Testing a Developmental Model of Parental Warmth, Amygdala–Subgenual Anterior Cingulate Cortex Connectivity, and Depressive Symptoms in Adolescents During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Neurobiological measures may serve as predictive markers of risk for and resilience to depressive symptoms during the COVID-19 pandemic. We tested a developmental model linking variation in amygdala–subgenual anterior cingulate cortex (sgACC) resting-state connectivity both to earlier experiences in the family environment and to subsequent vulnerability to depressive symptoms during the pandemic.Methods: We used data from a longitudinal study that included three waves (N=214 adolescents; ages 9-15 years at Time 1 (T1), 11-17 years at Time 2 (T2), and 12-19 years during the pandemic at Time 3 [T3]). We assessed parental warmth (T1), depressive symptoms (T1 to T3), and functional connectivity between the sgACC and basolateral (BLA) and centromedial amygdala (CMA) (T1 and T2). We modeled associations among early parental warmth, amygdala–sgACC connectivity, and depressive symptoms before and during the pandemic.Results: Less parental warmth was associated prospectively with stronger BLA–sgACC connectivity approximately two years later (=-.23, p=.021) over and above the effect of BLA–sgACC connectivity at T1. Stronger BLA–sgACC connectivity, in turn, was associated with heightened depressive symptoms, both before (r=.21, p=.031) and during the pandemic (=.22, p=.031; independent of the effect of pre-pandemic symptoms). Conclusion: Adolescents who experience less parental warmth may develop a pattern of BLA–sgACC connectivity that increases their risk for mental health problems during the pandemic. BLA–sgACC connectivity in early to middle adolescence may be a predictive marker of risk for depressive symptoms in general and specifically during periods of heightened stress.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".