Evaluation of BDNF as a Biomarker for Impulsivity in a Psychiatric Population
Bibliographic record
Abstract
Impulsivity is an important risk factor for suicide and therefore, identifying biomarkers associated with impulsivity could be important in evaluating psychiatric patients. Currently, assessment of impulsivity is based solely on clinical evaluation. In this study, brain-derived neurotrophic factor (BDNF), a nerve growth factor, was evaluated as a potential biomarker for impulsivity. We hypothesize that elevated BDNF may result in aberrantly high neurobiological activation, promoting impulsive behaviours. A total of 343 participants were recruited for the study and were divided into two groups, (i) elevated suicide risk (participants admitted to hospital with a recent suicide attempt), and (ii) average suicide risk (non-psychiatric participants and psychiatric participants without a history of suicide attempts). Impulsivity was measured by the Barratt Impulsiveness Scale, and serum BDNF levels were obtained. A regression analysis was performed to identify associations between BDNF and impulsivity. We identified a subtle but significant positive association between BDNF and impulsivity in the average risk for suicide group (B = 0.189, p = 0.014). The same association was not reproduced in the elevated risk group B = −0.086, p = 0.361). These findings lay the foundation to further explore the utility of BDNF as a biomarker for impulsivity to allow for early intervention.
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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 | 0.001 |
| 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".