Frequency-specific brain network architecture in resting-state fMRI
Bibliographic record
Abstract
Abstract The analysis of brain function in resting-state network (RSN) models, which has been found in the functional connectivity pattern of resting-state functional magnetic resonance imaging (rs-fMRI), is sufficiently powerful for studying large-scale functional integration of the brain. Although there has been an increasing interest in the relatively higher frequency of rs-fMRI data, the network architecture has been regarded as the same through different frequency bands in RSN-based research. This study examined whether the network architecture changes with frequency. The blood-oxygen-level-dependent (BOLD) signal was decomposed into four frequency bands (ranging from 0.007 Hz to 0.438 Hz), for each of which the clustering algorithm was applied. The best clustering number was selected for each frequency-band based on the overlap ratio with task activation maps provided by Neurosynth. The results demonstrate that (1) resting-state BOLD signals have frequency-specific network architecture, that is, the networks finely subdivided in the lower frequency bands are integrated into fewer networks in higher frequency bands rather than reconfigured, and (2) the default mode network(DMN) is the only associative network that has a strong enough architecture to survive the increasing noise in higher frequency bands. These findings provide a novel framework that enables a better understanding of brain function through the multiband frequency analysis of ultra-slow rs-fMRI data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".