Patterns of cortical thickness in MCI patients with and without semantic impairment
Bibliographic record
Abstract
• Half of MCI patients have deficits in semantic memory, which may harbinger progression to AD. • Semantic deficits in MCI are linked to reduced cortical thickness in key regions of the semantic network. • Reduced cortical thickness was found in the lateral, anterior temporal lobes, and the left parahippocampal cortex in MCI patients with semantic deficits. About half of MCI patients experience semantic deficits, which may predict progression to Alzheimer’s disease (AD). The neural basis of these deficits in MCI is not well understood. This study aimed to examine the relationship between semantic memory performance and cortical thickness in MCI patients. Using FreeSurfer, T1-weighted MRI scans were analyzed from MCI patients with (MCIsem+) and without (MCIsem-) semantic deficits. Correlation analyses across all participants, including healthy controls, examined the link between semantic memory and cortical thickness, controlling for age and education. Group comparisons of cortical thickness were also conducted between MCIsem+ and MCIsem- groups. Significant correlations emerged between semantic memory performance and cortical thickness in the left medial temporal lobe, right temporal pole, and bilateral frontal regions—areas involved in central semantic and executive processes. Additionally, MCIsem + patients showed reduced cortical thickness in frontal, parietal, and occipital areas compared to MCIsem- patients. Semantic memory performance in MCI patients is associated with structural differences in regions supporting both central and executive aspects of semantic processing. Given that MCIsem + patients may face higher risk of AD progression, longitudinal studies should investigate these cortical markers’ predictive value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".