Electrical Source Imaging of Sleep Spindles
Bibliographic record
Abstract
To identify and compare cortical source generators of slow and fast sleep spindles in healthy subjects, electroencephalographic (EEG) signals were obtained from 256 channels, and sources on neuroanatomical Montreal Neurological Institute (MNI) space estimated with low-resolution brain electromagnetic tomography analysis (LORETA). Spindle activity was recorded in 18 healthy volunteers during daytime napping. Because of lack of sleep or excessive artifacts, data from 13 subjects were analyzed off-line. Spindles were visually scored, marked, and bandpass filtered (slow 10-12 Hz or fast 12-14 Hz). EEG was segmented on the marker, and segments separately averaged. LORETA projected cortical sources on the MNI brain. Maximal intra- and inter-individual intensities were compared using the Wilcoxon test (P < .05) and cortical sources distribution compared using a chi2 test. Two to three slow spindles generators were consistently identified in frontal lobes, with additional sources in parietal and limbic lobes in half cases. Fast spindles had multiple temporo-parietal sources, with an inconstant frontal source. Inter-individual (P = 0.44), and intra-individual (P = 0.09 slow and P = 0.10 fast spindles) source intensities were comparable. Slow spindles sources were preferentially concentrated over frontal cortices in comparison with fast spindles (P = 0.0009). Our results demonstrate multiple, synchronous, and equipotent spindles cortical generators in healthy subjects, with more anterior generators for slow spindles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".