Assessing hyperexcitability in the context of cortical gray matter structures and white matter integrity in the dorsolateral prefrontal cortex and motor cortex of Alzheimer’s dementia patients
Bibliographic record
Abstract
BACKGROUND: Abnormal cortical excitability is a marker of neurodegeneration in Alzheimer's dementia (AD). However, the link between cortical excitability and structural changes in AD is not well understood. The objective of this study is to assess the relationship among motor cortex (MC) excitability, cortical thickness, and white matter integrity. We hypothesized that there is an inverse association between MC excitability and thickness or white matter tract integrity assessed from superior longitudinal fasciculus (SLF). METHOD: Participants were older individuals with AD meeting core National Institute on Aging and Alzheimer's Association (NIA-AA) clinical criteria or cognitively normal (CN) older individuals. Single-pulse TMS was delivered to the MC using a 7- cm figure-of-eight coil and Magtism 200 stimulator. EEG was recorded during the TMS protocol using a 64-channel Synamps 2 EEG system with DC at 20 kHz sampling rate. A rectified area under the curve between 50-275 ms post-TMS-evoked potential was used to measure excitability. T1-weighted MRI scans were pre-processed using established pipelines and estimates of cortical thickness were generated using FreeSurfer v6.0.1. Mean diffusivity (MD) and fractional anisotropy (FA) of the SLF were measured from diffusion-weighted MRI data using the ENIGMA-DTI protocol. RESULT: = -2.437, p = 0.018). In 31 participants with both MRI and TMS-EEG data, MC excitability did not differ between AD and CN groups. In the AD group, SLF MD correlated positively with MC excitability (r = 0.861, df = 7, p = 0.006). No relationships were found between MC excitability and cortical thickness or FA. CONCLUSION: The SLF is a major association pathway that interconnects the frontal lobe with other brain regions and is implicated in motor control. The positive correlation between SLF MD and MC excitability in AD may be related to a compensatory response of the MC in response to neurodegeneration.
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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 | 0.000 |
| 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".