/I pathways, regional neurochemistry, neural circuit function, and sensory behaviour has been lacking. Here we performed a multimodal investigation in 206 individuals (130 autistic), integrating gene-set polygenic scores for excitatory glutamatergic and inhibitory gamma-aminobutyric acid (GABA)-ergic pathways, magnetic resonance spectroscopy (MRS) measures of regional GABA and Glx (glutamate + glutamine) levels, vibrotactile psychophysical measures of tactile perception, and questionnaire measures of behavioural sensory reactivity. We found that glutamatergic polygenic scores predicted thalamic glutamate levels in neurotypical but not autistic individuals, suggesting altered genotype–neurochemistry coupling in autism. Thalamic Glx:GABA levels associated with tactile perception in both groups, but with opposing directions of effect, indicating that autistic and neurotypical individuals achieve similar perceptual outcomes with potentially differing thalamocortical circuit mechanisms. Within autistic individuals, tactile perceptual differences further related to behavioural sensory reactivity. Together, these findings suggest that autistic sensory processing potentially relies on distinct circuit mechanisms linking genetic variation, neurochemistry and perception. This work thus has important implications for how sensory differences are conceptualised, studied, and interpreted, and ultimately for how interventions and support are developed. Abstract Figure Summary Figure Summary of our comprehensive investigation of the role of excitation and inhibition in sensory processing differences in autism by integrating GABA and glutamate gene set polygenic scores (PGS), MRS-measured thalamic and anterior cingulate cortex (ACC) Glx (glutamate + glutamine) and GABA+ (GABA + macromolecules) levels, vibrotactile psychophysical measures of tactile perception (tactile detection thresholds, discrimination thresholds and tactile adaptation etc.) and questionnaire-based measures of sensory reactivity (behavioural and emotional responses to sensory stimuli, including hyper-and hypo-reactivity).

1H-Magnetic resonance spectroscopy (1H-MRS) is a noninvasive technique for quantifying brain metabolites, including glutamate, glutathione (GSH), and γ-aminobutyric acid (GABA), which are essential for brain function and implicated in various neurodevelopmental conditions. As such, 1H-MRS methods that enable reliable and accurate measurement of these metabolites are of considerable clinical value. Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES; echo time [TE] = 80 ms) is a spectral editing technique that allows for the simultaneous quantification of GABA and GSH, using subtraction approaches to resolve these metabolites in a difference spectrum. Additionally, glutamate plus glutamine resonances (Glx) can be resolved either from the HERMES GABA-edited difference spectrum (GABA-DIFF) or from the sum of all HERMES transients (SUM spectrum). However, the reliability of 80-ms HERMES for quantification of Glx has not been systematically assessed. Here, we evaluate the agreement between Glx obtained from HERMES GABA-DIFF and SUM spectra with Glx derived from short-TE PRESS (TE = 35 ms), which is conventionally used for Glx estimation and has demonstrated reproducibility. Data were acquired from 139 participants across two brain regions (ACC and Thalamus voxels), three scanners, two diagnostic groups (autism and neurotypical development) and two age groups (adolescent/adult and preschooler). Comparisons were made using both creatine-scaled and tissue-corrected Glx estimates. Our findings demonstrate significant systematic and proportional bias between Glx estimates from HERMES (SUM and GABA-DIFF) and short-TE PRESS, consistent across scanners, voxels, age groups and diagnostic categories. These findings indicate that Glx estimates derived from HERMES are not directly comparable to those from short-TE PRESS, and this discrepancy is consistent across a multisite study setting. This underscores the importance of sequence selection and careful methodological consideration when integrating and interpreting data from 1H-MRS across different acquisition protocols.

Objective Accumulating research conducted in high-income countries has reported that early life environmental factors (ELF) are linked to emotional-behavioral problems. However, approximately 90% of the world’s children live in low- and middle-income countries, where research remains limited. We investigated the prospective associations between ELF and emotional-behavioral problems in South African children and adolescents. Method Data were drawn from the Safe Passage Study, a population-derived birth cohort (5,889 mother-child dyads). ELF (pre-, peri-, and postnatal) were collected by questionnaires and clinical assessments and medical records at inception. In this Biomarkers of Neurodevelopmental Outcomes (BONO) follow-up, emotional-behavioral problems (externalizing and internalizing problems) were measured once using caregiver-reported Strengths and Difficulties Questionnaire at age 5-16 years (n=1,284). Associations were examined using regression models adjusted for the child’s sex and age at assessment. Results Of the 104 ELF, prenatal factors associated with both increased externalizing and internalizing problems were maternal mental health i.e., self-harm (externalizing problems: beta coefficient (β)=0.69; internalizing problems: β=0.60), depression (externalizing problems: β=0.06, internalizing problems: β=0.08) and anxiety (externalizing problems: β=0.05; internalizing problems: β=0.04) and infections (externalizing problems: β=0.65; internalizing problems: β=0.45). Factors only associated with externalizing problems included prenatal maternal substance use, asthma, paternal education, and infant secondhand smoke exposure. Enlarged placenta was associated with internalizing problems. Fetal, birth, and other placental factors showed modest associations. Conclusion In this South African setting, prenatal maternal mental health and infections emerged as consistent risk factors for both externalizing and internalizing problems. Future studies are needed to elucidate the impact of co-occurring ELF and investigate the mechanisms through which these ELF contribute to the emergence of emotional-behavioral problems.

Social disengagement and empathizing difficulties are common in autism and have been linked to various socioemotional outcomes. Yet, their relationship remains understudied. This study addresses this gap by examining associations among different forms of social disengagement and empathy in autistic children and adolescents, as well as their interactive effects in predicting concurrent emotion problems. Parents of 689 autistic youth aged 5-17 years (M [SD] = 11.23 [3.56]; 76% male) completed questionnaires: the Child Social Preference Scale assessed shyness, unsociability, and social avoidance; the Children's Empathy Quotient measured cognitive and affective empathy; and the Strengths and Difficulties Questionnaire assessed emotion problems. Results showed that all forms of social disengagement were associated with difficulties in both cognitive and affective empathy, with social avoidance showing the strongest associations. Furthermore, the interplay between different forms of social disengagement and empathy contributed to variability in emotion problems. Specifically, the links between unsociability and social avoidance and elevated emotion problems were stronger among youth with higher levels of empathy. In contrast, the positive association between shyness and emotion problems was not influenced by empathy. These findings underscore the importance of considering the multifaceted nature of social disengagement and its association with empathy, offering directions for future research.

Many autistic people have challenges with adaptive function, impacting education, employment and independent-living goals. Adaptive function outcomes of autistic people vary considerably, which makes planning for future independence or support needs challenging. Here, using a developmentally sensitive approach, we investigated if quantifying cortical functional connectivity - a neurobiological feature that differs in autism - could predict longitudinal changes in adaptive function in autistic people. Using electroencephalography (EEG) in 150 autistic and 159 non-autistic participants (aged 6-31 years), we investigated if the extent of cortico-cortical functional connectivity (mean degree) and small-world network organisation (small-world index) could predict longitudinal changes in adaptive function over an interval of 19-months (SD = 3.5 months). We assessed predictive performance for both continuous and binary changes in adaptive function abilities. We explicitly studied age-effects, given differing neurodevelopmental trajectories in autistic compared to non-autistic people. We quantified the extent to which these functional connectivity metrics had properties desired in prognostic biomarkers: high reliability and convergence with underlying biology (polygenic variation). Small-world index significantly predicted longitudinal changes in adaptive function in autistic people across the entire age-range. Predictive performance was best in 15-21-year-olds, where small-world index and mean degree explained 30% and 33% of additional variance in adaptive function outcomes, respectively. In this age-group, functional connectivity metrics outperformed measures of intelligence and autistic features in predictive ability. In categorising binary (increasing versus not-increasing) outcomes in autistic people, the model containing mean degree had an AUC of 0.80 [95% CI: 0.63-0.97] in 15-21-year-olds, while the model containing small-world index had an AUC of 0.76 [95% CI: 0.63- 0.89] across the 6-31-year age-range. Both metrics demonstrated high test-retest reliability and were significantly associated with polygenic variation in brain volume in autistic people. We demonstrate the first evidence that EEG-derived functional connectivity metrics significantly predict adaptive function outcomes in autistic people and may be developed as prognostic biomarkers. Considering developmental stage may reconcile heterogeneous findings in previous autism connectivity literature. ### Competing Interest Statement Joshua B Ewen previously consulted for Novartis. In the past 3 years, Jan K Buitelaar has been a consultant to / member of advisory board of / and/or speaker for Takeda, Medice, Angelini, Neuraxpharm and Bitsphi. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents, royalties. Pilar Garces is employed by Roche Innovation Center, Basel, Switzerland. Tony Charman has received consultancy fees from F. Hoffmann-La Roche Ltd. and royalties from Sage Publishing and Guilford Press. No other author has competing interests to declare. ### Funding Statement This work was supported by EU-AIMS (European Autism Interventions), which received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115300, the resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (grant FP7/2007-2013), from the European Federation of Pharmaceutical Industries and Associations companies' in-kind contributions, and from Autism Speaks. The results leading to this publication have also received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, and SFARI. This study was also delivered through the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC). None of the funders had a role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Any views expressed are those of the author(s) and not necessarily those of any of the funders, the NIHR or the Department of Health and Social Care. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: London - Queen Square Research Ethics Committee gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The LEAP Study data is available here, subject to an approved data application: https://redcap.pasteur.fr/surveys/?s=YRFF78PH89

Research priorities for autistic people include developing effective interventions for the numerous challenges affecting their daily living, e.g., mental health problems, sleep difficulties, and social wellbeing. However, clinical research progress is limited by a lack of validated objective measures that represent target outcomes for improvement. Digital technologies, including wearable devices and smartphone applications, provide opportunities to develop novel measures that may reflect everyday experience and complement key clinical assessments. However, little is known about the acceptability and feasibility of implementing digital data collection in this population. Our objective is to develop novel digital endpoints relevant to key target outcomes, for clinical research, including social communication, sleep, and mental health, using both in-person and remote (i.e., at home) procedures. In particular, this protocol aims to implement and evaluate the usability, acceptability, adherence and feasibility of such procedures, as well as explore the properties of certain resulting measures. Eligible autistic and non-autistic participants in the AIMS Longitudinal European Autism Project (LEAP) were invited to participate in a digitally augmented in-person Autism Diagnostic Observation Schedule-2 (ADOS-2) and a 28-day remote measurement (RM) protocol involving wearing a Fitbit device, downloading a passive smartphone data collection app, and using two active reporting apps. The first LEAP study participants were enrolled in September 2021 (in-person component) and March 2022 (RM component). To date, 190 participants have taken part in the digitally augmented ADOS-2 component, and 86 participants have been enrolled for the remote measurement protocol. Recruitment is now complete with some RM data collection ongoing until August 2025. Preliminary data analysis, including exploration of acceptability and feasibility metrics, pipeline development for ADOS-2 speech analysis and RM sleep measures, and framework development for coding qualitative data, has started. Results are expected to be submitted for publication from February 2025. This study lays important groundwork in understanding the acceptability and feasibility of in-person and remotely implemented digital measurement procedures to capture meaningful outcomes in domains important to improving everyday life for autistic people.

Background The field of biomedical research is entering a new era, in which public data sharing is increasingly the norm. There are many advantages of embracing data sharing initiatives, including tackling the replication crisis through enhanced transparency and publication of null findings, facilitating global collaborations to accelerate research progress, enhancing cost-effectiveness by reducing duplication of efforts, and making scientific advances more accessible to the public. However, there are also several crucial ethical and logistical challenges that must be addressed to maximise the benefits of data sharing and minimise risks. The potential, and increasingly recognised, risks of unregulated data sharing (e.g., data reidentification, misuse, and lack of representativeness due to variability in who agrees to share data) have also been exemplified by high profile data breaches and directly clash with efforts to make research more robust, accessible, and global. Methods/Results Here, we narratively outline current challenges for data sharing from the perspective of child and adolescent psychiatry, one area where they may be particularly acute. For example, child and early adolescent research often requires caregivers to consent on behalf of a minor – increasing the responsibility of researchers to consider how the science of today may evolve into the future (when those individuals are no longer minors). We use data from our research consortium Autism Innovative Medicines Study - 2 - Trials (AIMS-2-TRIALS; https://www.aims-2-trials.eu/) to illustrate the points raised in this perspective piece. Conclusions We also propose some potential solutions to begin to address current challenges for data sharing, focusing on key priorities, including shared control of data curation between researcher and participant communities and equity of access by research groups to the tools and resources needed to conduct responsible and sustainable data sharing.

Most current autism research focuses on categorical comparisons (e.g., autistic vs. neurotypical people) and usually examines only one biological domain (e.g., cognition, genetics, or brain imaging). Here, we present a comprehensive resource integrating quantitative phenotypic data, whole genome sequencing, brain magnetic resonance imaging, and electroencephalography. A total of 5,549 people were recruited in Europe through LEAP and InovAND, including 2,061 autistic people, 62 people with intellectual developmental disability who do not meet diagnostic criteria for autism, 2,551 undiagnosed relatives and 875 neurotypical people. Among these people, 2,531 have both clinical and genetic data, and 875 people additionally have neuroimaging data (EEG and/or MRI). We stratified people based on autistic traits and cognitive skills, revealing clusters with distinct genetic and brain signatures. Differences were observed in both rare and common variants, particularly in synaptic and chromatin remodeling genes pathways, and suggesting distinct trajectories of cortical maturation at early stages of development. This resource is available to support research into the complex links between genes, brain structures/functions, and autism. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by Institut Pasteur, Universite Paris Cite, the Simons Foundation Autism Research Initiative (SFARI award #240059), the Bettencourt-Schueller Foundation, the GenMed Labex, and AIMS-2-TRIALS, which received support from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA and AUTISM SPEAKS, Autistica, SFARI, and the Inception program (Investissement d'Avenir grant ANR-16-CONV-0005). This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant 847818 (CANDY), and from Horizon Europe under grant 101057385 (R2D2-MH). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work benefited from the DNA & cell bank core facility, at the ICM-Paris Brain Institute. This work received support from the French government, managed by the National Research Agency (Agence Nationale de la Recherche), under the France 2030 program, reference ANR-23-IAHU-0010. Part of this work was funded by a grant from the Conseil Regional d'Ile de France (grant number EX024087). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study is multi-site; Ethical approval was obtained through ethics committees at each site: The London Queen Square Health Research Authority Research Ethics Committee of King's College London & University of Cambridge (KCL & UCAM) gave ethical approval for this work (13/LO/1156). The Radboud Universitair Medisch Centrum Instituut Waarborging Kwaliteit en Veiligheid Commissie Mensgebonden Onderzoek Regio Arnhem-Nijmegen (Radboud University Medical Centre Institute Ensuring Quality and Safety Committee on Research Involving Human Subjects Arnhem-Nijmegen) from Radboud University Nijmegen Medical Centre (RUNMC) & University Medical Centre Utrecht (UMCU) gave ethical approval for this work (2013/455). The UMM Universitatsmedizin Mannheim, Medizinishe Ethik Commission II (UMM University Medical Mannheim, Medical Ethics Commission II) from Central Institute of Mental Health (CIMH) gave ethical approval for this work (2014-540N-MA). The Universita Campus Bio Medica De Roma Comitato Etico (University Campus Bio-Medical Ethics Committee De Roma) from the University Campus Bio-Medico (UCBM) gave ethical approval for this work (18/14 PAR ComET CBM). The Centrala Etikprovningsnamnden (Central Ethical Review Board) from Karolinska Institutet (KI) gave ethical approval for this work (32 2010). The Ethics Committee overseeing the INOVAND cohort (Inserm C07 33) cohorts gave ethical approval for this work (CEER 2008 A00019 46). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets generated and analyzed in this study, including all modalities from the LEAP (EUAIMS / AIMS2 TRIALS) and InovAND cohorts are securely stored on ELIXIR LU servers, part of the European infrastructure for life science information, based at the Luxembourg Centre for Systems Biomedicine (LCSB) and supported by the Luxembourg National Data Service (LNDS). The LEAP dataset (clinical, cognitive, eye-tracking, neuroimaging, and genetic data) is available via the ELIXIR Luxembourg data catalog. The InovAND dataset will be made available through the same repository upon publication. Access to both datasets is granted upon reasonable request following review and approval by the Data Access Committee, including scientific leads, ethics experts, and Autism community representatives. Requests should be submitted via the data catalog website with a detailed project proposal describing the intended use of the data and must be aligned with General Data Protection Regulation (GDPR) requirements as well as the AIMS2 TRIALS consortium or InovAND data sharing policies, respectively.

Objective:Altered social drive is a common feature across psychiatric disorders and is embedded in multiple diagnostic criteria, underscoring the need for a transdiagnostic approach. However, the extent to which social drive alterations vary across diagnoses and clinical presentations remains poorly characterized. This study examined whether distinct social drive profiles-defined by differences in social reticence, seeking, and maintaining challenges-relate to variations in clinical features and show specificity to particular neurodevelopmental and neuropsychiatric conditions. Method:Data were drawn from the Healthy Brain Network (N = 2,380; ages 5-21 years, mean [SD] age = 10.27 [3.39] years; 68% male) and included youth with attention-deficit/hyperactivity disorder, anxiety disorders, autism spectrum disorder, oppositional defiant/conduct disorder, depressive disorders, obsessive-compulsive disorder, and tic disorders. Latent profile analysis identified distinct social drive profiles based on constellations of social reticence, seeking, and maintaining challenges. Profiles were compared across demographic, social functioning, and clinical measures, and the distribution of diagnostic categories within each profile was assessed. Results:Five profiles emerged: engaged (n = 1,530), inhibited (n = 477), aloof (n = 189), avoidant (n = 143), and constrained (n = 50). Profile differences were evident in demographic factors, social functioning, and clinical features. No single diagnosis mapped exclusively onto any profile; rather, participants with distinct neurodevelopmental or neuropsychiatric diagnoses were distributed across all 5 profiles. Conclusion:Psychiatric diagnoses alone may not fully capture alterations in social drive, which appear to transcend diagnostic boundaries. These findings support a transdiagnostic framework and challenge disorder-specific models of social drive differences.

INTRODUCTION:Increased resistance to uterine artery blood flow is an index for poor pregnancy outcomes. This study aimed to examine the association between maternal uterine artery pulsatility index and cognitive development in infants aged one year, and whether placental dysfunction moderates this relationship. METHODS:This was a prospective cohort study in an economically deprived community in South Africa. 1297 pregnant women with singleton gestations and their term infants were assessed. uterine artery pulsatility index, assessed by Doppler ultrasound in the second and third trimesters of pregnancy were examined. Placental dysfunction is indicated by maternal vascular malperfusion and accelerated villous maturation of the placenta. The primary outcome was infant cognitive development, assessed by the composite score of the Mullen Scales of Early Learning, at one year of age. RESULTS:Higher uterine artery pulsatility index was associated with lower cognitive scores, when adjusting for alcohol consumption and antenatal depression in the second trimester (β = -0.086, p = 0.007), explaining 5% of the variance in the model. There was no evidence of moderation by maternal vascular malperfusion or accelerated villous maturation of the placenta. CONCLUSIONS:Abnormal uterine artery pulsatility indices during the second trimester is associated with cognitive development in infants.