Resting‐state functional connectivity <scp>MRI</scp> reveals active processes central to cognition
Bibliographic record
Abstract
Analysis of spontaneously correlated low-frequency activity fluctuations across the brain using functional magnetic resonance imaging (MRI)-commonly referred to as resting-state functional connectivity (RSFC) MRI-was initially seen as a useful tool for mapping functional-anatomic networks in the living human brain, characterizing brain changes and differences in clinical populations, and studying comparative anatomy across species. However, little was known about the potential relevance of RSFC to cognitive processes. Indeed, there has been considerable controversy and debate as to the utility of studying the resting-state in cognitive neuroscience. However, recent work has shown that RSFC, rather than merely reflecting passive or epiphenomenal activity within underlying functional-anatomic networks, reveals important dynamic processes that play an active role in cognition. RSFC has been associated with individual differences in a number of behavioral and cognitive domains, including perception, language, learning and memory, and the organization of conceptual knowledge. In this article, we review and integrate the latest research demonstrating that RSFC is functionally relevant to human behavior and higher-level cognition, and propose a hypothesis regarding its mechanism of action on functional network dynamics and cognition. We conclude that RSFC MRI will be an invaluable tool for future discovery of the fundamental neurocognitive interactions that underlie cognition. WIREs Cogn Sci 2014, 5:233-245. doi: 10.1002/wcs.1275 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".