Visual memory scores are associated with lateralization of tau in the medial temporal lobe
Bibliographic record
Abstract
Abstract Background Aggie Figures Learning Test (AFLT) is a visual memory test that was conceived as an analogue of the widespread Rey Auditory Verbal Learning Test (RAVLT), which tests verbal memory. Previous research has indicated that performance may rely on the left medial temporal lobe (lMTL) for RAVLT and on the right medial temporal lobe (rMTL) for AFLT, although evidence is inconclusive. The present study looks into the association between delayed recall (DR) scores in both tests and tau binding in the brain. Method Tau PET ([18F]‐MK6240) was acquired for 130 individuals for analysis involving AFLT, and 139 for analysis involving RAVLT. Demographic data is shown on Tables 1 and 2. MRI were segmented into probabilistic grey (GM) and white (WM) maps, non‐linearly registered to the ADNI template using Dartel and smoothed with an 8mm FWHM gaussian kernel. Voxel‐wise linear regression models were applied, using VoxelStats, with either DR AFLT or DR RAVLT as dependent variables and tau as a predictor. We corrected for age, diagnosis, and amyloid load. All other variables that were tested did not significantly contribute to predict DR scores. Results We found negative associations between tau binding and DR RAVLT in the MTL bilaterally. Interestingly, we found a negative correlation between tau binding and DR AFLT scores that is remarkable in the rMTL and barely reaches significance in the lMTL. Conclusion Our findings provide evidence in support of the lateralization of memory in the brain based on the learning modality. This dissociation is in line with previous findings; namely, that the rMTL is responsible for visual memory. However, these findings are not entirely in line with previous research since associations with RAVLT are found bilaterally. Of note, most of the previous research has been made in the context of epilepsy and/or patients with anterior MTL lesions. Here, however, we are investigating a totally different population and we are looking at a parameter that is specific to this population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".