Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multisource Longitudinal Network and Latent Variable Model Analyses of ADHD Symptoms in Children.

ABSTRACT: Objective: Multisource longitudinal network analysis was used to determine if between-child and within-child variance of attention-deficit/hyperactivity disorder (ADHD) symptoms provided unique findings of ADHD relative to latent variable model (LVM) analyses.Method: Mothers and fathers of 802 Spanish first-grade children (54% boys) provided ratings of ADHD symptoms at two time points six weeks apart (assessment 1: 723 mothers and 603 fathers; assessment 2: 667 mothers and 584 fathers). Network and latent variable models were applied to the ratings.Results: Inattention, hyperactivity, and mixed hyperactive/impulsive symptom communities occurred for the within- and between-children's symptom networks with the results being consistent across mothers and fathers, especially for the between-children's symptom networks. LVM analyses identified three factors with the same symptoms on each factor as in the symptom communities. These models also showed invariance across mothers and fathers as well as assessments.Conclusions: Longitudinal networks provided several useful insights for ADHD, including centrality symptoms that differed across between- and within-child levels. However, many findings were also largely consistent with the LVM analyses. Future studies should use novel methods (e.g., intensive longitudinal measurement) and analytic tools to determine if more unique theoretical and clinical findings emerge when applying network analysis to longitudinally measured ADHD symptoms.

SUBMITTER: Preszler J

PROVIDER: S-EPMC7704608 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A Bayesian latent variable mixture model for longitudinal fetal growth.

Project description:Fetal growth restriction is a leading cause of perinatal morbidity and mortality that could be reduced if high-risk infants are identified early in pregnancy. We propose a Bayesian model for aggregating 18 longitudinal ultrasound measurements of fetal size and blood flow into three underlying, continuous latent factors. Our procedure is more flexible than typical latent variable methods in that we relax the normality assumptions by allowing the latent factors to follow finite mixture distributions. Using mixture distributions also permits us to cluster individuals with similar observed characteristics and identify latent classes of subjects who are more likely to be growth or blood flow restricted during pregnancy. We also use our latent variable mixture distribution model to identify a clinically meaningful latent class of subjects with low birth weight and early gestational age. We then examine the association of latent classes of intrauterine growth restriction with latent classes of birth outcomes as well as observed maternal covariates including fetal gender and maternal race, parity, body mass index, and height. Our methods identified a latent class of subjects who have increased blood flow restriction and below average intrauterine size during pregnancy. These subjects were more likely to be growth restricted at birth than a class of individuals with typical size and blood flow.

| S-EPMC3717393 | biostudies-literature

Application of the Bifactor S - 1 Model to Multisource Ratings of ADHD/ODD Symptoms: an Appropriate Bifactor Model for Symptom Ratings.

Project description:The symmetrical bifactor model is often applied to attention-deficit/hyperactivity disorder (ADHD)-hyperactive/impulsive (HI), ADHD-inattentive (IN), and oppositional defiant disorder (ODD) symptoms, but this model frequently yields anomalous or inadmissible results. An alternative model, the bifactor S - 1 model, is more appropriate for examining the hierarchical structure of ADHD/ODD symptoms. Both models were applied to ADHD-HI, ADHD-IN, and ODD symptom ratings by mothers, fathers, and teachers for 2142 Spanish children (49.49% girls; ages 8-13 years). The symmetrical bifactor model yielded the typical anomalous loadings, with a weakly defined ADHD-HI specific factor and difficult to interpret associations of general and specific factors with correlates. In contrast, the bifactor S - 1 model with ADHD-HI symptoms as general reference factor produced clearly interpretable results. For mothers and fathers, slightly more than 50% of true score variance in ADHD-IN and ODD symptoms represented specific residual variance not shared with the general ADHD-HI reference factor. For teachers, approximately 69% and 39% of true score variance in ADHD-IN and ODD symptoms, respectively, represented specific residual variance not shared with the general ADHD-HI reference factor. The general ADHD-HI reference factor and specific ADHD-IN and ODD residual factors showed convergent and discriminant validity across sources, along with unique associations with peer rejection, social impairment, and academic impairment factors. The bifactor S - 1 model also yielded results consistent with predictions from trait-impulsivity theory of ADHD/ODD development. Researchers should use the bifactor S - 1 model rather than the symmetrical bifactor model if hypotheses involve the latent hierarchical structure of ADHD/ODD symptoms.

| S-EPMC8017439 | biostudies-literature

The impact of depressive symptoms in adults with ADHD symptoms on family function and ADHD symptoms of their children.

Project description:OBJECTIVE: People with attention-deficit/hyperactivity disorder (ADHD) exhibit considerable impairment in social, academic, or occupational functioning. The present study aimed to examine the patterns of associations between ADHD symptoms, depression, and family functioning. METHODS: The sample consisted of 1,022 adults randomly selected from a district in Seoul, South Korea. Several self-assessment scales were utilized to rate ADHD symptoms (both past and current), current symptoms of depression, and level of family functioning. ADHD symptoms in the children of these participants were also assessed. Pearson's correlation and multiple linear regression analyses were performed; structural equation modeling (SEM) was conducted to determine the best fitting model. RESULTS: Adult ADHD symptoms were positively associated with depressive symptoms. Depressive symptoms, in turn, mediated the relationship between adult ADHD symptoms and cohesion among family members. In addition, depressive symptoms mediated the relationship between adult ADHD symptoms and their children's ADHD symptoms. CONCLUSION: The relationship between adult ADHD symptoms and family dysfunction may be influenced by depressive symptoms. When treating ADHD in adults, clinicians should pay attention to the presence or absence of depression.

| S-EPMC4023085 | biostudies-other

Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity.

Project description:Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized. A common choice is convex ℓ1 plus nuclear norm to regularize the searching process. However, the best estimator performance is not always achieved with these additive convex regularizations, especially when the sample complexity is low. In this paper, we consider a concave additive regularization which does not require the strong irrepresentable condition. We use concave regularization to correct the intrinsic estimation biases from Lasso and nuclear penalty as well. We establish the proximity operators for our concave regularizations, respectively, which induces sparsity and low rankness. In addition, we extend our method to also allow the decomposition of fused structure-sparsity plus low rankness, providing a powerful tool for models with temporal information. Specifically, we develop a nontrivial modified alternating direction method of multipliers with at least local convergence. Finally, we use both synthetic and real data to validate the excellence of our method. In the application of reconstructing two-stage cancer networks, "the Warburg effect" can be revealed directly.

| S-EPMC5097857 | biostudies-literature

Hidden Markov latent variable models with multivariate longitudinal data.

Project description:Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use.

| S-EPMC5097710 | biostudies-literature

A Longitudinal Study on Attention Development in Primary School Children with and without Teacher-Reported Symptoms of ADHD.

Project description:Background: Prospective longitudinal studies are essential in characterizing cognitive trajectories, yet few of them have been reported on the development of attention processes in children. We aimed to explore attention development in normal children and children with attention deficit and hyperactivity disorder (ADHD) symptoms in a repeated measures design using the attention network test (ANT). Methods: The population sample included 2,835 children (49.6% girls) aged 7-11 years from 39 schools in Barcelona (Catalonia, Spain) who performed the ANT four times from January 2012 to March 2013. According to teacher ratings, 10.5% of the children presented ADHD symptoms. We performed multilevel mixed-effects linear regression models, adjusting for school and individual, to test the effects of age-related growth on the ANT networks: alerting, orienting and executive attention, and three measurements related to attentiveness: median of hit reaction time (HRT), hit reaction time standard error (HRT-SE) and variability. Results: We observed age-related growth in all the outcomes, except orienting. The curves were steeper at the younger groups, although for alertness the improvement was further at the oldest ages. Gender and ADHD symptoms interacted with age in executive attention, HRT and variability. Girls performed better in executive attention at young ages although boys reached females at around 10 years of age. For HRT, males showed faster HRT. However, girls had a more pronounced improvement and reached the levels of boys at age 11. Children with ADHD symptoms had significant differences in executive attention, HRT and variability compared to children without ADHD symptoms. Conclusions: We detected an ongoing development of some aspects of attention in primary school children, differentiating patterns by gender and ADHD symptoms. Our findings support the ANT for assessing attention processes in children in large epidemiological studies.

| S-EPMC5432613 | biostudies-literature

Childhood executive functions and ADHD symptoms predict psychopathology symptoms in emerging adults with and without ADHD: a 10-year longitudinal study.

Project description:Deficits in executive functions (EFs) are theorized to play an important role in causing functional impairment and associated psychopathology in individuals with ADHD. The objective of this study was to examine the role of EFs and ADHD symptoms as longitudinal predictors of psychopathology symptoms in individuals with ADHD and typically developing individuals. We assessed individuals with and without ADHD (N = 135) with neuropsychological tests of EFs and scales of ADHD symptoms and psychopathology symptoms at baseline (T1; Mage = 11.59, 57.8% boys), 2-year follow-up (T2; Mage = 13.63, 97% retention), and 10-year follow-up (Mage = 21.18, 75% retention). Baseline EFs predicted psychopathology symptoms at the 2- and the 10-year follow-up, explaining 17% and 12% of the variance, respectively. Baseline EFs predicted both internalizing and externalizing symptoms, and the predictive value of EFs on psychopathology symptoms at 10-year follow-up was accounted for by cognitive flexibility. Baseline ADHD symptoms were a significant predictor of all symptom domains at all time points. Thus, childhood EFs, in particular cognitive flexibility, can predict psychopathology symptoms in emerging adulthood beyond the effect of ADHD symptoms. This supports dominating theories of ADHD stating that executive dysfunction contributes to the observed phenotype, including associated psychopathology symptoms, and suggests that EFs are important targets of interventional efforts.

| S-EPMC9867664 | biostudies-literature

Family Functioning in Children With ADHD and Subthreshold ADHD: A 3-Year Longitudinal Study.

Project description:ObjectiveTo compare family functioning over time for elementary school children with Attention-Deficit/Hyperactivity Disorder (ADHD; N = 179) and subthreshold ADHD (ST-ADHD; N = 86), to children without ADHD (Control; N = 212).MethodADHD was assessed using the Conners 3 ADHD Index and Diagnostic Interview Schedule for Children IV. At baseline, 18-month follow-up and 36-month follow-up, parents completed measures assessing a range of family functioning domains.ResultsAt baseline, the ADHD group reported higher psychological distress, less parenting self-efficacy, less parenting consistency, and more stressful life events; and both groups reported poorer family quality of life (QoL) and greater parenting anger. Trajectories were largely similar to controls (i.e., stable over time), but unlike controls, ADHD and ST-ADHD groups showed lessening parent-partner support and parenting warmth, respectively; and both groups showed worsening aspects of family QoL.ConclusionFamilies of children with ADHD and ST-ADHD report persistently poor or worsening family functioning; highlighting a need for tailored psycho-social supports.

| S-EPMC10838481 | biostudies-literature

A latent variable transformation model approach for exploring dysphagia.

Project description:Multiple outcomes are often collected in applications where the quantity of interest cannot be measured directly or is difficult or expensive to measure. In a head and neck cancer study conducted at Dana-Farber Cancer Institute, the investigators wanted to determine the effect of clinical and treatment factors on unobservable dysphagia through collected multiple outcomes of mixed types. Latent variable models are commonly adopted in this setting. These models stipulate that multiple collected outcomes are conditionally independent given the latent factor. Mixed types of outcomes (e.g., continuous vs. ordinal) and censored outcomes present statistical challenges, however, as a natural analog of the multivariate normal distribution does not exist for mixed data. Recently, Lin et al. proposed a semiparametric latent variable transformation model for mixed outcome data; however, it may not readily accommodate event time outcomes where censoring is present. In this paper, we extend the work of Lin et al. by proposing both semiparametric and parametric latent variable models that allow for the estimation of the latent factor in the presence of measurable outcomes of mixed types, including censored outcomes. Both approaches allow for a direct estimate of the treatment (or other covariate) effect on the unobserved latent variable, greatly enhancing the interpretability of the models. The semiparametric approach has the added advantage of allowing the relationship between the measurable outcomes and latent variables to be unspecified, rendering more robust inference. The parametric and semiparametric models can also be used together, providing a comprehensive modeling strategy for complicated latent variable problems.

| S-EPMC7263574 | biostudies-literature

A tractable latent variable model for nonlinear dimensionality reduction.

Project description:We propose a latent variable model to discover faithful low-dimensional representations of high-dimensional data. The model computes a low-dimensional embedding that aims to preserve neighborhood relationships encoded by a sparse graph. The model both leverages and extends current leading approaches to this problem. Like t-distributed Stochastic Neighborhood Embedding, the model can produce two- and three-dimensional embeddings for visualization, but it can also learn higher-dimensional embeddings for other uses. Like LargeVis and Uniform Manifold Approximation and Projection, the model produces embeddings by balancing two goals-pulling nearby examples closer together and pushing distant examples further apart. Unlike these approaches, however, the latent variables in our model provide additional structure that can be exploited for learning. We derive an Expectation-Maximization procedure with closed-form updates that monotonically improve the model's likelihood: In this procedure, embeddings are iteratively adapted by solving sparse, diagonally dominant systems of linear equations that arise from a discrete graph Laplacian. For large problems, we also develop an approximate coarse-graining procedure that avoids the need for negative sampling of nonadjacent nodes in the graph. We demonstrate the model's effectiveness on datasets of images and text.

| S-EPMC7354940 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data