Mapping neurogenetic characteristics of psychopathological procrastination using normative modeling in a prospective twin cohort
Bibliographic record
Abstract
Abstract Procrastination, affecting over 70% global population, is pervasively incurring negative outcomes in human society. This has long been studied as a bad daily habit, but it loses in delineating neurogenetic substrates underlying its psychopathological phenotyping. Using a prospective twin adolescent cohort, we demonstrate moderate heritability of this subclinical condition - PPS. Neuroimaging normative modeling analysis, further reveals that neurodevelopmental deviations in nucleus accumbens during adolescence, are predictive of PPS in adulthood, while such deviations-PPS mappings were highly genetically shared. Beyond to regional anomalies, PPS-specific whole-brain deviation patterns, notably in the default mode network, are neurobiologically enriched with changes in cortical manifolds (gradients) and neurotransmitter systems. Integrating these neuroimaging markers with transcriptomic atlas, we capture significant PPS-specific neurogenetic signatures associated with molecular transport system, neuroimmune responses, and neuroinflammation, particularly in serotonergic and dopaminergic pathways. These findings shed light on the multisystem neurogenetic architecture underlying PPS, providing evidence to theoretically conceptualize this psychopathological phenotype as a subclinical “brain disorder”. Teaser Psychopathological procrastination is not a bad daily habit solely, but a “brain disorder” associated with multiscale neurodevelopment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".