Transdiagnostic associations across communication, cognitive, and behavioural problems in a developmentally at-risk population: a network approach.
ABSTRACT: BACKGROUND:Communication, behavioural, and executive function problems often co-occur in childhood. Previous attempts to identify the origins of these comorbidities have typically relied on comparisons of different deficit groups and/or latent variable models. Here we apply a network approach to a heterogeneous sample of struggling learners to conceptualise these comorbidities as a dynamic system of interacting difficulties. METHODS:714 children struggling with attention, learning, and/or memory were included. The sample consisted of children with both diagnosed (41%) and undiagnosed difficulties. The conditional independence network of parent ratings of everyday behaviour, cognition, and communication was estimated. RESULTS:A clustering coefficient identified four interconnected areas of difficulty: (1) structural language and learning; (2) pragmatics and peer relationships; (3) behavioural and emotional problems; and (4) cognitive skills. Emotional and behavioural symptoms shared multiple direct connections with pragmatic abilities and cognitive problems, but not with structural language skills or learning problems. Poor structural language and cognitive skills were associated with learning problems. Centrality indices highlighted working memory and language coherence as symptoms bridging different problem areas. CONCLUSION:The network model identified four areas of difficulty and potential bridging symptoms. Although the current analytic framework does not provide causal evidence, it is possible that bridging symptoms may be the origins of comorbidities observed on a dimensional level; problems in these areas may cascade and activate problems in other areas of the network. The potential value of applying a dynamic systems network approach to symptoms of developmental disorders is discussed.
Project description:Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age-typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a child's cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole-brain structural connectomes for children from these four groupings (n = 184), alongside an additional group of typically developing children (n = 36), and identified distinct patterns of brain organization for each group. This study represents a novel move toward identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.
Project description:Children with attention-deficit/hyperactivity disorder (ADHD) commonly experience behavioural sleep problems, yet these difficulties are not routinely assessed and managed in this group. Presenting with similar symptoms to ADHD itself, sleep problems are complex in children with ADHD and their aetiology is likely to be multifactorial. Common internalising and externalising comorbidities have been associated with sleep problems in children with ADHD; however, this relationship is yet to be fully elucidated. Furthermore, limited longitudinal data exist on sleep problems in children with ADHD, thus their persistence and impact remain unknown. In a diverse sample of children with ADHD, this study aims to: (1) quantify the relationship between sleep problems and internalising and externalising comorbidities; (2) examine sleep problem trajectories and risk factors; and (3) examine the longitudinal associations between sleep problems and child and family functioning over a 12-month period.A prospective cohort study of 400 children with ADHD (150 with no/mild sleep problems, 250 with moderate/severe sleep problems) recruited from paediatric practices across Victoria, Australia. The children's parents and teacher provide data at baseline and 6-month and 12-month post enrolment.Parent report of child's sleep problem severity (no, mild, moderate, severe); specific sleep domain scores assessed using the Child Sleep Habits Questionnaire; internalising and externalising comorbidities assessed by the Anxiety Disorders Interview Schedule for Children IV/Parent version.Multiple variable logistic and linear regression models examining the associations between key measures, adjusted for confounders identified a priori.Ethics approval has been granted. Findings will contribute to our understanding of behavioural sleep problems in children with ADHD. Clinically, they could improve the assessment and management of sleep problems in this group. We will seek to publish in leading paediatric journals, present at conferences and inform Australian paediatricians through the Australian Paediatric Research Network.
Project description:BACKGROUND:Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or "moderators") may participate within these forums to offer guidance and support when participants are struggling but must expend considerable effort to continually review new content. Demands on moderators limit the scalability of evidence-based digital health interventions. OBJECTIVE:Automated identification of recovery problems could allow moderators to engage in more timely and efficient ways with participants who are struggling. This paper aimed to investigate whether computational linguistics and supervised machine learning can be applied to successfully flag, in real time, those discussion forum messages that moderators find most concerning. METHODS:Training data came from a trial of a mobile phone-based health intervention for individuals in recovery from alcohol use disorder, with human coders labeling discussion forum messages according to whether or not authors mentioned problems in their recovery process. Linguistic features of these messages were extracted via several computational techniques: (1) a Bag-of-Words approach, (2) the dictionary-based Linguistic Inquiry and Word Count program, and (3) a hybrid approach combining the most important features from both Bag-of-Words and Linguistic Inquiry and Word Count. These features were applied within binary classifiers leveraging several methods of supervised machine learning: support vector machines, decision trees, and boosted decision trees. Classifiers were evaluated in data from a later deployment of the recovery support intervention. RESULTS:To distinguish recovery problem disclosures, the Bag-of-Words approach relied on domain-specific language, including words explicitly linked to substance use and mental health ("drink," "relapse," "depression," and so on), whereas the Linguistic Inquiry and Word Count approach relied on language characteristics such as tone, affect, insight, and presence of quantifiers and time references, as well as pronouns. A boosted decision tree classifier, utilizing features from both Bag-of-Words and Linguistic Inquiry and Word Count performed best in identifying problems disclosed within the discussion forum, achieving 88% sensitivity and 82% specificity in a separate cohort of patients in recovery. CONCLUSIONS:Differences in language use can distinguish messages disclosing recovery problems from other message types. Incorporating machine learning models based on language use allows real-time flagging of concerning content such that trained staff may engage more efficiently and focus their attention on time-sensitive issues.
Project description:Background:Maternal mental health problems often develop prenatally and predict post-partum mental health. However, the circumstances before and following childbirth differ considerably. We currently lack an understanding of dynamic variation in the profiles of depressive and anxiety symptoms over the perinatal period. Methods:Depressive and anxiety symptoms were self-reported by 980 women at 26-week pregnancy and 3 months post-partum. We used network analysis of depressive and anxiety symptoms to investigate if the symptoms network changed during and after pregnancy. The pre- and post-partum depressive-anxiety symptom networks were assessed for changes in structure, unique symptom-symptom interactions, central and bridging symptoms. We also assessed if central symptoms had stronger predictive effect on offspring's developmental outcomes outcomes at birth and 24, 54, and 72 months old than non-central symptoms. Bridging symptoms between negative and positive mental health were also assessed. Results:Though the depressive-anxiety network structures were stable during and after pregnancy, the post-partum network was more strongly connected. The central depressive-anxiety symptoms were also different between prenatal and post-partum networks. During pregnancy, central symptoms were mostly related to feeling worthless or useless; after pregnancy, central symptoms were mostly related to feeling overwhelmed or being punished. Central symptoms during pregnancy were associated with poorer developmental outcomes for the child. Anxiety symptoms were strongest bridging symptoms during and after pregnancy. The interactions between negative and positive mental health symptoms were also different during and after pregnancy. Conclusions:The differences between pre- and post-partum networks suggest that the presentation of maternal mental health problems varies over the peripartum period. This variation is not captured by traditional symptom scale scores. The bridging symptoms also suggest that anxiety symptoms may precede the development of maternal depression. Interventions and public health policies should thus be tailored to specific pre- and post-partum symptom profiles.
Project description:Diagnosis of 'specific' language impairment traditionally required nonverbal IQ to be within normal limits, often resulting in restricted access to clinical services for children with lower NVIQ. Changes to DSM-5 criteria for language disorder removed this NVIQ requirement. This study sought to delineate the impact of varying NVIQ criteria on prevalence, clinical presentation and functional impact of language disorder in the first UK population study of language impairment at school entry.A population-based survey design with sample weighting procedures was used to estimate population prevalence. We surveyed state-maintained reception classrooms (n = 161 or 61% of eligible schools) in Surrey, England. From a total population of 12,398 children (ages 4-5 years), 7,267 (59%) were screened. A stratified subsample (n = 529) received comprehensive assessment of language, NVIQ, social, emotional and behavioural problems, and academic attainment.The total population prevalence estimate of language disorder was 9.92% (95% CI 7.38, 13.20). The prevalence of language disorder of unknown origin was estimated to be 7.58% (95% CI 5.33, 10.66), while the prevalence of language impairment associated with intellectual disability and/or existing medical diagnosis was 2.34% (95% CI 1.40, 3.91). Children with language disorder displayed elevated symptoms of social, emotional and behavioural problems relative to peers, F(1, 466) = 7.88, p = .05, and 88% did not make expected academic progress. There were no differences between those with average and low-average NVIQ scores in severity of language deficit, social, emotional and behavioural problems, or educational attainment. In contrast, children with language impairments associated with known medical diagnosis and/or intellectual disability displayed more severe deficits on multiple measures.At school entry, approximately two children in every class of 30 pupils will experience language disorder severe enough to hinder academic progress. Access to specialist clinical services should not depend on NVIQ.
Project description:Three-quarters of the burden of mental health problems occurs in low-and-middle-income countries, but few epidemiological studies of these problems in preschool children from sub-Saharan Africa have been published. Behavioural and emotional problems often start in early childhood, and this might be particularly important in Africa, where the incidence of perinatal and early risk factors is high. We therefore aimed to estimate the prevalence and risk factors of behavioural and emotional problems in young children in a rural area on the Kenyan coast.We did a population-based epidemiological study to assess the burden of behavioural and emotional problems in preschool children and comorbidities in the Kilifi Health and Demographic Surveillance System (KHDSS, a database formed of the population under routine surveillance linked to admissions to Kilifi County Hospital). We used the Child Behaviour Checklist (CBCL) to assess behavioural and emotional problems. We then determined risk factors and medical comorbidities associated with behavioural and emotional problems. The strength of associations between the risk factors and the behavioural and emotional problems was estimated using generalised linear models, with appropriate distribution and link functions.3539 families were randomly selected from the KHDSS. Of these, 3273 children were assessed with CBCL. The prevalence of total behavioural and emotional problems was 13% (95% CI 12-14), for externalising problems was 10% (9-11), and for internalising problems was 22% (21-24). The most common CBCL syndrome was somatic problems (21%, 20-23), whereas the most common DSM-IV-oriented scale was anxiety problems (13%, 12-14). Factors associated with total problems included consumption of cassava (risk ratio 5·68, 95% CI 3·22-10·03), perinatal complications (4·34, 3·21-5·81), seizure disorders (2·90, 2·24-3·77), and house status (0·11, 0·08-0·14). Seizure disorders, burn marks, and respiratory problems were important comorbidities of behavioural and emotional problems.Behavioural and emotional problems are common in preschool children in this Kenyan rural area and are associated with preventable risk factors. Behavioural and emotional problems and associated comorbidities should be identified and addressed in young children.Wellcome Trust.
Project description:Fragile X syndrome (FXS) is an inherited neurodevelopmental condition characterised by behavioural, learning disabilities, physical and neurological symptoms. In addition, an important degree of comorbidity with autism is also present. Considered a rare disorder affecting both genders, it first becomes apparent during childhood with displays of language delay and behavioural symptoms.Main aim: To show whether the combination of 10 mg/kg/day of ascorbic acid (vitamin C) and 10 mg/kg/day of ?-tocopherol (vitamin E) reduces FXS symptoms among male patients ages 6 to 18 years compared to placebo treatment, as measured on the standardized rating scales at baseline, and after 12 and 24 weeks of treatment.Secondary aims: To assess the safety of the treatment. To describe behavioural and cognitive changes revealed by the Developmental Behaviour Checklist Short Form (DBC-P24) and the Wechsler Intelligence Scale for Children-Revised. To describe metabolic changes revealed by blood analysis. To measure treatment impact at home and in an academic environment.A phase II randomized, double-blind pilot clinical trial.male children and adolescents diagnosed with FXS, in accordance with a standardized molecular biology test, who met all the inclusion criteria and none of the exclusion criteria.clinical data, blood analysis, Wechsler Intelligence Scale for Children-Revised, Conners parent and teacher rating scale scores and the DBC-P24 results will be obtained at the baseline (t0). Follow up examinations will take place at 12 weeks (t1) and 24 weeks (t2) of treatment.A limited number of clinical trials have been carried out on children with FXS, but more are necessary as current treatment possibilities are insufficient and often provoke side effects. In the present study, we sought to overcome possible methodological problems by conducting a phase II pilot study in order to calculate the relevant statistical parameters and determine the safety of the proposed treatment. The results will provide evidence to improve hyperactivity control and reduce behavioural and learning problems using ascorbic acid (vitamin C) and ?-tocopherol (vitamin E). The study protocol was approved by the Regional Government Committee for Clinical Trials in Andalusia and the Spanish agency for drugs and health products.ClinicalTrials.gov Identifier: NCT01329770 (29 March 2011).
Project description:Learning to read is a fundamental developmental milestone, and achieving reading competency has lifelong consequences. Although literacy development proceeds smoothly for many children, a subset struggle with this learning process, creating a need to identify reliable biomarkers of a child's future literacy that could facilitate early diagnosis and access to crucial early interventions. Neural markers of reading skills have been identified in school-aged children and adults; many pertain to the precision of information processing in noise, but it is unknown whether these markers are present in pre-reading children. Here, in a series of experiments in 112 children (ages 3-14 y), we show brain-behavior relationships between the integrity of the neural coding of speech in noise and phonology. We harness these findings into a predictive model of preliteracy, revealing that a 30-min neurophysiological assessment predicts performance on multiple pre-reading tests and, one year later, predicts preschoolers' performance across multiple domains of emergent literacy. This same neural coding model predicts literacy and diagnosis of a learning disability in school-aged children. These findings offer new insight into the biological constraints on preliteracy during early childhood, suggesting that neural processing of consonants in noise is fundamental for language and reading development. Pragmatically, these findings open doors to early identification of children at risk for language learning problems; this early identification may in turn facilitate access to early interventions that could prevent a life spent struggling to read.
Project description:BACKGROUND:Sweden is rapidly becoming an increasingly multicultural and digitalized society. Encounters between pediatric nurses and migrant mothers, who are often primary caregivers, are impeded by language problems and cultural differences. To support mothers, doulas, who are women having the same linguistic and cultural backgrounds, serve as cultural bridges in interactions with health care professionals. In addition, information and communication technology (ICT) can potentially be used to manage interactions owing to its accessibility. OBJECTIVE:The objective of this study was to investigate the role of ICT in managing communicative challenges related to language problems and cultural differences in encounters with migrant mothers from the perspectives of Swedish pediatric nurses and doulas. METHODS:Deep semistructured interviews with five pediatric nurses and four doulas from a migrant-dense urban area in western Sweden were audio recorded, transcribed, and analyzed using thematic content analysis. RESULTS:The results showed that ICT contributes to mitigating communicative challenges in interactions by providing opportunities for nurses and migrant mothers to receive distance interpreting via telephones and to themselves interpret using language translation apps. Using images and films from the internet is especially beneficial while discussing complex and culturally sensitive issues to complement or substitute verbal messages. These findings suggest that ICT helps enable migrant mothers to play a more active role in interactions with health care professionals. This has important implications for their involvement in other areas, such as child care, language learning, and integration in Sweden. CONCLUSIONS:The findings of this study suggest that ICT can be a bridging tool between health care professionals and migrants. The advantages and disadvantages of translation tools should be discussed to ensure that quality communication occurs in health care interactions and that health information is accessible. This study also suggests the development of targeted multimodal digital support, including pictorial and video resources, for pediatric care services.
Project description:BACKGROUND:Identifying children at risk of poor developmental outcomes remains a challenge, but is important for better targeting children who may benefit from additional support. We explored whether data routinely collected in early life predict which children will have language disability, overweight/obesity or behavioural problems in later childhood. METHODS:We used data on 10?262 children from the UK Millennium Cohort Study (MCS) collected at 9 months, 3, and 11 years old. Outcomes assessed at age 11 years were language disability, overweight/obesity and socioemotional behavioural problems. We compared the discriminatory capacity of three models: (1) using data currently routinely collected around the time of birth; (2) Model 1 with additional data routinely collected at 3 years; (3) a statistically selected model developed using a larger set of early year's risk factors for later child health outcomes, available in the MCS-but not all routinely collected. RESULTS:At age 11, 6.7% of children had language disability, 26.9% overweight/obesity and 8.2% socioemotional behavioural problems. Model discrimination for language disability was moderate in all three models (area under the curve receiver-operator characteristic 0.71, 0.74 and 0.76, respectively). For overweight/obesity, it was poor in model 1 (0.66) and moderate for model 2 (0.73) and model 3 (0.73). Socioemotional behavioural problems were also identified with moderate discrimination in all models (0.71; 0.77; 0.79, respectively). CONCLUSION:Language disability, socioemotional behavioural problems and overweight/obesity in UK children aged 11 years are common and can be predicted with moderate discrimination using data routinely collected in the first 3?years of life.