Brain activity at rest: a multiscale hierarchical functional organization
Bibliographic record
Abstract
Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01- to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems.
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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".