EEG in Silent Small Vessel Disease
Bibliographic record
Abstract
INTRODUCTION: Vascular cognitive impairment, no dementia (vCIND) is a prevalent and potentially preventable disorder. Clinical presof the small vessel subcortical subtype may be insidious and difficult to diagnose in the initial stage. We investigated electroencephalographic sources of subcortical vCIND in comparison to amnesic multidomain mild cognitive impairment (amdMCI) to determine the additional diagnostic value of quantitative electroencephalograhy (EEG) in this setting. METHODS: Fifty-seven community residing patients with an uneventful central neurological history and first presentation of cognitive decline without dementia were included, 35 patients were diagnosed with vCIND and 22 with amdMCI. A cognitive control group, deliberately recruited from a cerebrovascular impaired cohort, consisted of cognitively healthy participants who experienced a fully recovered first ever transient ischemic attack (TIA) without clinical or magnetic resonance imaging evidence of stroke. From standard EEGs, the differences in standardized low-resolution brain electromagnetic tomography (sLORETA) sources were determined for the discrete frequency ranges 1-4 (delta), 4-8 (theta), 8-10.5 (alpha1), 10.5-13 (alpha2), 13-22 (beta1), and 22-30 (beta2) Hz. RESULTS: In vCIND, a statistically significant decrease in parietooccipital alpha1 relative power current density compared with TIA and mild cognitive impairment patients was found. There was a significant decrease in frontal and parietooccipital beta1 relative power current density in vCIND compared with TIA patients. A significant increase in (pre) frontal delta relative power current density in vCIND compared with amdMCI was found as well. In amdMCI, delta relative power current density was significantly increased in the core limbic system. DISCUSSION: Cortical sources of abnormal EEG activity in regions implicated in the default mode network are revealed by sLORETA at an early stage in vascular cognitive impairment. Mapping of parietooccipital alpha1, frontoparietooccipital beta1 and (pre) frontal delta loci in vCIND may reflect early executive and visuospatial dysfunction in this cohort. Standard EEG with sLORETA mapping might be an additional, noninvasive, and cost-effective tool in the diagnostic workup of patients presenting with a cognitive decline.
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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) | 0.003 | 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".