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The relational structure of psychological symptoms and disorders is of crucial importance to mechanistic and causal research. Methodologically, factor analytic approaches (latent variable modeling) and network analyses are two dominant approaches. Amidst some debate about their relative merits, use of both methods simultaneously in the same data set has rarely been reported in child or adolescent psychopathology. A second issue is that the nosological structure can be enriched by inclusion of transdiagnostic constructs, such as neurocognition (e.g., executive functions and other processes). These cut across traditional diagnostic boundaries and are rarely included even though they can help map the mechanistic architecture of psychopathology. Using a sample enriched for ADHD (n = 498 youth ages 6 to 17 years; M = 10.8 years, SD = 2.3 years, 55% male), both approaches were used in two ways: (a) to model symptom structure and (b) to model seven neurocognitive domains hypothesized as important transdiagnostic features in ADHD and associated disorders. The structure of psychopathology domains was similar across statistical approaches with internalizing, externalizing, and neurocognitive performance clusters. Neurocognition remained a distinct domain according to both methods, showing small to moderate associations with internalizing and externalizing domains in latent variable models and high connectivity in network analyses. Overall, the latent variable and network approaches yielded more convergent than discriminant findings, suggesting that both may be complementary tools for evaluating the utility of transdiagnostic constructs for psychopathology research.
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http://dx.doi.org/10.1007/s10802-021-00770-8 | DOI Listing |
IEEE Trans Cybern
September 2025
To combine the strengths of Gaussian and non-Gaussian latent variable models, a novel information fusion strategy has recently been proposed under the deep learning framework. Although promising results have been obtained, the critical structure learning problem remains unsolved, which seriously hinders the automation of data-driven modeling and analytics. In this article, the maximal information coefficient (MIC) method is introduced as a measurement of the AS between two latent variables, which has no restriction in the type of data distribution.
View Article and Find Full Text PDFJ Behav Med
September 2025
Department of Psychology, University of Wisconsin-La Crosse, La Crosse, WI, USA.
Latent profile analysis (LPA) is in the finite mixture model analysis family and identifies subgroups by participants' responses to continuous variables (i.e., indicators); participants' probable membership in each subgroup is based on the similarity between the subgroup's prototypical responses and the person's unique responses.
View Article and Find Full Text PDFJ Behav Med
September 2025
Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7039, Cincinnati, OH, 45229, USA.
Randomized pretest, posttest, follow-up (RPPF) designs are widely used in longitudinal behavioral intervention research to evaluate the efficacy of treatments over time. These designs typically involve random assignment of participants to treatment and control conditions, with assessments conducted at baseline, immediately post-intervention, and during the follow-up period. Researchers primarily focus on determining whether the intervention is more effective than the control condition at post-treatment and whether these effects are sustained or change over time.
View Article and Find Full Text PDFJ Child Psychol Psychiatry
September 2025
Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Background: Prospective studies of autism family history infants primarily report recurrence and predictors of autism at 3 years. Less is known about ADHD family history infants and later childhood outcomes. We characterise profiles of mid-childhood developmental and behavioural outcomes in infants with a family history of autism and/or ADHD to identify potential support needs and patterns of co-occurrence across domains.
View Article and Find Full Text PDFJAACAP Open
September 2025
A.J. Drexel Autism Institute at Drexel University, Philadelphia, Pennsylvania.
Objective: The goal of this study is to characterize health outcomes across 3 domains-overall well-being, behavioral health, and physical health-in a large sample of autistic and non-autistic children and adolescents in the Environmental influences on Child Health Outcomes (ECHO) program.
Method: First, we examined differences in health outcomes between autistic (N = 286) and non-autistic (N = 4,225) children and adolescents in the ECHO Program. Using a subsample of 1,809 participants (116 autistic participants) with complete outcome data, we conducted latent profile analyses (LPAs) to define profiles of health outcomes for autistic children and adolescents and for the combined sample of autistic and non-autistic participants.