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 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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".