Assessing Motor Cortex Excitability and Its Relationship with Cortical Atrophy in Alzheimer’s Dementia
Bibliographic record
Abstract
ABSTRACT Background: Cortical excitability has been proposed as a novel neurophysiological marker of neurodegeneration in Alzheimer’s dementia (AD). However, the link between cortical excitability and structural changes in AD is not well understood. Objective: To assess the relationship between cortical excitability and motor cortex thickness in AD. Methods: In 62 participants with AD (38 females, mean ± SD age = 74.6 ± 8.0) and 47 healthy control (HC) individuals (26 females, mean ± SD age = 71.0 ± 7.9), transcranial magnetic stimulation resting motor threshold (rMT) was determined, and T1-weighted MRI scans were obtained. Skull-to-cortex distance was obtained manually for each participant using MNI coordinates of the motor cortex (x = −40, y = −20, z = 52). Results: The mean skull-to-cortex distances did not differ significantly between participants with AD (22.9 ± 4.3 mm) and HC (21.7 ± 4.3 mm). Participants with AD had lower motor cortex thickness than healthy individuals ( t (92) = −4.4, p = <0.001) and lower rMT (i.e., higher excitability) than HC ( t (107) = −2.0, p = 0.045). In the combined sample, rMT was correlated positively with motor cortex thickness ( r = 0.2, df = 92, p = 0.036); however, this association did not remain significant after controlling for age, sex and diagnosis. Conclusions: Patients with AD have decreased cortical thickness in the motor cortex and higher motor cortex excitability. This suggests that cortical excitability may be a marker of neurodegeneration in AD.
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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 | 0.001 | 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 | 0.000 | 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".