The lifelong nonlinear development of spatial variability of brain signals
Bibliographic record
Abstract
The physiological information carried by brain signals is distinguished by their mean and variability. Research has indicated that both the variability of local signals and the spatial mean of the whole-brain signal (known as the global signal, GS) are sensitive to brain development. This raises the question of whether the spatial variability of the whole-brain signal, referred to as global variability (GV), could potentially serve as a more specific marker of brain development. We first established the reliability of GV and its topography (GVtopo) using data from the Human Connectome Project (HCP). Then, we examined the age-related patterns of GV and GVtopo in the Nathan Kline Institute Rockland Sample (NKI-RS; N = 968, ages ranging from 6 to 85 years) and validated these findings in an independent dataset from Southwest University (SALD; N = 492, ages ranging from 19 to 80 years). Our results demonstrated the robustness of GV and GVtopo, with intra-class correlation coefficients surpassing 0.61. Both GV and GVtopo exhibited distinct non-linear developmental trajectories, differring from those of GS and its topography. Furthermore, GV demonstrated substantial age-predictive capability, underscoring its potential as a valuable marker of brain development and its significance for future age-related research.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".