Dynamic changes of ICA‐derived EEG functional connectivity in the resting state
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Bibliographic record
Abstract
An emerging issue in neuroscience is how to identify baseline state(s) and accompanying networks termed "resting state networks" (RSNs). Although independent component analysis (ICA) in fMRI studies has elucidated synchronous spatiotemporal patterns during cognitive tasks, less is known about the changes in EEG functional connectivity between eyes closed (EC) and eyes open (EO) states, two traditionally used baseline indices. Here we investigated healthy subjects (n = 27) in EC and EO employing a four-step analytic approach to the EEG: (1) group ICA to extract independent components (ICs), (2) standardized low-resolution tomography analysis (sLORETA) for cortical source localization of IC network nodes, followed by (3) graph theory for functional connectivity estimation of epochwise IC band-power, and (4) circumscribing IC similarity measures via hierarchical cluster analysis and multidimensional scaling (MDS). Our proof-of-concept results on alpha-band power demonstrate five statistically clustered groups with frontal, central, parietal, occipitotemporal, and occipital sources. Importantly, during EO compared with EC, graph analyses revealed two salient functional networks with frontoparietal connectivity: a more medial network with nodes in the mPFC/precuneus which overlaps with the "default-mode network" (DMN), and a more lateralized network comprising the middle frontal gyrus and inferior parietal lobule, coinciding with the "dorsal attention network" (DAN). Furthermore, a separate MDS analysis of ICs supported the emergence of a pattern of increased proximity (shared information) between frontal and parietal clusters specifically for the EO state. We propose that the disclosed component groups and their source-derived EEG functional connectivity maps may be a valuable method for elucidating direct neuronal (electrophysiological) RSNs in healthy people and those suffering from brain disorders.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it