Disconnection of cortical face network in prosopagnosia revealed by diffusion tensor imaging
Bibliographic record
Abstract
Current anatomic models of face processing propose a ‘core’ system (occipital face area - OFA, the fusiform face area - FFA, and superior temporal sulcus - STS) and a multi-modal ‘extended’ system, which includes anterior temporal cortex. Classic models of prosopagnosia suggest that disruption of face processing may occur from disconnection within such a network, though most current studies have focused on damage to the modules themselves. In this report we describe the white matter changes in a prosopagnosic patient with only modest cortical damage. Patient R-AT1 acquired prosopagnosia following a right amygdalohippocampectomy for epilepsy, which resulted in lesion of the anterior part of the inferior longitudinal fasciculus (ILF). We used functional MRI (face-objects comparison) to localise regions in the core system, all of which were still present in R-AT1, and then performed diffusion tensor imaging (DTI) tractography to visualise tracts extending from these regions in R-AT1and 8 healthy controls. Tracts from the OFA of R-AT1's intact left hemisphere extended along the ILF, towards the anterior temporal lobe. In the lesioned right hemisphere of R-AT1, tracts from posterior occipitotemporal regions did not extend far anteriorally. Compared to controls, a region of reduced fractional anisotropy (FA: DTI index associated with white matter structural integrity) was found in the patient's right hemisphere ILF, adjacent and superior to the patient's FFA. These inter-subject and inter-hemispheric differences may reflect a retrograde degeneration of ILF tracts in the patient's lesioned right hemisphere. Hence, disruption of connections in the face-processing network between posterior occipitotemporal face-selective areas and anterior temporal cortex may contribute to R-AT1's prosopagnosia, particularly since the amount of cortical damage in R-AT1 is modest and in a location unusual for prosopagnosia.
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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 | 0.000 | 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 | 0.001 |
| 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".