The pons and human affective processing — Implications for Parkinson's disease
Bibliographic record
Abstract
Parkinson's disease Affective processingAffective processing determines how we react to stimuli, interact with the surroundings and perceive the world.It shapes our behaviour.In their article in this issue of EBioMedicine, Lee et al. (Lee et al., 2015) shed light on the involvement of the pons in affective processing in healthy volunteers.The authors report the findings of experiments where they showed images to adult women.The images consisted of visual stimuli containing a positive, neutral or negative affective charge.They conducted blood-oxygen-level dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) to determine brain metabolism during the visualising and subsequent emotional processes.Specifically, they analysed task-based BOLD signals, tested small-world connectivity and examined resting-state functional and diffusion tensor imaging (DTI) structural connectivity.They discovered that showing positive visual stimuli results in activation of a pontine region, possibly the caudal raphe nuclei.A significant functional connectivity was found between the pons and corticosub-cortical structures involved in affective processing, encompassing the caudate nucleus, thalamus, hippocampus, amygdala, as well as the cingulate, insular and frontal cortices.The authors conclude that their findings indicate that the pons forms a network with "cortico-limbic-striatal" systems to mediate one's affective state after seeing emotionally-charged images.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".