Specificity of cortical area and thickness as biomarkers for comorbid internalizing and externalizing mental disorders in pre-adolescence
Bibliographic record
Abstract
Abstract Background Comorbidity is the rule rather than the exception for childhood and adolescent onset mental disorders, but we cannot predict its occurrence and do not know the neural mechanisms underlying comorbidity. We investigate if the effects of comorbid internalizing and externalizing disorders on anatomical changes represent a simple aggregate of the effects on each disorder, and if comorbidity-related cortical surface changes relate to a distinct genetic underpinning. Methods We studied the cortical surface area (SA) and thickness (CT) of 11,878 preadolescents (9-10 years) from the Adolescent Brain and Cognitive Development Study. Linear mixed models were implemented in comparative and association analyses among internalizing (Dysthymia, Major Depressive Disorder, Disruptive Mood Dysregulation Disorder, Agoraphobia, Panic Disorder, Specific Phobia, Separation Anxiety Disorder, Social Anxiety Disorder, Generalized Anxiety Disorder, Post-Traumatic Stress Disorder), externalizing diagnostic groups (Attention-Deficit/Hyperactivity Disorder, Oppositional Defiant Disorder, Conduct disorder) a group with comorbidity of the two and a healthy control group. Genome-wide association analysis and cell type specificity analysis were performed on 4,716 unrelated European participants from this cohort. Results Reduced cortical surface area but increased thickness occurs across patient groups when compared to controls. Children with comorbid internalizing and externalizing disorders had more pronounced areal reduction than those without comorbidity, indicating an additive burden. In contrast, cortical thickness had a non-linear effect with comorbidity: the comorbid group had no significant CT changes, while those patient groups without comorbidity had significant thickness increases. Distinct biological pathways were implicated for regional SA and CT changes. Specifically, CT changes were associated with immune-related processes implicating microglia, while SA-related changes related mainly to excitatory neurons. Conclusions The emergence of comorbidity across distinct clusters of psychopathology is unlikely to be a simple additive neurobiological effect. Distinct risk-adaptation processes, with unique genetic and cell-specific factors may underlie SA and CT changes. Children with highest risk but lowest resilience, both captured in their developmental morphometry, develop a comorbid illness pattern.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".