Impairment of Everyday Spatial Navigation Abilities in Mild Cognitive Impairment Is Weakly Associated with Reduced Grey Matter Volume in the Medial Part of the Entorhinal Cortex
Bibliographic record
Abstract
BACKGROUND: Research in rodents identified specific neuron populations encoding information for spatial navigation with particularly high density in the medial part of the entorhinal cortex (ERC), which may be homologous with Brodmann area 34 (BA34) in the human brain. OBJECTIVE: The aim of this study was to test whether impaired spatial navigation frequently occurring in mild cognitive impairment (MCI) is specifically associated with neurodegeneration in BA34. METHODS: The study included baseline data of MCI patients enrolled in the Alzheimer's Disease Neuroimaging Initiative with high-resolution structural MRI, brain FDG PET, and complete visuospatial ability scores of the Everyday Cognition test (VS-ECog) within 30 days of PET. A standard mask of BA34 predefined in MNI space was mapped to individual native space to determine grey matter volume and metabolic activity in BA34 on MRI and on (partial volume corrected) FDG PET, respectively. The association of the VS-ECog sum score with grey matter volume and metabolic activity in BA34, APOE4 carrier status, age, education, and global cognition (ADAS-cog-13 score) was tested by linear regression. BA28, which constitutes the lateral part of the ERC, was used as control region. RESULTS: The eligibility criteria led to inclusion of 379 MCI subjects. The VS-ECog sum score was negatively correlated with grey matter volume in BA34 (β= -0.229, p = 0.022) and age (β= -0.124, p = 0.036), and was positively correlated with ADAS-cog-13 (β= 0.175, p = 0.003). None of the other predictor variables contributed significantly. CONCLUSION: Impairment of spatial navigation in MCI is weakly associated with BA34 atrophy.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".