Brain Connectomics and Severity of Internalizing Symptoms in Early Adolescence Predict Severity of Suicidal Ideation in Later Adolescence
Bibliographic record
Abstract
Abstract Background Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the consequences of SI, it is important to identify neurobiological and psychological predictors of SI in adolescents in order to improve strategies to prevent suicide. Methods In 109 participants (61 female), we assessed psychological characteristics and obtained resting-state fMRI data in early adolescence (ages 9-13). Using graph theoretical methods, we assessed local network properties across 250 brain regions by computing measures of nodal interconnectedness: local efficiency, eigenvector centrality, nodal degree, within-module z-score, and participation coefficient. Four years later (ages 13-17), participants self-reported their SI severity. We used LASSO regression to identify a linear combination of the most important psychological, environmental, and brain-based predictors of future SI severity. Results The LASSO analysis identified a combination of 10 predictors of future SI severity (R 2 =0.23). Severity of internalizing symptoms at baseline was the strongest predictor; the remaining 9 predictors were brain-based, including nodal degree of the inferior frontal gyrus, precentral gyrus, fusiform gyrus, and inferior temporal gyrus; within-module degree of the substantia nigra and inferior parietal lobe; eigenvector centrality of the subgenual cingulate gyrus; participation coefficient of the caudal cingulate gyrus and medial amygdala. Conclusions Our findings suggest that combining network properties and earlier internalizing symptoms may improve prediction of later SI, compared to prior symptoms and other sociodemographic variables alone. Research should validate the clinical utility of these markers as predictors of suicidal thoughts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".