Neurodevelopmental Impact of Bipolar Disorder Genetic Risk on Cortical Thickness and Network Topology in Adolescents
Bibliographic record
Abstract
Abstract Bipolar disorder (BD) is a highly heritable psychiatric condition characterized by recurrent mood episodes that commonly manifest during adolescence. Although polygenic risk scores (PRS) effectively quantify genetic susceptibility to BD, the neurobiological correlates of this genetic risk during adolescent brain development remain unclear. Using normative modeling and longitudinal neuroimaging data from the Adolescent Brain Cognitive Development (ABCD) cohort (N = 4519), we examined cortical thickness (CT) deviations and structural covariance network (SCN) alterations in adolescents stratified into high and low BD genetic risk groups based on PRS. Adolescents with high PRS for bipolar disorder exhibited greater cortical thickening in the inferior frontal gyrus and primary visual cortex, whereas those with low PRS demonstrated greater thickening in the posterior cingulate cortex and middle precentral gyrus. These patterns suggest PRS-related variations in cortical maturation, potentially reflecting distinct neurodevelopmental trajectories associated with genetic susceptibility to bipolar disorder. Furthermore, high PRS individuals displayed altered SCN topology, characterized by decreased local clustering and enhanced global network efficiency. Longitudinal data show that these abnormal regions exhibit atypical developmental trajectories, accompanied by a global reorganization of network topology. Additionally, high genetic risk was associated with lifestyle factors, especially correlated with increased positive expectancies toward substance use. Eventually, we found three potential BD risk genes during adolescence, including PLEKHA2, ZSCAN31 and ANK3, encoding the function of the postsynaptic membrane and synaptic membrane. These findings elucidate early neurodevelopmental deviations linked to genetic risk for BD, highlighting potential biomarkers for early identification and targeted 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".