Prosopagnosia Following Epilepsy Surgery: What You See Is Not All They Have
Bibliographic record
Abstract
Background: Studies of acquired prosopagnosia suggest that occipitotemporal lesions involving the fusiform gyrus are associated with perceptual deficits in face processing, while anterior temporal lesions are associated with associative or amnestic deficits, and that these lesions are right dominant. Surgical procedures for epilepsy can cause prosopagnosia in rare cases; however, the utility of their surgical lesions for structure-function correlations is uncertain, because by selection these patients have pre-operative focal neurological anomalies. Objective: We describe two cases of prosopagnosia following epilepsy surgery, in whom we located their surgical lesions and characterized their face processing networks, and related this to behavioural results in a structure-function correlation study. Method: Subjects had structural and functional MRI using a sensitive dynamic face localizer to find ROIs and characterize the status of their core face network (fusiform face area, FFA, occipital face area, OFA, and superior temporal sulcus, STS). In a perceptual battery, we evaluated face detection, face recognition, face perception, face imagery, and semantic knowledge about people. Subjects also underwent event-related potential to characterize the face-selective N170. Results: Subject R-AT1 had a right amygdalo-hippocampectomy sparing the core face network. Unlike other subjects with right anterior temporal lesions after trauma or encephalitis, she was impaired in face detection and perception, and had an anomalous N170 potential. Subject L-IOT1 became prosopagnosic after resection of the left fusiform gyrus and was impaired in face detection and perception. However, MRI also showed that the right fusiform gyrus was atrophic and did not show activation to faces. He was also impaired in semantic knowledge of people. Conclusion: Prosopagnosia following epilepsy surgery may reflect the effects of not only the surgical lesion but also pre-operative cerebral anomalies, resulting in more widespread functional deficits than predicted by their surgical lesion. Meeting abstract presented at VSS 2012
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".