Exploring Correlation-Based Brain Networks with Adaptive Signed Random Walks
Bibliographic record
Abstract
Abstract The human brain is a highly connected network with complex patterns of correlated and anticorrelated activity. Analyzing functional connectivity matrices derived from neuroimaging data can provide insights into the organization of brain networks and their association with cognitive processes or disorders. Common approaches, such as thresholding or binarization, often disregard negative connections, which may result in the loss of critical information. This study introduces an adaptive signed random walk (ASRW) model for analyzing correlation- based brain networks that incorporates both positive and negative connections. The model calculates transition probabilities between brain regions as a function of their activities and connection strengths, dynamically updating probabilities based on the differences in node activity and connection strengths at each time step. Results show that the classical random walk approach, which only considers the absolute value of connections, underestimates the mean first passage time (MFPT) compared to the proposed ASRW model. Our model captures a wide range of interactions and dynamics within the network, providing a more comprehensive understanding of its structure and function. This study suggests that considering both positive and negative connections, has the potential to offer valuable insights into the interregional coordination underlying various cognitive processes and behaviors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".