Understanding medial temporal activation in memory tasks: Evidence from fMRI of encoding and recognition in a case of transient global amnesia
Bibliographic record
Abstract
We used fMRI to examine the activation patterns of patient AE during encoding and recognition of visual scenes during an episode of transient global amnesia (TGA) and 3 months later. Controls (n = 5) showed bilateral (R > L) activation in parahippocampal and fusiform gyri during encoding and right-sided activation in the same regions associated with recognition of previously viewed scenes. AE showed a similar pattern at follow-up. During acute TGA, when performance was profoundly impaired, AE showed no medial temporal activation associated with encoding of new scenes or recognition of old scenes. In both contrasts, the percent signal change in relevant medial temporal regions was more than three standard deviations below the control sample mean. She did, however, show striking bilateral hippocampal activation for recognition attempts (old + new scenes > baseline) even though retrieval was unsuccessful (55% recognition accuracy). This finding was unique to AE on this occasion. This is the first study to document normalization of both encoding and recognition activation patterns in TGA. Furthermore, the strong hippocampal activation during unsuccessful retrieval highlights important issues in interpreting memory-related activations, particularly in dysfunctional systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".