Topographical Disorientation in Mild Cognitive Impairment: A Voxel-Based Morphometry Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To assess the neural substrates underlying topographical disorientation (TD) in patients affected by mild cognitive impairment (MCI), forty-one patients diagnosed with MCI and 24 healthy control individuals were recruited. METHODS: TD was assessed clinically in all participants. Neurological and neuropsychological evaluations and a volumetric-head magnetic resonance imaging scan were performed in each participant. Voxel-based morphometry was used to compare patterns of gray-matter atrophy between patients with and without TD, and a group of normal controls. RESULTS: We found TD in 17 out of the 41 MCI patients (41.4%). The functional abilities were significantly impaired in MCI patients with TD compared to in MCI patients without TD. Voxel-based morphometry analyses showed that the presence of TD in MCI patients is associated with loss of gray matter in the medial temporal regions, including the hippocampus and parahippocampal cortex, the fusiform gyrus, the inferior occipital gyrus, the amygdala, and the cerebellum. CONCLUSIONS: The findings found in this study represent the first evidence that the presence of TD in patients with MCI is associated with loss of gray matter in those brain regions that have been documented to be responsible for orientation in both neuropsychological and neuroimaging studies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".