Large-scale Signal Propagation Modes in the Human Brain
Bibliographic record
Abstract
Abstract The brain’s large-scale temporal dynamics play a crucial role in understanding its operations, but developing a cohesive framework to integrate the potentially extensive array of spatiotemporal patterns remains elusive. Our work addresses this gap by identifying multiple large-scale signal propagation modes in resting-state fMRI time series under a unified methodological framework. We found five distinct modes that effectively predict future blood-oxygen-level-dependent (BOLD) signal dynamics, each reconciling transitions between well-known large-scale brain networks into coherent spatiotemporal units. By utilizing these coherent units, our approach circumvents the need to explore combinatorial explosion of transitions between potential states, enabling parsimonious modeling and effective prediction of whole-brain temporal evolution. Each mode captures specific operational dimensions of neural resource allocation, ensuring their interpretability. Importantly, we showed that complex spatiotemporal features emerge from the superposition of these few propagation modes, unifying a broad spectrum of well-known brain dynamics phenomena. Our results lay the groundwork for a unified framework to understand large-scale spatiotemporal brain organization. Moreover, individual differences in mode expression profiles correlate with general cognitive abilities, exhibit heritability, and demonstrate cross-task stability, underscoring their functional significance. This could lead to efficient methods for characterizing functional fingerprints and advancing diagnostic approaches for neurological disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".