Sleep Differentially and Profoundly Impairs Recall Memory in a Patient with Fornix Damage
Bibliographic record
Abstract
In March 2020, C.T., a kind, bright, and friendly young woman underwent surgery for a midline tumor involving her septum pellucidum and extending down into her fornices bilaterally. Following tumor diagnosis and surgery, C.T. experienced significant memory deficits: C.T.'s family reported that she could remember things throughout the day, but when she woke up in the morning or following a nap, she would expect to be in the hospital, forgetting all the information that she had learned before sleep. The current study aimed to empirically validate C.T.'s pattern of memory loss and explore its neurological underpinnings. On two successive days, C.T. and age-matched controls watched an episode of a TV show and took a nap or stayed awake before completing a memory test. Although C.T. performed numerically worse than controls in both conditions, sleep profoundly exacerbated her memory impairment, such that she could not recall any details following a nap. This effect was replicated in a second testing session. High-resolution MRI scans showed evidence of the trans-callosal surgical approach's impact on the mid-anterior corpus callosum, indicated that C.T. had perturbed white matter particularly in the right fornix column, and demonstrated that C.T.'s hippocampal volumes did not differ from controls. These findings suggest that the fornix is important for processing episodic memories during sleep. As a key output pathway of the hippocampus, the fornix may ensure that specific memories are replayed during sleep, maintain the balance of sleep stages, or allow for the retrieval of memories following sleep.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".