Journal of Neurodevelopmental Disorders最新文献

Background: Phelan-McDermid syndrome (PMS) is a rare genetic syndrome characterized by developmental delay/intellectual disability, absent or delayed speech, physical dysmorphic features and high rates of autistic features. However, it is currently unknown whether people with PMS have similar neurocognitive atypicalities to those previously identified in idiopathic autism. Disruption in social orienting has previously been suggested as an early hallmark feature of idiopathic autism that impacts social learning and social interaction.

Methods: This study used a semi-naturalistic task to explore orienting to social versus non-social stimuli and its relation to clinical features in individuals diagnosed with PMS, autism, and neurotypical children recruited in the United States and the United Kingdom.

Results: At the group level, autistic and neurotypical children responded on average more often to social than non-social stimuli, while children with PMS responded similarly to both stimulus types. Both clinical groups responded significantly less often to social stimuli than neurotypical children. In addition, we found considerable variability in orienting responses within each group that were of clinical relevance. In the autism group, non-social orienting was associated with mental age, while in the PMS group social and non-social orienting were related to strength of autistic features.

Conclusions: These findings do not support specific social motivation difficulties in either clinical group. Instead, they highlight the importance of exploring individual differences in orienting responses in Phelan-McDermid Syndrome in relation to autistic features.

Trial registration: NA.

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early.

Background: A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children.

Methods: 78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model.

Results: The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD.

Conclusions: This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Objective: To analyze the complex relationship between socioeconomic status (SES) and neurodevelopmental achievements by investigating the temporal dynamics of these associations from birth to age 6.

Methods: This retrospective cohort study was conducted over 6 years using population-based data from the National Health Insurance Service and integrated data from the National Health Screening Program for Infants and Children. Participants were children born between 2009 and 2011 in Korea without neurodevelopmental delays with potential developmental implications. We analyzed results from the Korean Developmental Screening Test, administered at age 6, which covered overall assessment and six domains of gross and fine motor function, cognition, language, sociality, and self-care. The secondary outcome was to determine when neurodevelopmental outcomes began after birth and how these differences changed over time.

Results: Of 276,167 individuals (49.2% males), 66,325, 138,980, and 60,862 had low, intermediate, and high SES, respectively. Neurodevelopmental delays observed across all developmental domains were more prevalent in the low-SES group than in the high-SES group. Disparities in neurodevelopment according to these statuses were apparent as early as age 2 and tended to increase over time (interaction, P < 0.001). The cognition and language domains exhibited the most substantial disparities between SES levels. These disparities persisted in subgroup analyses of sex, birthweight, head circumference, birth data, and breastfeeding variables.

Conclusions: Low SES was significantly associated with an increased risk of adverse neurodevelopmental outcomes in preschool children, particularly those affecting cognitive and language domains. These differences manifested in early childhood and widened over time.

Background: Restricted and repetitive behaviors (RRBs) are highly prevalent and reduce function in individuals with fragile X syndrome (FXS). As transdiagnostic features of intellectual disability, elevated rates of RRBs in FXS could represent various underlying known co-occurring conditions in FXS such as anxiety or autism spectrum disorder (ASD), yet this distinction has not been investigated. Further, delineating whether RRBs are more indicative of anxiety or ASD in FXS may clarify phenotypic profiles within FXS and improve differential assessment.

Methods: We longitudinally examined the potentially independent or multiplicative effect of ASD and anxiety symptom severity on RRBs in 60 children with FXS. Anxiety was measured using the Child Behavior Checklist (CBCL), ASD severity was measured using the Childhood Autism Rating Scale (CARS), and RRBs were measured using the Repetitive Behavior Scale - Revised (RBS-R). We estimated a series of moderated regression models with anxiety and ASD symptoms at the initial assessment (Time 1) as predictors of RRBs at the outcome assessment two years later (Time 2), along with an anxiety-by-ASD interaction term to determine the potential multiplicative effect of these co-occurring conditions on RRBs.

Results: Results identified a significant interaction between ASD and anxiety symptom severity at the initial assessment that predicted elevated sensory-motor RRBs two years later. Increased sensory-motor RRBs were predicted by elevated ASD symptoms only when anxiety symptom severity was low. Likewise, increased sensory-motor RRBs were predicted by elevated anxiety symptoms only when ASD symptom severity was low. Interestingly, this relationship was isolated to Sensory-Motor RRBs, with evidence that it could also apply to total RRBs.

Conclusions: Findings suggest that ASD and anxiety exert independent and differential effects on Sensory-Motor RRBs when at high severity levels and a multiplicative effect when at moderate levels, which has important implications for early and targeted interventions.

Introduction: Rett syndrome (RTT) is a rare neurodevelopmental disorder with developmental impairments, comorbidities, and abnormal behaviours such as hand stereotypies and emotional features. The Rett Syndrome Behaviour Questionnaire (RSBQ) was developed to describe the behavioural and emotional features of RTT. Little is known how RSBQ scores are associated with genetic and clinical characteristics in RTT. This study investigated relationships between genotype, age, walking, hand function, sleep, and RSBQ total and subscale scores in RTT.

Methods: This is a cross-sectional analysis of data collected in the Australian Rett Syndrome Database and the International Rett Syndrome Phenotype Database. Parent caregivers completed the RSBQ and Sleep Disturbance Scale for Children [subscales for disorders of initiating and maintaining sleep (DIMS), disorders of excessive somnolence (DOES)], and provided information on age, variant type, functional abilities (mobility, hand function), seizure frequency and gastrointestinal problems. Associations between the RSBQ scores and the independent variables were modelled using linear regression.

Results: Data were available for 365 individuals with RTT [median (range) age 17.8 (2.9-51.9) years, 2 males]. Compared to adults, 2- to 12-year-old children had higher mean Total, Night-time Behaviour and Fear/Anxiety scores. Compared to individuals with a C-terminal deletion, individuals with the p.Arg255* variant had higher mean Total and Night-time Behaviours scores, whereas the p.Arg294* variant had higher mean Mood scores. Individuals with intermediate mobility and hand function abilities had a higher mean Total score. Total RSBQ and subscale scores were similar across categories for seizures, constipation, and reflux, but were higher with abnormal DIMS and abnormal DOES scores.

Conclusion: Except for associations with sleep, the RSBQ measures the behavioural phenotype rather than clinical severity in RTT, as traditionally conceptualised in terms of functional abilities and comorbidities. When designing clinical trials, the RSBQ needs to be complemented by other outcome measures to assess specific core functions and associated comorbidities in RTT.

Background: Recent studies have suggested an increasing prevalence of intellectual disability diagnoses in some countries. Our aim was to describe the trend in the prevalence of intellectual disability diagnoses in Sweden and explore whether associated sociodemographic and perinatal factors can explain changes in the prevalence.

Methods: We used a register-based nationwide cohort of residents in Sweden born between 2001 and 2011. We calculated the prevalence of intellectual disability diagnoses by age 10 for each birth cohort and the prevalence ratios in relation to the baseline year 2011, overall and by severity of intellectual disability, and comorbidity of autism and attention-deficit/hyperactivity disorder. The prevalence ratios were stratified and adjusted for associated sociodemographic and perinatal factors.

Results: Among 1,096,800 individuals, 8,577 were diagnosed with intellectual disability by age 10. Among these, 3,949 (46%) and 2,768 (32%) were also diagnosed with autism and attention-deficit/hyperactivity disorder, respectively, and 4% were diagnosed with profound, 8% severe, 20% moderate, 52% mild, and 16% other/unspecific intellectual disability. The recorded age-10 prevalence of intellectual disability diagnoses increased from 0.64% (95% confidence interval 0.59-0.69%) in 2011 to 1.00% (0.94-1.06%) in 2021, corresponding to an annual prevalence ratio of 1.04 (1.04-1.05). The increase was, however, restricted to mild, moderate, and other/unspecific intellectual disability diagnoses, while the trends for profound and severe intellectual disability diagnoses were stable. The increasing trend was perhaps less pronounced among females and children with diagnosed attention-deficit/hyperactivity disorder, but independent of the co-occurrence of autism. The prevalence ratios did not change with stratification or adjustment for other associated demographic and perinatal factors.

Conclusion: The recorded prevalence of diagnosed mild and moderate intellectual disability among 10-year-olds in Sweden has increased over the recent decade. This increase could not be explained by changes in associated sociodemographic or perinatal factors, including birth weight, gestational age, and parental age, migration status, and education at the child's birth. The increase instead may be due to changes in diagnostic practices in Sweden over time.

Background: Do different genetic disorders impart different psychiatric risk profiles? This question has major implications for biological and translational aspects of psychiatry, but has been difficult to tackle given limited access to shared batteries of fine-grained clinical data across genetic disorders.

Methods: Using a new suite of generalizable analytic approaches, we examine gold-standard diagnostic ratings, scores on 66 dimensional measures of psychopathology, and measures of cognition and functioning in two different sex chromosome aneuploidies (SCAs)-Klinefelter (XXY/KS) and XYY syndrome (n = 102 and 64 vs. n = 74 and 60 matched XY controls, total n = 300). We focus on SCAs for their high collective prevalence, informativeness regarding differential X- vs. Y-chromosome effects, and potential relevance for normative sex differences.

Results: We show that XXY/KS elevates rates for most psychiatric diagnoses as previously reported for XYY, but disproportionately so for anxiety disorders. Fine-mapping across all 66 traits provides a detailed profile of psychopathology in XXY/KS which is strongly correlated with that of XYY (r = .75 across traits) and robust to ascertainment biases, but reveals: (i) a greater penetrance of XYY than KS/XXY for most traits except mood/anxiety problems, and (ii) a disproportionate impact of XYY vs. XXY/KS on social problems. XXY/KS and XYY showed a similar coupling of psychopathology with adaptive function and caregiver strain, but not IQ.

Conclusions: This work provides new tools for deep-phenotypic comparisons of genetic disorders in psychiatry and uses these to detail unique and shared effects of the X- and Y-chromosome on human behavior.

Background: Phelan-McDermid syndrome (PMS) is caused by monoallelic loss or inactivation at the SHANK3 gene, located in human chr 22q13.33, and is often associated with Autism Spectrum Disorder (ASD).

Objectives: To assess the clinical and developmental phenotype in a novel sample of PMS patients, including for the first time auxometric trajectories and serotonin blood levels.

Methods: 70 Italian PMS patients were clinically characterized by parental report, direct medical observation, and a thorough medical and psychodiagnostic protocol. Serotonin levels were measured in platelet-rich plasma by HPLC.

Results: Our sample includes 59 (84.3%) cases with chr. 22q13 terminal deletion, 5 (7.1%) disruptive SHANK3 mutations, and 6 (8.6%) ring chromosome 22. Intellectual disability was present in 69 (98.6%) cases, motor coordination disorder in 65 (92.9%), ASD in 20 (28.6%), and lifetime bipolar disorder in 12 (17.1%). Prenatal and postnatal complications were frequent (22.9%-48.6%). Expressive and receptive language were absent in 49 (70.0%) and 19 (27.1%) cases, respectively. Decreased pain sensitivity was reported in 56 (80.0%), hyperactivity in 49 (80.3%), abnormal sleep in 45 (64.3%), congenital dysmorphisms in 35 (58.3%), chronic stool abnormalities and especially constipation in 29 (41.4%). Parents reported noticing behavioral abnormalities during early childhood immediately after an infective episode in 34 (48.6%) patients. Brain MRI anomalies were observed in 53 (79.1%), EEG abnormalities in 16 (23.5%), kidney and upper urinary tract malformations in 18 (28.1%). Two novel phenotypes emerged: (a) a subgroup of 12/44 (27.3%) PMS patients displays smaller head size at enrollment (mean age 11.8 yrs) compared to their first year of neonatal life, documenting a deceleration of head growth (p < 0.001); (b) serotonin blood levels are significantly lower in 21 PMS patients compared to their 21 unaffected siblings (P < 0.05), and to 432 idiopathic ASD cases (p < 0.001).

Conclusions: We replicate and extend the description of many phenotypic characteristics present in PMS, and report two novel features: (1) growth trajectories are variable and head growth appears to slow down during childhood in some PMS patients; (2) serotonin blood levels are decreased in PMS, and not increased as frequently occurs in ASD. Further investigations of these novel features are under way.

Background: Recent evidence suggests that certain fetal anomalies detected upon prenatal ultrasound screenings are associated with autism spectrum disorder (ASD). In this cross-sectional study, we aimed to identify genetic variants associated with fetal ultrasound anomalies (UFAs) in children with ASD.

Methods: The study included all children with ASD who are registered in the database of the Azrieli National Center of Autism and Neurodevelopment and for whom both prenatal ultrasound and whole exome sequencing (WES) data were available. We applied our in-house integrative bioinformatics pipeline, AutScore, to these WES data to prioritize rare, gene-disrupting variants (GDVs) probably contributing to ASD susceptibily. Univariate statistics and multivariable regression were used to assess the associations between UFAs and GDVs identified in these children.

Results: The study sample comprised 126 children, of whom 43 (34.1%) had at least one UFA detected in the prenatal ultrasound scan. A total of 87 candidate ASD genetic variants were detected in 60 children, with 24 (40%) children carrying multiple variants. Children with UFAs were more likely to have loss-of-function (LoF) mutations (aOR = 2.55, 95%CI: 1.13-5.80). This association was particularly noticeable when children with structural anomalies or children with UFAs in their head and brain scans were compared to children without UFAs (any mutation: aOR = 8.28, 95%CI: 2.29-30.01; LoF: aOR = 5.72, 95%CI: 2.08-15.71 and any mutation: aOR = 6.39, 95%CI: 1.34-30.47; LoF: aOR = 4.50, 95%CI: 1.32-15.35, respectively). GDVs associated with UFAs were enriched in genes highly expressed across all tissues (aOR = 2.76, 95%CI: 1.14-6.68). There was a weak, but significant, correlation between the number of mutations and the number of abnormalities detected in the same children (r = 0.21, P = 0.016).

Conclusions: The results provide valuable insights into the potential genetic basis of prenatal organogenesis abnormalities associated with ASD and shed light on the complex interplay between genetic factors and fetal development.