Exploring Deep Magnetoencephalography via Thalamo‐Cortical Sleep Spindles
Bibliographic record
Abstract
Subcortical brain regions like the thalamus are integral to numerous sensory and cognitive functions. Magnetoencephalography (MEG) enables the study of widespread brain networks with high temporal resolution, but the degree to which deep sources like the thalamus can be resolved remains unclear. Functional connectivity methods may enhance differentiation, yet few studies have extended them beyond the cortex. We investigated the possibility of resolving deep sources via connectivity patterns during thalamo-cortical sleep spindles to leverage their well-characterized circuitry, and during spindle-free periods of non-rapid eye movement sleep to explore neural recordings that lack such high-amplitude bursts of activity. MEG and electroencephalography (EEG) were recorded in 19 participants during a 2-h nap. Spindle and non-spindle periods were identified, and connectivity was assessed using coherence and imaginary coherence within a spindle-related network. Graph theory was also applied to identify network hubs. As expected, functional connectivity increased during spindles within a distributed thalamo-cortical-hippocampal network. Cortical connectivity patterns allowed differentiation among small thalamic nuclei, but metric choice and contrast use influenced topography and distance effects. Graph theory revealed distinct cortical, thalamic, and hippocampal contributions to fast (13-16 Hz) and slow (10-13 Hz) sigma-band connectivity. These findings demonstrate that MEG functional connectivity can resolve deep brain networks during NREM sleep and during spindles, and demonstrate how it can be used to study the functional roles of subcortical regions non-invasively in healthy humans. By clarifying methodological influences, we aim to guide future research design and interpretation.
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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 | 0.000 | 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) | 0.001 | 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".