The Interest and Usefulness of Resting State fMRI in Brain Connectivity Research
Bibliographic record
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a cornerstone in brain connectivity research since its introduction in the mid-1990s by Bharat Biswal and colleagues. A key advantage of rs-fMRI is its ability to detect functional connectivity without requiring task performance, making it particularly valuable for studying populations such as children, the elderly, or individuals with severe cognitive impairments. Ongoing advancements in rs-fMRI methodologies and analytical techniques continue to propel brain connectivity research into new frontiers. The non-invasive, versatile, and robust nature of rs-fMRI ensures its continued relevance in both research and clinical settings. As we refine our approaches, the potential of rs-fMRI to transform our understanding of the brain remains vast, promising new insights into the intricate dynamics of brain connectivity across the lifespan and in diverse clinical populations.
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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 | 0.019 | 0.556 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".