Resting state low-frequency fluctuations in prefrontal cortex reflect degrees of harm avoidance and novelty seeking: an exploratory NIRS study
Bibliographic record
Abstract
Harm avoidance (HA) and novelty seeking (NS) are temperament dimensions defined by Temperament and Character Inventory (TCI), respectively, reflecting a heritable bias for intense response to aversive stimuli or for excitement in response to novel stimuli. High HA is regarded as a risk factor for major depressive disorder and anxiety disorder. In contrast, higher NS is linked to increased risk for substance abuse and pathological gambling disorder. A growing body of evidence suggests that patients with these disorders show abnormality in the power of slow oscillations of resting-state brain activity. It is particularly interesting that previous studies have demonstrated that resting state activities in medial prefrontal cortex (MPFC) are associated with HA or NS scores, although the relation between the power of resting state slow oscillations and these temperament dimensions remains poorly elucidated. This preliminary study investigated the biological bases of these temperament traits by particularly addressing the resting state low-frequency fluctuations in MPFC. Regional hemodynamic changes in channels covering MPFC during 5-min resting states were measured from 22 healthy participants using near-infrared spectroscopy (NIRS). These data were used for correlation analyses. Results show that the power of slow oscillations during resting state around the dorsal part of MPFC is negatively correlated with the HA score. In contrast, NS was positively correlated with the power of resting state slow oscillations around the ventral part of MPFC. These results suggest that the powers of slow oscillation at rest in dorsal or ventral MPFC, respectively, reflect the degrees of HA and NS. This exploratory study therefore uncovers novel neural bases of HA and NS. We discuss a neural mechanism underlying aversion-related and reward-related processing based on results obtained from this study.
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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 | 0.000 |
| 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".