Effect of visual stimuli of pain on empathy brain network in people with and without Autism Spectrum Disorder
Bibliographic record
Abstract
The extent to which affective empathy is impaired in Autism Spectrum Disorder (ASD) remains unclear, as some-but not all-previous neuroimaging studies investigating empathy for pain in ASD have shown similar activation levels to those of neurotypicals individuals. These inconsistent results could be due to the use of different empathy-eliciting stimuli. While some studies used pictures of faces exhibiting a painful expression, others used pictures of limbs in painful situations. In this study, we used fMRI to compare activation in areas associated with empathy processing (empathy network) for these two types of stimuli in 31 participants (16 with ASD, 15 controls). We found a group difference in the inferior frontal gyrus (IFG) and the thalamus when participants viewed stimuli of limbs in painful situations, but not when they viewed face stimuli with a painful expression. Both groups of participants activated their empathy network more when viewing pictures of limbs in painful situations than when viewing pictures of faces with a painful expression; this increased activation for limbs versus faces was significantly enhanced in controls relative to ASD participants, especially in the secondary somatosensory cortex (SII). Our findings suggest that empathy defect of people with ASD is contingent upon the type of stimuli used, and may be related to the level of Mirror Neuron System involvement, as brain regions showing group differences (IFG, SII) underlie embodiment. We discuss the potential clinical implications of our findings in terms of developing interventions boosting the empathetic abilities of people with ASD.
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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) | 0.002 | 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".