Molecular Autism

Autism Spectrum Disorder (ASD) is a heterogenous condition that has no biologically relevant subtypes yet. Here, we utilized a multidimensional approach considering social deficits in ASD alongside negative valence and empathy dysfunction to distinguish ASD from Neurotypicals (NT) and to generate ASD subtypes using machine learning approaches. 114 subjects were analyzed, with 70 being NT and 44 ASD, all male with an IQ greater than 70, with 5 domains of personality (NEO-PI-r) and Reading the Mind the Eyes Test (RMET) scores included in the main classifier. We then used a multitude of behavioral (such as IQ, Broader Autism Phenotype, Autism Quotient, Interpersonal Reactivity Index) and clinical measures such as Autism Diagnostic Interview-Revised (ADI-R) alongside biological methods including DNA methylation of OXTR gene and resting-state functional connectivity (rsFC) to validate the putative subtypes. 30 ASD who received IN-OXT in a randomized, placebo-controlled, within-subject design and 17 new NT were part of the rs-FC analysis. A random forest tree algorithm was used to classify NT and ASD and Shapley Additive Explanation Values were used to describe the model and to cluster ASD subtypes using K-Means clustering. Three subtypes were generated with two of them being highly distinctive in behavioral and brain functional traits. One subtype named NASA (or Negative Affect and Social Aloofness) was characterized by high Neuroticism and Low warmth alongside lower rsFC between networks involved in social cognition, self-awareness, and sensory processing, such as Superior Temporal Sulcus and Sensorimotor Network; or ACC/Insula with visual cortex, Posterior Cingulate Cortex and visual cortex. The second subtype NADR (Neurocognitive and Affect Dysregulation with Resistance to Change) was characterized by higher DNA methylation of OXTR, hyperconnectivity between default mode network, reward areas and inferior frontal and fusiform networks. NADR has more cognitive difficulties and higher ADI-R scores as well as higher Neuroticism, higher personal distress, higher rigidity and lower openness. In a mixed model analysis, we found that IN-OXT in a dose dependent manner impacted NASA subtype by modulating rsFC between PCC and cerebellum and between Brainstem/Cerebellum and Parietal cortex to probably enhance social cognition and to reduce negative valence in this subtype.

Rare pathogenic variants in many genes contribute to neurodevelopmental disorders (NDDs), including intellectual disability and/or global developmental delay (ID), autism spectrum disorder (ASD), epilepsy (EP), and cerebral palsy (CP). These conditions frequently co-occur and share genetic etiologies, yet the broader phenotypic eYects and the extent of shared versus distinct genetic influences remain unclear. Here, we adopt a cross-disorder framework to examine NDD genes across four diagnostic categories, characterize gene-associated phenotypic profiles, and identify convergent pathways that help refine how pathogenic variants in these genes shapes clinical outcomes. Using a discovery cohort of 8,973 probands with disease-causing variants in 263 NDD genes, we performed phenotype-based gene clustering and identified six distinct gene clusters. These clusters reveal structured patterns of genetic overlap, showing that subsets of NDD genes preferentially contribute to specific disorder combinations of ID, ASD, EP, and CP. The largest gene cluster was characterized by ID, whereas the other five included one enriched for ASD and ID, two for EP and ID and two for CP and ID, each with significantly diYering frequencies. In an independent validation cohort of 19,704 probands, five of the six clusters were replicated. Gene Ontology enrichment analyses revealed distinct biological processes in each cluster, suggesting that coherent molecular mechanisms underlie the diYering NDD diagnostic profiles. Together these findings demonstrate that NDD genes fall into coherent clusters that consistently map onto characteristic phenotype profiles, providing a framework to inform future therapeutic strategies and support early prognostication for individuals with pathogenic variants in NDD genes.

Very preterm infants (<30 weeks gestation) are at elevated risk for neurodevelopmental and social-behavioral challenges. DNA methylation (DNAm) may provide a biological link between preterm birth and later behavioral outcomes. We examined associations between DNAm profiles at neonatal intensive care unit (NICU) discharge and at age 5 with Social Responsiveness Scale (SRS) scores which measure social communication, social interaction, and repetitive behaviors at age 5, including sex-specific effects, in the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) Study. Epigenome-wide buccal DNAm was profiled at NICU discharge (n=218) and at 5 years (n=188). We identified 38 neonatal and 6 age-5 CpG sites associated with SRS scores (all q<0.05) using epigenome-wide association studies (EWAS) at each time point. Several CpGs mapped to genes involved in neurodevelopment including TCF4, KLC4, CAP2, PTDSS1, ADAM12, SENP1, CHN2, SH3D19, and ITGA1, with sex-specific effects observed for CpGs in CAMTA1 and GABBR1. Enriched pathways included neurodevelopment, cytoskeletal regulation, stress-response, and metabolic processes. DNAm patterns during early life, particularly the neonatal period, were associated with social-behavioral development in very preterm children. Findings in key genes such as TCF4 and CAMTA1 highlight potential epigenetic mechanisms linking early-life biology to later behavioral challenges.

Low-frequency variants (LFVs), defined by minor allele frequencies (MAF) of 1-5%, occupy the gap between common and rare variants in both frequency and effect size. The conventional genome-wide association study (GWAS) significance threshold (5x10-) is overly conservative for LFVs, which account for more than 25% of variants in GWAS. This limitation may obscure meaningful associations in highly heritable yet genetically complex disorders such as autism spectrum disorder (ASD). We hypothesize that the scarcity of significant LFVs in ASD GWAS reflects statistical constraints rather than a true lack of association. To address this, we derived a MAF-specific genome-wide significance threshold using linkage disequilibrium-informed simulations applied to ASD GWAS summary statistics, identifying 2.03x10- as optimal. Applying this threshold revealed three novel LFVs mapping to zinc finger proteins (ZNF420, ZNF781) and known ASD-related genes (KMT2E, PRKDC, MCM4). Enrichment analyses suggested their function in nervous system development and gene regulation. Our findings highlight the contribution of LFVs to ASD risk and underscore the importance of frequency-aware association strategies.

Autism spectrum disorder (ASD; MIM 209850) is reported to vary globally from 0.01% in East Asian populations to 4.36% in certain Australian cohorts. Despite high heritability estimates (61-94%), the genetic architecture underlying ASD susceptibility remains poorly characterized across diverse populations, as most genomic studies have initially focused on individuals of European ancestry. To investigate ancestry-specific genetic contributions to ASD, we analyzed whole-genome sequencing data from three independent ASD cohorts. We identified admixed ASD probands (n = 1 033) and ancestry-matched controls (n = 1 033) and performed admixture mapping (AM). AM using five continental reference populations (European, African, East Asian, South Asian, and Native American) identified five ancestry-specific ASD-susceptibility loci, including one African-related locus at 1p21.2 near S1PR1 and four Native American-associated loci at chromosome 11q13.4. Three of these latter loci were contiguous and encompassed genes previously implicated in ASD, notably SHANK2 and DHCR7, with fine-mapping identifying a significantly associated variant between the two genes (rs77695321; P = 1.52 x 10-). The fourth Native American-associated signal at 11q13.4 overlapped the folate receptor genes FOLR1 and FOLR3, with fine-mapping identifying a genome-wide significant variant (rs7950807; P = 5.21 x 10-). A secondary admixture mapping analysis restricted to Latin American individuals, incorporating 6 487 Brazilian controls, identified 16 additional ancestry-specific loci across seven genomic regions.

BackgroundThe present study examines the link between DNA methylation at the nerve growth factor-induced protein A (NGFI-A) binding domain of the NR3C1 1F promoter and later cognitive functions in children from families living in disadvantaged psychosocial conditions. MethodsParticipants were 132 children who took part in a Swiss Parents as Teachers (PAT) randomized controlled trial (72 in the intervention group, 60 in the control group). DNA methylation was quantified from saliva samples collected at age three using sodium bisulfite next-generation sequencing (NGS). Cognitive functions were assessed at age five using the SON-R 2.5-7 Intelligence Test. Results(a) DNA methylation at age three predicted lower IQ at age five through increased concentration problems; (b) participation in the three-year PAT program predicted lower methylation levels at the end of the intervention; and (c) early life stressors predicted lower IQ through increased methylation and concentration problems with descriptively stronger effects in the control group. ConclusionsThese findings demonstrate a link between early DNA methylation at the NGFI-A binding site of the NR3C1 1F promoter and later cognitive functions in children and highlight the role of early life stressors and the PAT intervention in shaping these associations.

BackgroundThe influence of genetic and environmental factors, especially during early development, is critical in the pathogenesis of autism. Maternal autoantibodies that recognize specific fetal brain proteins can be strong predictors of autism risk. These antibodies cross the placenta and bind to their target antigens, which play critical roles in neurodevelopment, thereby increasing autism risk. This etiologically defined subtype is now referred to as Maternal Autoantibody-Related Autism (MARA). The newly developed MAR-AutismTM test is an indirect multi-ELISA assay designed to detect specific combinations of these maternal antibodies, which strongly predicts increased autism risk. ObjectiveTranslation of the indirect ELISA assays for the eight relevant antibodies (LDH-A, LDH-B, GDA, STIP1, CRMP1, CRMP2, NSE and YBOX) from an academic laboratory to a clinical development laboratory for optimization and determination of the analytical performance of the individual antibody assays. MethodsFeasibility assays were transferred from the academic laboratory and their performance confirmed prior to optimization of all steps from target protein production to preliminary threshold determination. Validation to rigorous standards was conducted. The ELISAs are qualitative assays using an internal continuous response and a cutoff to define positivity and negativity for each analyte. Analytical performance metrics of linearity, sensitivity, specificity, precision, and stability were determined by standard testing methodologies. ResultsThe optimized ELISAs all performed at acceptable standards for analytical performance. All of the assays except one were demonstrated to be linear upon dilution with buffer and with non-reactive plasma, however, recovery was overestimated with buffer diluent. The precision profile results demonstrated that the Lower Limit of Quantification (LOQ) was greater than the Limit of Detection (LOD) and below the preliminary thresholds determined from a general population cohort distribution. Precision studies showed coefficients of variation less than 15% with two minor exceptions. Common interfering substances, apart from whole human IgG, did not affect assay performance. The microtiter assay plates were stable for at least 6 months without significant drift. ConclusionOverall, the individual antibody assays demonstrated high sensitivity, specificity, and robustness sufficient to enable extension to clinical validation. These assays enable evaluation of specific antibody combinations that were previously reported to strongly and specifically correlate with autism risk, particularly in settings of suspected diagnosis or in families with an older sibling with a confirmed autism diagnosis.

BackgroundParental genetics matters for childrens behavioural difficulties, but the extent to which this is due to direct genetic transmission versus environmentally mediated indirect genetic effects remains unclear. MethodsWe studied eight European birth cohorts with over 33,000 family-based trio samples. We analysed polygenic scores (PGSs) for 13 mental health and neurodevelopmental conditions and their composite indices (PC1 and mean) representing general neuropsychiatric liabilities, as well as educational attainment (EA) and alcohol and cigarette use, from children (PGSc), mothers (PGSm), and fathers. Child internalising, externalising, and total difficulties reported by mothers and/or fathers were examined at preschool and school ages. We then conducted multivariate meta-analyses to combine cohort-level results. FindingsWe observed several direct genetic effects on externalising difficulties, while indirect genetic influences were mainly identified for internalising difficulties. Specifically, child PGSs for attention-deficit/hyperactivity disorder (ADHD) and EA predicted higher and lower levels, respectively, of child externalising and total difficulties (all pFDR<0{middle dot}001; for school-aged externalising difficulties, PGSc-ADHD: {beta}=0{middle dot}121 [95% CI 0{middle dot}091 to 0{middle dot}151], pFDR<0{middle dot}0001; PGSc-EA: {beta}=-0{middle dot}095 [95% CI -0{middle dot}127 to -0{middle dot}063], pFDR<0{middle dot}0001), whereas maternal PGSs for major depressive disorder (MDD) and general neuropsychiatric liabilities were associated with internalising and total difficulties across parental raters and child ages (all pFDR<0{middle dot}05; for school-aged internalising difficulties, PGSm-MDD: {beta}=0{middle dot}049 [95% CI 0{middle dot}017 to 0{middle dot}081], pFDR=0{middle dot}016; PGSm-PC1: {beta}=0{middle dot}056 [95% CI 0{middle dot}022 to 0{middle dot}091], pFDR=0{middle dot}011). No statistically significant effects from paternal PGSs were identified. InterpretationIn this multi-cohort study, findings across multiple traits, raters, and ages supported several direct genetic effects of ADHD and EA on child externalising difficulties and indirect genetic effects on internalising difficulties, especially maternal depression and general neuropsychiatric liabilities. These suggest that child internalising difficulties are not solely driven by direct genetic transmission. More comprehensive research is needed to better understand the mechanisms involved, and ultimately how to ameliorate child behavioural difficulties. FundingEU, ERC, RCN, RCF, UKRI, SERI, DFG Research in contextO_ST_ABSEvidence before this studyC_ST_ABSIndirect genetic effects (IGEs) refer to the influence of parental genotypes on offspring outcomes beyond direct genetic effects (DGEs), for example via environmental pathways. While IGEs on offspring cognitive traits are well-established for educational attainment, evidence for IGEs of parental liabilities to mental health and neurodevelopmental conditions remains limited. To assess the current state of evidence, we conducted a systematic search of published studies applying trio-based polygenic score (PGS) designs to child and adolescent mental health outcomes. We identified 141 primary studies in MEDLINE, Embase, PsycInfo, and Web of Science, by 6 March 2025, after removing duplicates; following screening, 12 studies met inclusion criteria (see supplement for a full description including results). Ten out of the 12 studies focused on externalising outcomes, with little or inconsistent support for IGEs. When observed, IGEs were mainly driven by maternal liabilities to autism, educational attainment, and cognitive performance on child outcomes. The current evidence was too limited and heterogeneous to synthesize findings quantitatively, therefore a qualitative synthesis was conducted. Many studies were statistically underpowered, and the observed IGEs were in all cases sample-specific. There were no published multi-cohort studies. Added value of this studyWe integrated information across over 33,000 mother-father-child trios from eight European cohorts, investigating 18 PGSs from parents and children, using maternal and paternal ratings of offsprings internalising, externalising, and total difficulties as outcomes at both preschool and school age. We mainly observed DGEs on externalising difficulties, consistent with previous studies. Some evidence of IGEs was found for internalising and total difficulties. IGEs were often found to be maternally driven, with the most robust evidence across ages and raters emerging for maternal depression and general neuropsychiatric liabilities. Implications of all the available evidenceThe current evidence suggests that childrens behavioural difficulties, especially internalising difficulties, may be partly driven by the environment shaped by maternal neuropsychiatric liabilities. Ours and previous findings highlight a pressing need for more comprehensive studies across different cohorts, raters, outcomes, and time points to understand the true extent of IGEs in the intergenerational transmission of mental health.

IntroductionResearch has shown that social and physical stressors of early-life adversity (ELA) can negatively affect long-term health trajectories. Despite differences in types of ELA exposure, previous studies have identified common health-related outcomes in adults who had experienced less favourable conditions during developmentally sensitive periods. This meta-analysis investigates the potential role of DNA methylation in mediating these adverse health trajectories by identifying common biological signatures across cohorts with distinct adversity exposures and environmental backgrounds. Materials and MethodsDNA methylation data from previously published studies was used to perform a meta-analysis on 227 individuals across three cohorts. These include the EpiPath cohort consisting of adults who were exposed early institutional care, ImmunoTwin cohort consisting of adversity discordant monozygotic twin pairs and lastly a cohort of young children exposed to early institutional care. ResultsDNA methylation analysis across the three cohorts revealed differential methylation at CpG loci associated with 15 genes common to all cohorts. These genes are involved in neuronal development, chromatin remodeling and metabolism. Pathway enrichment analysis of the combined dataset showed potential associations with oxytocin signalling, regulation of nervous system development, and calcium signalling in relation to the later-life phenotype of the adversity exposed individuals. In addition, a poly-epigenetic score was developed by identifying a subset of 200 differentially methylated CpG sites through PLS-DA analysis with the combined beta matrix of these cohorts. ConclusionThis study highlights the long-term impact of adversity by identifying common DNA methylation signatures of negative life experiences across three cohorts. The development of a poly-epigenetic score represents the first steps towards identifying group differences by combining weighted methylation values for CpG sites of interest. This method illustrates the potential to track changes in individuals across long-term studies that may benefit research in lifelong healthoutcomes.

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a common heritable neurodevelopmental disorder, affecting [~]7 million children (11.4%) in the U.S. However, ADHDs underlying genetic architecture remains largely unknown. Transcriptome-wide association studies (TWAS), which integrate expression quantitative trait loci (eQTL) and GWAS summary data, can identify differentially expressed risk genes underlying complex phenotypes. Here we conduct a TWAS of ADHD using expression data from multiple brain tissues to improve understanding of the complex genetic architecture underlying this psychopathology. MethodsWe applied the TWAS framework OTTERS to train multiple gene expression imputation models using cis-eQTL summary statistics from MetaBrain for three brain regions: cortex (n=2,683), basal ganglia (n=208), and cerebellum (n=492), and GWAS summary statistics from the most recent meta-analysis of ADHD (n=225,534; case fraction =0.17). We further conducted fine-mapping, colocalization analysis, and functional enrichment analysis. ResultsWe identified 29 significant TWAS risk genes for ADHD (11 in cortex, 4 in basal ganglia, and 14 in cerebellum). Six genes appear novel for ADHD (MPL, C1orf210, MDFIC, NKX2-2, FAM183A, HIGD1A) while four genes were previously implicated in autism spectrum disorder (XRN2, KIZ, NKX2-4, NKX2-2). Pathway analysis indicated cortex and basal ganglia were enriched for neurodevelopmental pathways and regulation of cell development, and the protein-protein interaction network was statistically significant (p=1.12E-04). ConclusionThis multi-tissue TWAS refines the genetic architecture of ADHD by identifying genes whose genetically regulated expression is associated with risk, including six candidates not previously linked to ADHD. Together, these findings provide novel insights for potential targets in translational research and drug discovery.

BackgroundOrganization, time management, and planning (OTMP) difficulties are associated with academic underachievement. OTMP skills training programs are effective in reducing OTMP deficits and improving academic performance. A randomized controlled trial of Homework, Organization, and Planning Skills (HOPS) for students ages 11-14 (1) found it to be effective with medium to large effects. In that study, HOPS was provided by counselors employed by the research team. This study is a replication examining HOPS under more authentic conditions when providers are employed by schools serving enrolled students. The primary aim is to evaluate HOPS offered by school providers in relation to treatment-as-usual/waitlist (TAU/WL). To respond to limited school resources post-COVID-19, HOPS is also provided by research team members, creating the opportunity to replicate the findings from the prior trial (1) and explore differential effectiveness when HOPS is implemented by school vs. research providers. MethodsStudents in about 30 schools serving students ages 11-14 will be enrolled. Schools are randomly assigned to HOPS vs. TAU/WL on a 2:1 ratio. Students assigned to HOPS schools are randomly assigned to a school vs. research provider on a 1:1 basis. Providers receive two hours of training and additional assistance on request. Child outcomes related to OTMP skills, homework, and academic performance are assessed at post-treatment, 6-month (from baseline) follow-up, and 12-month follow-up. HOPS sessions are video recorded for fidelity coding. Potential effect modifiers include student ADHD, oppositional defiant, and internalizing symptoms, and family socioeconomic level. Analyses will use mixed effects modeling. The goal of the study is to enroll 135 participants, yielding a minimal detectable effect size of 0.50, within the expected range based on prior research. DiscussionThe study is unique in examining intervention implementation and effectiveness when intervention is provided under authentic practice conditions. Trial RegistrationThis study was registered with clinicaltrials.gov (NCT04465708).

ObjectiveTo analyze the structure of mental disorders in children in the outpatient practice of a specialized mental health center for optimization of care organization for this patient category. MethodsA retrospective analysis of medical records of 23 children (out of 44 patients) at the Insight Mental Health Center (Dushanbe, Tajikistan) was conducted for the period from December 9, 2025, to January 8, 2026. Diagnosis was performed according to ICD-10 criteria using standardized instruments: M-CHAT-R, ADOS-2, and ADI-R for autism spectrum disorder (ASD); SNAP-IV for attention deficit hyperactivity disorder (ADHD); CGI; and pediatric versions of PHQ and GAD. ResultsChildren accounted for 52% of all patients. Primary school-age children (7-12 years) predominated at 43.5%. Disorders of psychological development (F80-F89) dominated the nosological structure at 82.6%, with ASD comprising 56.5%. ADHD was diagnosed in 30.4% of cases. Comorbidity was registered in 47.7% of patients. ConclusionThe structure of pediatric psychiatric pathology is characterized by a predominance of developmental disorders and high comorbidity levels, justifying the need for a multidisciplinary approach.

ObjectiveAlthough mental health and healthy lifestyle interventions are associated with functional outcomes in adolescence, the extent to which particular lifestyle factors explain relationships between mental health and outcome are unclear. Here we examined mediating effects of lifestyle factors on relationships between mental health and two functional outcomes measured 2-3 years later as well as the moderating effect of environmental risk factors on mediation strength in early adolescence. MethodsThis study analyzed data from 3 waves of the Adolescent Brain Cognitive Development Study (ages 10-11, 11-12, and 12-13). Mediating effects of sleep quality, screen time, physical activity and Mediterranean diet on the relationships between depression, anxiety, psychotic-like experience (PLE) distress, and total problems with two subsequent functional outcomes (academic functioning and social problems) were examined. Secondary analyses included environmental factors as moderators. ResultsSleep quality mediated 18.5%, 36.3%, 8.3%, and 3.4% of the relationships between depression, anxiety, PLE distress and total problems with academic functioning, respectively. Screen time was the second strongest mediating factor. For social problems, only sleep quality showed > 3% mediation (19.6% - 23.3%). Mediating effects of sleep and screen time on academic functioning decreased as financial adversity increased. Conversely, mediating effects of sleep quality on social problems increased with worsening family conflict, financial adversity, and school environment. ConclusionsThese results suggest that healthy lifestyle factors (in particular sleep quality) may partially explain the associations between mental health and functioning in adolescents and suggest that these effects are modulated by environmental factors. These results may have important implications for future intervention studies.

BackgroundLight plays a critical role in mental health, as the primary input to the circadian system, which regulates mood, energy, and the sleep-wake cycle. Altered light sensitivity is a potential mechanism in circadian-associated mental disorders. MethodsActigraphy-derived sleep, physical activity, and circadian rhythm correlates of the pupillary light reflex were explored in young people with emerging mental disorders. Participants were 27 healthy controls (Mean age=25.67 {+/-} 2.83, 52% female) and 155 young people from the Neurobiology Youth Follow-up Study (Mean age=25.48 {+/-} 5.65; 60% female), recruited from an early intervention mental health service. 32% of the latter group were re-assessed over 12 months. Pupil constriction, average and maximal constriction velocity, and constriction latency were recorded by the PLR-3000 monocular pupillometer in response to dim ([~]10 lux) and bright ([~]1500 lux) pulses. ResultsCompared to healthy controls, young people with emerging mental disorders had a smaller change in pupil diameter (p=0.037) and a slower maximal constriction velocity (p=0.018) in response to dim light. In the full sample, decreased dim light sensitivity was correlated with later timing of actigraphy-derived sleep midpoint. Within the clinical cases, increased genetic risk for bipolar disorder was correlated with increased dim light sensitivity, and higher insomnia clinical scores were correlated with decreased dim light sensitivity. Pupillometry measures were stable across time and seasons. ConclusionAltered light sensitivity may be associated with the emergence of mood disorder in young people and with altered sleep-wake timing.

Menieres disease (MD) is a chronic inner ear disorder characterized by recurrent vertigo, fluctuating sensorineural hearing loss, and tinnitus. Despite these distinctive symptoms, its etiology remains poorly understood. We performed a genome-wide meta-analysis of 8,969 cases and 1,962,542 controls across five large biobanks, identifying five independent genome-wide significant loci and estimating an observed-scale SNP heritability of 7% (SE 0.8%), consistent with a modest but significant genetic contribution to MD risk. Fine-mapping and integrative functional analyses implicate two convergent biological processes - developmental regulation of the inner ear, involving EYA4, EYA1, and LMO4 - and retinoic acid metabolism, with loci near CYP26A1/C1 and ALDH1A2 suggesting disrupted RA signaling in sensory and fluid-pressure homeostasis. These developmental regulator genes are robustly expressed in fetal and adult human inner ear cell types, supporting a model in which altered developmental programs predispose to adult vestibular and auditory dysfunction. Phenome-wide and genetic correlation analyses further reveal shared genetic architecture between MD and related traits, including vertigo, tinnitus, hearing loss, migraine, and sleep apnea, situating MD within a broader spectrum of sensory and neurological disorders. Collectively, these findings establish a genetic framework for Menieres disease risk and implicate developmental regulators and retinoic acid signaling as key contributing pathways.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms (Activity-Merged, Activity-Only). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

BackgroundTo clarify the working mechanisms of psychotherapy for obsessive-compulsive disorder (OCD), we studied the neural effects of two psychotherapies: cognitive behavioral therapy with exposure and response prevention (CBT-ERP) and inference-based cognitive behavioral therapy (I-CBT). MethodsFifty-five individuals with OCD completed an emotional processing task during fMRI before and after 20 weekly psychotherapy sessions, using general fear and OCD-related visual stimuli. Forty-two healthy controls performed the task once. We used Bayesian region-of-interest analyses to assess changes in brain activation in prefrontal, limbic, sensory, subcortical, and visual areas, and their association with symptom improvement. ResultsAfter treatment, the CBT-ERP group (N=28) showed strong credible evidence for decreased activation across all brain regions during fear (but not OCD) versus neutral stimuli, especially in treatment responders. Conversely, the I-CBT group (N=27) showed increased activation during fear versus neutral stimuli in the precentral gyrus and lateral occipital cortex (LOC), which correlated with symptom improvement. A similar but weaker pattern was observed for OCD-related stimuli. Across all ROIs, baseline fear-related activity was associated with symptom improvement in CBT-ERP, while lower baseline activity was associated with improvement in I-CBT in, amongst others, the precentral gyrus and dorsolateral prefrontal cortex. Lower baseline LOC activation during OCD-related stimuli was linked to symptom improvement after both psychotherapies. ConclusionsThe results support CBT-ERPs mechanism of fear reduction and I-CBTs mechanism of sensory engagement. Visual brain activity during emotional processing may predict treatment response across psychotherapies.

Backgroundspeech carries cues to variation in mental state in schizophrenia spectrum disorders/psychotic disorders, typically indexed with clinician-rated scales such as the PANSS. Progress in the automation of speech-based symptom modelling has been constrained by data scale and the underrepresentation of low-resource languages. In this study, we aggregate multi-center recordings to assemble a large corpus and assess symptom-prediction models at scale, to enable more objective and efficient assessments and the early detection of relapse-related signals from speech. MethodsWe compiled data from 453 patients with schizophrenia spectrum disorders, recruited from ten global sites, and clipped their speech recordings into 6,664 segments. Across three feature sets, acoustic-prosodic profile, pretrained multilingual embeddings, and their concatenation, we compared 16 algorithms to predict eight relapse-related PANSS items, including three positive (P1, P2, P3), three negative (N1, N4, N6), and two general (G5, G9) items, on speaker-disjoint splits (80% train, 10% test, and 10% validation). Performance was assessed by root-mean-squared-error (RMSE) at both segment and participant (median aggregation) levels. Best model per item underwent bias checks for age, sex, education, and symptom severity. OutcomesBest-performing models predicted symptoms with prediction errors of 1{middle dot}5 PANSS points or lower: P1 1{middle dot}494/1{middle dot}527, P2 1{middle dot}318/1{middle dot}107, P3 1{middle dot}407/1{middle dot}542, N1 1{middle dot}029/1{middle dot}030, N4 1{middle dot}452/1{middle dot}430, N6 0{middle dot}860/0{middle dot}855, G5 0{middle dot}850/0{middle dot}882, G9 1{middle dot}213/1{middle dot}282 (segment/participant). Performance of the pretrained multilingual embeddings surpassed acoustic-prosodic features and their concatenation. Results were comparable in low-resource languages (e.g., Czech). We found no bias by age, sex, or education, aside from reduced N4 accuracy in males; but performance degraded with higher symptom severity. InterpretationSpeech can support automatic assessment of schizophrenia symptoms using pretrained multilingual embeddings, even without the use of transcripts. Such models show promise as clinically meaningful, efficient, and low-burden tools for real-time monitoring of symptom trajectories. FundingEU Horizon research and innovation programme. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAutomatic assessment of disease severity is a key issue in schizophrenia research, for which spontaneous speech offers a cost-effective, automatable solution. To evaluate existing evidence for speech-based symptom assessment, two reviewers (RHe, MK) searched PubMed, IEEE Xplore, arXiv, bioRxiv, and medRxiv for publications from inception to Aug 25, 2025, using the terms: ("symptom" OR "PANSS" OR "Positive and Negative Syndrome Scale") AND ("psychosis" OR "schizophrenia") AND ("language" OR "speech" OR "spontaneous speech") AND ("prediction" OR "machine learning" OR "deep learning" OR "algorithm" OR "neural network" OR "AI" OR "artificial intelligence"). Fourteen studies on symptom-level modelling were identified. Ten studies dichotomized clinical scores (e.g., PANSS) into low vs high for classification: five used conventional ML (e.g., random forests) and five used neural networks, with F1 scores ranging from 0{middle dot}60-0{middle dot}85. The remaining four studies, and two of the ten studies as mentioned above, modelled raw scores directly as regression tasks. Two relied solely on conventional regressors and the rest used neural networks, with errors from 0{middle dot}487 for single items (scale 1-7) to 8{middle dot}04 for summed scores (scale 18-126). All studies used free speech for elicitation, except one study, which used a reading task. Three studies incorporated additional tasks, such as picture description and immediate recall. None were multilingual: nine were in English, three in Chinese, one in Swiss German, and one in Brazilian Portuguese. Features spanned a wide range, including acoustic-prosodic profiles, morpho-syntactic structure, semantic organization, pragmatics (including sentiments), and even visual features capturing movement during talking. Representations from pretrained language models were also widely employed. Sample sizes (counting patients with schizophrenia) were generally small: eleven studies enrolled <50 patients, one had 65, and only two exceeded 100 patients. Some increased their effective sample size via multiple recordings per patient or by adding healthy controls and/or patients with other psychiatric disorders (e.g., depression). Added value of this studyTo our knowledge, this is the first multilingual, speech-based study modelling schizophrenia symptom severity with machine learning approach, and it includes the largest cohort of patients with schizophrenia to date. We further increased effective sample size by using diverse elicitation tasks and segmenting recordings into clips. This multilingual corpus empowers the usage of complex models and supports transfer learning from high-resource languages (e.g., English) to low-resource ones (e.g., Czech). For each of eight selected relapse-related PANSS items, the best audio-only models achieved RMSE < 1{middle dot}5, underscoring clinical relevance. We assessed potential biases: no effects were found for age, sex, or education (except poorer N4 performance in males), though performance declined at higher symptom severity. Trained models are released for use. Implications of all the available evidenceWe show that speech is a powerful signal for automatic assessment of schizophrenia symptom severity and holds promise for relapse prediction, even without transcripts. The approach readily extends to incorporate textual features (from manual or automatic transcripts) and more advanced models. Prospective studies with repeated recordings across relapse episodes are needed to validate the utility of our models on relapse prediction, for the sake of supporting precision psychiatry while reducing clinician burden.

A significant proportion of individuals with suspected genetic developmental and epileptic encephalopathies (DEEs) remain unsolved following whole genome sequencing (WGS). We screened individuals who received WGS analyses at Genomic Medicine Centre Karolinska for Rare Diseases for biallelic RNU2-2 variants. Deep phenotyping was performed and phenotypic traits were transcribed to their corresponding Human Phenotype Ontology (HPO) term. HPO terms were used to generate pairwise phenotypic similarity scores and assess for significant phenotypic enrichment in the RNU2-2 sub-cohort. RNA sequencing analyses were performed in fibroblast and blood tissues to compare splicing events between RNU2-2 individuals and two independent control groups. We identified 14 individuals with 12 ultra-rare biallelic RNU2-2 variants clustering in the conserved 5 domains. All individuals presented with a highly concordant, severe DEE, characterised by severe to profound intellectual disability, inability to walk or communicate, hyperkinesia and refractory seizures. Infantile spasms and tonic seizures were the predominant seizure types and a Lennox-Gastaut syndrome-like phenotype was common. These individuals had a significantly similar phenotypic signature when compared with 703 individuals with paediatric epilepsy (two-sided Monte Carlo permutation test, p=0.005). RNA sequencing analyses in fibroblast tissues showed a clear separation of aberrant mutually exclusive exon and alternate 3 splice site events between RNU2-2 individuals and controls, which was not detectable in blood. In summary, we present deep phenotyping data and transcriptomic analyses which provide support for rare, 5 clustering biallelic RNU2-2 variants causing a novel, severe DEE. We propose an RNA sequencing methodology on fibroblast tissue for future validation of RNU2-2 variants.

Age-related hearing loss (ARHL) is a progressive, bilateral decline in hearing ability that affects one in four individuals over 60 years of age worldwide. While previous genome-wide association studies (GWAS) have identified distinct single-nucleotide variants (SNVs) associated with metabolic and sensory ARHL phenotypes, the contribution of short tandem repeats (STRs) - a neglected yet important class of genetic variants - remains poorly understood. To address this gap, TRTools was used to impute STRs from a high quality, sequencing-derived SNV-STR reference panel to investigate the association between STRs and metabolic and sensory estimates. Heritability analyses revealed that while STRs contribute to estimates of both ARHL components, this class of variation plays a more important role in metabolic hearing loss (6%), which typically increases with age, compared to sensory hearing loss (4%). Further, the inclusion of this class of variant into GWAS analyses uncovered an association between a haplotype consisting of two missense variants (rs7714670 and rs6453022) and an intronic STR (chr5:73778077:A16) in ARHGEF28 (P=3.30x10-9), proving further insight into the variants driving this previously identified signal. Notably, burden analyses revealed that rare and longer repeats were associated with an increased risk of the metabolic phenotype and a reduced risk of the sensory phenotype. Functional annotation of significant and nominally significant STRs revealed potential effects on gene expression and splicing of nearby genes. Our findings provide the first evidence that STRs explain some of the missing heritability of ARHL phenotypes and create an STR resource for researchers to use in future analyses.