Bibliographic record
Abstract
Abstract Brain organization can be measured across multiple spatial and temporal scales where each scale affects the other in the emergent functions that are known as cognition. As a complex adaptive system, the interplay of these scales in the brain represents the information that ultimately supports what one thinks and does. The dynamics of these multiscale operations can be quantified with measures of complexity, which are sensitive to the balance between information that is coded in local cell populations and that is captured in the network interactions between populations. This local versus global balance has its foundation in the structural connectivity of the brain, which is then realized through the dynamics of cell populations and their ensuing interactions with other populations. Considering brain function and cognition in this way enables a different perspective on the changes in cognitive function in aging. Changes in brain signal complexity from childhood to adulthood were assessed in two independent studies. Both showed that maturation is accompanied by an overall increase in signal complexity, which also correlated with more stable and accurate cognitive performance. There was some suggestion that the maximal change occurs in medial posterior cortical areas, which have been considered “network hubs” of the brain. In extending to the study of healthy aging, a scale-dependent change in brain complexity was observed across three independent studies. Healthy aging brings a shift in local and global balance, where more information is coded in local dynamics and less in global interactions. This balance is associated with better cognitive performance and, interestingly, in a more active lifestyle. It also seems that the lack of this shift in local and global balance is predictive of worse cognitive performance and potentially predictive of additional decline indicative of dementia.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".