A network of amygdala connections predict individual differences in trait anxiety
Bibliographic record
Abstract
In this study we demonstrate that the pattern of an amygdala-centric network contributes to individual differences in trait anxiety. Individual differences in trait anxiety were predicted using maximum likelihood estimates of amygdala structural connectivity to multiple brain targets derived from diffusion-tensor imaging (DTI) and probabilistic tractography on 72 participants. The prediction was performed using a stratified sixfold cross validation procedure using a regularized least square regression model. The analysis revealed a reliable network of regions predicting individual differences in trait anxiety. Higher trait anxiety was associated with stronger connections between the amygdala and dorsal anterior cingulate cortex, an area implicated in the generation of emotional reactions, and inferior temporal gyrus and paracentral lobule, areas associated with perceptual and sensory processing. In contrast, higher trait anxiety was associated with weaker connections between amygdala and regions implicated in extinction learning such as medial orbitofrontal cortex, and memory encoding and environmental context recognition, including posterior cingulate cortex and parahippocampal gyrus. Thus, trait anxiety is not only associated with reduced amygdala connectivity with prefrontal areas associated with emotion modulation, but also enhanced connectivity with sensory areas. This work provides novel anatomical insight into potential mechanisms behind information processing biases observed in disorders of emotion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".