Amyloid (a), tau (t) and voxel‐based morphometry (n) correlates of visual memory performance
Bibliographic record
Abstract
Abstract Background The Aggie Figures Learning Test (AFLT) is a memory test that was designed to be a visual analogue of the Rey Auditory Verbal Learning Test (RAVLT). Previous studies have found associations between tau and amyloid PET and brain volume and RAVLT scores. However, to date, no study has explored the associations between these markers and AFLT scores. We aimed at exploring such associations. Methods Structural MRI, amyloid PET ([18F]‐NAV4694) and tau PET ([18F]‐MK6240) were acquired for 161 individuals. We conducted analyses on two sub‐samples. 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 AFLT sub‐scores as dependent variables and either tau and amyloid binding or Voxel‐Based Morphometry as predictors. We corrected for sex, Apoe genotype, age and years of education. Results We found negative associations between tau binding and AFLT total (trials 1‐5) and AFLT delayed recall (DR) scores in the MTL and temporo‐occipital cortices, as well as in some of their WM tracts. These associations were stronger for the total scores and, in both cases, in the right hemisphere. For the amyloid biomarker, associations with AFLT total and AFLT DR scores were negative in right ventromedial PFC, right posterior thalamus and basal ganglia. In this case, strongest associations are reported between amyloid burden and AFLT DR scores. Finally, we found positive associations between AFLT total and DR scores and VBM in the fusiform gyrus bilaterally. Conclusions Results resemble previously reported findings on associations between RAVLT and tau, amyloid and brain volume estimates. Our findings are also in line with initial reporting of patients with right hemisphere damage having difficulties with AFLT tasks (Madjan, et al. 1996). In addition, relationships with tau tend to be more posterior, while relationships with amyloid were shown to be more anterior. Further analyses based on diagnostic categories are needed to explore how these associations change with pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".