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.

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

Data sharing is a key element of scientific research, but it is associated with many complex legal, ethical, and practical challenges. These are particularly salient in autism research, where concerns have been raised about researchers' intentions, research priorities not aligning with those of autistic people, and differing opinions within stakeholder communities as to what priorities should be addressed. This review paper was co-produced through an iterative collaborative process to incorporate diverse viewpoints of stakeholder representatives from academia, charity, industry, the medical community, and the autism community. We discuss the main benefits and challenges of autism data sharing and argue that the perspectives of autistic people must be central to discussions around its ethical and technological aspects. We outline recommendations for ethical and responsible data sharing practices and note key developments within the field, including federated data sharing and community platforms and registries.

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.

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.

Background Research priorities for autistic people include developing effective interventions for the numerous challenges affecting their daily living, for example, mental health problems, sleep difficulties, and social well-being. 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 apps, 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. Objective The primary objective of this study is to evaluate the usability, acceptability, adherence, and feasibility of a dual in-person and remote (ie, at-home) protocol. Secondarily, we aim to explore the properties of certain resulting data with a view to developing novel digital end points for key target outcomes, including social communication, sleep, and mental health. Methods Eligible autistic and nonautistic in the AIMS Longitudinal European Autism Project 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 2 active reporting apps. Results The first AIMS Longitudinal European Autism Project 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 RM protocol. Recruitment is now complete with some RM data collection ongoing until August 2025. Data analysis has commenced, including qualitative framework analysis of feedback interview data coproduced with autism community members, exploration of acceptability and feasibility metrics, pipeline development for ADOS-2 speech analysis, and RM sleep measures. Conclusions 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. International Registered Report Identifier (IRRID) DERR1-10.2196/71145

At least 50% of autistic people experience clinically relevant anxiety symptoms. However, reasons for elevated rates of anxiety in autism remain poorly understood and there is a high unmet need for novel and adapted therapies for anxiety that are accessible to autistic people. This study aimed to establish the feasibility of a novel app-based anxiety management tool ("Molehill Mountain") that has been developed with, and adapted for, autistic people. A single-centre, single-arm feasibility study design was employed, whereby autistic people (≥ 16 years) with mild-to-severe symptoms of anxiety were recruited to a 13-week intervention period (King's College London, UK; clinicaltrials.gov identifier NCT05302167). Of 123 prospective participants screened, 100 (81%) participants aged 16-74 years (n = 69 female) were enrolled within approximately 15 months. n = 76 (76%) completed an anxiety measure at ~15 weeks (Generalized Anxiety Disorder-7 Item Scale; GAD-7). Most adhered to the full intervention duration: 65% (n = 47), with most using the app weekly (1-6 days per week; 58%). 73% of participants agreed that they found the app easy to use overall and that an app is a good format for offering anxiety support to autistic people. There was a significant reduction in self-reported anxiety symptom severity with mean difference 2.88 (95% CI 1.88, 3.89; p < 0.001; Cohen's d = 0.45). We found that an autism-adapted app-based anxiety management tool is acceptable to the community and associated with reduced anxiety symptom severity in autistic adults, on average. Following optimization to further enhance usability, the efficacy of the Molehill Mountain app for reducing anxiety must now be tested under randomized controlled conditions in a full-scale clinical trial.

Imaging transcriptomics has become a power tool for linking imaging-derived phenotypes (IDPs) to genomic mechanisms. Yet, its potential for guiding CNS drug discovery remains underexplored. Here, utilizing spatially-dense representations of the human brain transcriptome, we present an analytical framework for the transcriptomic decoding of high-resolution surface-based neuroimaging patterns, and for linking IDPs to the transcriptomic landscape of complex neurotransmission systems in vivo. Leveraging publicly available Positron Emission Tomography (PET) data, we initially validated our approach against molecular targets with a high correspondence between gene expression and protein binding. Subsequently, we used the cortical gene expression profiles of candidate genes to dissect two discrete classes of GABAA-receptor subunits, each characterized by a distinct cortical expression pattern, and to link these to specific behavioural symptoms and traits. Our approach thus represents a future avenue for in vivo pharmacotranscriptomics that may guide the development of targeted pharmacotherapies and personalized interventions.

BACKGROUND:Neurodevelopmental conditions, such as autism, are highly heterogeneous at both the mechanistic and phenotypic levels. Therefore, parsing heterogeneity is vital for uncovering underlying processes that could inform the development of targeted, personalized support. We aimed to parse heterogeneity in autism by identifying subgroups that converge at both the phenotypic and molecular levels. METHODS:An imaging transcriptomics approach was used to link neuroanatomical imaging-derived phenotypes in autism to whole-brain gene expression signatures provided by the Allen Human Brain Atlas. Neuroimaging and clinical data of 359 autistic participants ages 6 to 30 years were provided by EU-AIMS (European Autism Interventions) LEAP (Longitudinal European Autism Project). Individuals were stratified using data-driven clustering techniques based on the correlation between brain phenotypes and transcriptomic profiles. The resulting subgroups were characterized on the clinical, neuroanatomical, and molecular levels. RESULTS:We identified 3 subgroups of autistic individuals based on the correlation between imaging-derived phenotypes and transcriptomic profiles that showed different clinical phenotypes. The individuals with the strongest transcriptomic associations with imaging-derived phenotypes showed the lowest level of symptom severity. The gene sets most characteristic for each subgroup were significantly enriched for genes previously implicated in autism etiology, including processes such as synaptic transmission and neuronal communication, and mapped onto different gene ontology categories. CONCLUSIONS:Autistic individuals can be subgrouped based on the transcriptomic signatures associated with their neuroanatomical fingerprints, which reveal subgroups that show differences in clinical measures. The study presents an analytical framework for linking neurodevelopmental and clinical diversity in autism to underlying molecular mechanisms, thus highlighting the need for personalized support strategies.

Background Due to the increased emphasis on co-produced and community led research, neurodiversity within research communities has sparked interest, particularly within the context of autism research. This study investigates the presence of neurodivergent researchers within a neuroscience research consortium, with a particular focus on autism prevalence. Methods Using survey data collected from active contributors to the consortium, we examined the self-reported neurodivergent status of researchers, including formal diagnoses of autism, ongoing diagnostic processes, and self-identification as neurodivergent. Results A proportion of the surveyed researchers reported formal diagnoses or self-identification as autistic (23%), that were significantly more frequent at career stages below and including postdoctoral roles (Chi-Square p-value = 0.01). Further, we identified an association between neurodivergence and a diagnosis of a mental health condition among researchers (Coef. = 1.93, p-value = 0.002), highlighting the importance of accommodating neurodiversity within research environments. Conclusions This study underscores the need for inclusivity and support for neurodivergent researchers, particularly in the context of neuroscience that does or does not yet embed participatory research initiatives. By amplifying the voices of neurodivergent researchers, research communities can enhance the equity and impact of their outcomes and foster better public engagement by sharing experiences and understanding the needs of community members.

Qualitative EEG abnormalities are common in Autism Spectrum Disorder (ASD) and hypothesized to reflect disrupted excitation/inhibition 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 quantitative EEG measures to alpha oscillations from source-reconstructed data in the EU-AIMS compilation of 267 EEG recordings from children-adolescents and adults with ASD and 209 controls. We analyzed these quantitative measures alongside evaluating for qualitative EEG abnormalities ranging from slowing of activity to epileptiform patterns aiming to replicate the findings from the SPACE-BAMBI study (Bruining et al., 2020). EEG abnormalities were only identified in a few adults and could not be statistically assessed. ASD children-adolescents with EEG abnormalities exhibited lower relative alpha power and lower fE/I compared to children-adolescents without abnormalities; however, the EEG-abnormality scoring did not stratify the behavioral heterogeneity of ASD using clinical measures. Surprisingly, several controls presented with qualitative EEG abnormalities and showed a strikingly similar anatomical distribution of lower fE/I to the one observed in the ASD group, suggesting a shift towards inhibition-dominated network dynamics, in regions associated with altered sensory processing. 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 associated effects on network activity may help understand neurodevelopmental physiological heterogeneity and the difficulties in implementing E/I targeting treatments in unselected cohorts. ### Competing Interest Statement H.B., K.L.-H., and S.-S.P. are shareholders of Aspect Neuroprofiles BV, which develops physiology-informed prognostic measures for neurodevelopmental disorders. K.L.-H. has filed the patent claim (PCT/NL2019/050167) "Method of determining brain activity"; with priority date 16 March 2018. TC has served as a paid consultant to F. Hoffmann-La Roche Ltd. and Servier; and has received royalties from Sage Publications and Guilford Publications. A.E.-A. is a paid consultant for Aspect Neuroprofiles BV. J.B. has been in the past 3 years a consultant to / member of advisory board of / and/or speaker for Takeda, Roche, Medice, Angelini, Neuraxpharm, and Servier. 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. P.G. and J.F.H. are full-time employees of F. Hoffmann - La Roche Ltd. T.B. served in an advisory or consultancy role for eye level, Infectopharm, Medice, Neurim Pharmaceuticals, Oberberg GmbH and Takeda. He received conference support or speaker's fee by Janssen-Cilag, Medice and Takeda. He received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. The rest of the authors have no competing interests to declare. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

BACKGROUND:Autism is accompanied by highly individualized patterns of neurodevelopmental differences in brain anatomy. This variability makes the neuroanatomy of autism inherently difficult to describe at the group level. Here, we examined interindividual neuroanatomical differences using a dimensional approach that decomposed the domains of social communication and interaction (SCI), restricted and repetitive behaviors (RRBs), and atypical sensory processing (ASP) within a neurodiverse study population. Moreover, we aimed to link the resulting neuroanatomical patterns to specific molecular underpinnings. METHODS:Neurodevelopmental differences in cortical thickness (CT) and surface area (SA) were correlated with SCI, RRB, and ASP domain scores by regression of a general linear model in a large neurodiverse sample of 288 autistic individuals and 140 nonautistic individuals, ages 6 to 30 years, recruited within the European Autism Interventions Longitudinal European Autism Project (EU-AIMS LEAP). The domain-specific patterns of neuroanatomical variability were subsequently correlated with cortical gene expression profiles via the Allen Human Brain Atlas. RESULTS:Across groups, behavioral variations in SCI, RRBs, and ASP were associated with interindividual differences in CT and SA in partially non-overlapping frontoparietal, temporal, and occipital networks. These domain-specific imaging patterns were enriched for genes that 1) are differentially expressed in autism, 2) mediate typical brain development, and 3) are associated with specific cortical cell types. Many of these genes were implicated in pathways governing synaptic structure and function. CONCLUSIONS:Our study corroborates the close relationship between neuroanatomical variation and interindividual differences in autism-related symptoms and traits within the general framework of neurodiversity and links domain-specific patterns of neuroanatomical differences to putative molecular underpinnings.

Importance:In the neurotypical brain, regions develop in coordinated patterns, providing a fundamental scaffold for brain function and behavior. Whether altered patterns contribute to clinical profiles in neurodevelopmental conditions, including autism, remains unclear. Objectives:To examine if, in autism, brain regions develop differently in relation to each other and how these differences are associated with molecular/genomic mechanisms and symptomatology. Design, Setting, and Participants:This study was an analysis of one the largest deep-phenotyped, case-control, longitudinal (2 assessments separated by approximately 12-24 months) structural magnetic resonance imaging and cognitive-behavioral autism datasets (EU-AIMS Longitudinal European Autism Project [LEAP]; study dates, February 2014-November 2017) and an out-of-sample validation in the Brain Development Imaging Study (BrainMapASD) independent cohort. Analyses were performed during the 2022 to 2023 period. This multicenter study included autistic and neurotypical children, adolescents, and adults. Autistic participants were included if they had an existing autism diagnosis (DSM-IV/International Statistical Classification of Diseases and Related Health Problems, Tenth Revision or DSM-5 criteria). Autistic participants with co-occurring psychiatric conditions (except psychosis/bipolar disorder) and those taking regular medications were included. Exposures:Neuroanatomy of neurotypical and autistic participants. Main Outcomes and Measures:Intraindividual changes in surface area and cortical thickness over time, analyzed via surface-based morphometrics. Results:A total of 386 individuals in the LEAP cohort (6-31 years at first visit; 214 autistic individuals, mean [SD] age, 17.3 [5.4] years; 154 male [72.0%] and 172 neurotypical individuals, mean [SD] age, 16.35 [5.7] years; 108 male [62.8%]) and 146 individuals in the BrainMapASD cohort (11-18 years at first visit; 49 autistic individuals, mean [SD] age, 14.31 [2.4] years; 42 male [85.7%] and 97 neurotypical individuals, mean [SD] age, 14.10 [2.5] years; 58 male [59.8%]). Maturational between-group differences in cortical thickness and surface area were established that were mostly driven by sensorimotor regions (eg, across features, absolute loadings for early visual cortex ranged from 0.07 to 0.11, whereas absolute loadings for dorsolateral prefrontal cortex ranged from 0.005 to 0.06). Neurodevelopmental differences were transcriptomically enriched for genes expressed in several cell types and during various neurodevelopmental stages, and autism candidate genes (eg, downregulated genes in autism, including those regulating synaptic transmission; enrichment odds ratio =3.7; P =2.6 × -10). A more neurotypical, less autismlike maturational profile was associated with fewer social difficulties and more typical sensory processing (false discovery rate P <.05; Pearson r ≥0.17). Results were replicated in the independently collected BrainMapASD cohort. Conclusions and Relevance:Results of this case-control study suggest that the coordinated development of brain regions was altered in autism, involved a complex interplay of temporally sensitive molecular mechanisms, and may be associated with both lower-order (eg, sensory) and higher-order (eg, social) clinical features of autism. Thus, examining maturational patterns may provide an analytic framework to study the neurobiological origins of clinical profiles in neurodevelopmental/mental health conditions.

Objective We aim to investigate the relationship between the core symptoms of autism, anxiety levels, and attention deficit hyperactivity disorder (ADHD) traits, and a non-autism-specific, neurophysiological metric, the Delta-Beta phase-amplitude coupling (PAC), extracted from the resting-state EEG for autistic and non-autistic populations across three different age groups (children, adolescents, and adults). Methods We analyze the eyes-open resting-state EEG of 371 individuals. We applied a phase de-biasing PAC algorithm expected to result in a more accurate PAC estimate than other PAC methodologies available in the literature. Results In the adult group, we found a significant increase of the delta-beta PAC in the autistic subgroup who met the Autism Diagnostic Observation Schedule-2 (ADOS-2) Autism Diagnostic Interview-Revised (ADR-R) ADOS-2/ADI-R threshold compared to non-autistic individuals. The differences seem age-specific since we found no statistically significant differences in the children and adolescent populations. Moreover, we found a significant positive correlation with the restricted and repetitive behaviours score of the ADOS-2 diagnostic instrument and with ADHD hyperactivity/impulsivity in the entire autistic cohort. Conclusions The neurophysiological differences we found only in the autistic individuals that meet the thresholds also point out the need for future studies that look for autistic neurodiverse subgroups beyond age. Significance The delta-beta debiasing PAC (dPAC) may potentially serve as a severity biomarker in the autistic population.

INTRODUCTION:Autism is a common neurodevelopmental condition with a complex genetic aetiology that includes contributions from monogenic and polygenic factors. Many autistic people have unmet healthcare needs that could be served by genomics-informed research and clinical trials. The primary aim of the European Autism GEnomics Registry (EAGER) is to establish a registry of participants with a diagnosis of autism or an associated rare genetic condition who have undergone whole-genome sequencing. The registry can facilitate recruitment for future clinical trials and research studies, based on genetic, clinical and phenotypic profiles, as well as participant preferences. The secondary aim of EAGER is to investigate the association between mental and physical health characteristics and participants' genetic profiles. METHODS AND ANALYSIS:EAGER is a European multisite cohort study and registry and is part of the AIMS-2-TRIALS consortium. EAGER was developed with input from the AIMS-2-TRIALS Autism Representatives and representatives from the rare genetic conditions community. 1500 participants with a diagnosis of autism or an associated rare genetic condition will be recruited at 13 sites across 8 countries. Participants will be given a blood or saliva sample for whole-genome sequencing and answer a series of online questionnaires. Participants may also consent to the study to access pre-existing clinical data. Participants will be added to the EAGER registry and data will be shared externally through established AIMS-2-TRIALS mechanisms. ETHICS AND DISSEMINATION:To date, EAGER has received full ethical approval for 11 out of the 13 sites in the UK (REC 23/SC/0022), Germany (S-375/2023), Portugal (CE-085/2023), Spain (HCB/2023/0038, PIC-164-22), Sweden (Dnr 2023-06737-01), Ireland (230907) and Italy (CET_62/2023, CEL-IRCCS OASI/24-01-2024/EM01, EM 2024-13/1032 EAGER). Findings will be disseminated via scientific publications and conferences but also beyond to participants and the wider community (eg, the AIMS-2-TRIALS website, stakeholder meetings, newsletters).

Up to 50% of autistic people experience co-occurring anxiety, which significantly impacts their quality of life. Consequently, developing new interventions (and/ or adapting existing ones) that improve anxiety has been indicated as a priority for clinical research and practice by the autistic community. Despite this, there are very few effective, evidence-based therapies available to autistic people that target anxiety; and those that are available (e.g., autism adapted Cognitive Behavioural Therapy; CBT) can be challenging to access. Thus, the current study will provide an early-stage proof of concept for the feasibility and acceptability of a novel app-based therapeutic approach that has been developed with, and adapted for, autistic people to support them in managing anxiety using UK National Institute for Health and Care Excellence (NICE) recommended adapted CBT approaches. This paper describes the design and methodology of an ethically approved (22/LO/0291) ongoing non-randomised pilot trial that aims to enrol approximately 100 participants aged ≥16-years with an existing autism diagnosis and mild-to-severe self-reported anxiety symptoms (trial registration NCT05302167). Participants will be invited to engage with a self-guided app-based intervention—‘Molehill Mountain’. Primary (Generalised Anxiety Disorder Assessment, Hospital Anxiety and Depression Scale) and secondary outcomes (medication/ service use and Goal Attainment Scaling) will be assessed at baseline (Week 2 +/- 2), endpoint (Week 15 +/- 2) and three follow-ups (Weeks 24, 32 and 41 +/- 4). Participants will also be invited to complete an app acceptability survey/ interview at the study endpoint. Analyses will address: 1) app acceptability/ useability and feasibility (via survey/ interview and app usage data); and 2) target population, performance of outcome measures and ideal timing/ duration of intervention (via primary/ secondary outcome measures and survey/ interview)–with both objectives further informed by a dedicated stakeholder advisory group. The evidence from this study will inform the future optimisation and implementation of Molehill Mountain in a randomised-controlled trial, to provide a novel tool that can be accessed easily by autistic adults and may improve mental health outcomes.

A paradigm shift in research culture is required to ease perceived tensions between autistic people and the biomedical research community. As a group of autistic and non-autistic scientists and stakeholders, we contend that through participatory research, we can reject a deficit-based conceptualization of autism while building a shared vision for a neurodiversity-affirmative biomedical research paradigm.