Investigating Heterogeneity and Overlapping Clinical and Neurobiological Features across Early Psychosis and Neurodevelopmental Disorders in Children and Adolescents
Bibliographic record
Abstract
The presence of psychosis symptoms in childhood and adolescence is associated with impaired daily functioning and the later development of more severe psychotic and non-psychotic psychiatric disorders. Evidence is emerging that neurodevelopmental disorders, such as autism spectrum disorder (ASD), or attention deficit hyperactivity disorder (ADHD) are themselves risk factors for psychosis symptoms and psychotic disorders. Heterogeneity within these disorders is well-established, as well as that they share overlapping clinical and biological features. In addition, such disorders share common genetic and neural circuit substrates with psychotic disorders. However, the relationship between these disorders including their heterogeneity and comorbidities, as well as underlying neurobiology is still unclear. This thesis applies multivariate, machine learning, and factor analytic approaches and magnetic resonance imaging to investigate neurobiology and other risk factors, such as biological sex, associated with overlapping psychopathology in children and adolescents. In study one, cortico-striatal-thalamic-cortical circuit structure and connectivity is examined in children and adolescents experiencing psychosis spectrum symptoms, as well as how differences vary with age and sex. In study two, novel data-driven subgroups across children with obsessive-compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are identified based on integrated multimodal structural imaging and behavior measures. In study three, factor structure underlying a wide range of psychopathology symptoms is investigated in children using ten dimensional instruments from both child and parent informants and a novel modeling approach. Together, the work presented in this thesis highlights heterogeneity within and similarities across children and youth experiencing early psychosis symptoms and neurodevelopmental disorders (ASD, ADHD, and OCD) in neurobiology and behavior. By improving our understanding of different neurobiological profiles of youth within and across diagnostic categories and how these map onto behavior, we can better understand the pathophysiology of mental illnesses, identify biomarkers of risk that can be used to predict illness/outcome trajectories, and provide opportunities to develop more individualized interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".