Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure
Bibliographic record
Abstract
The hierarchical organization of brain networks can be captured by clustering time series using multiple numbers of clusters, or scales, in resting-state functional magnetic resonance imaging. However, the systematic examination of all scales is a tedious task. Here, I propose a method to select a limited number of scales that are representative of the full hierarchy. A bootstrap analysis is first performed to estimate stability matrices, which quantify the reliability of the clustering for every pair of brain regions, over a grid of possible scales. A subset of scales is then selected to approximate linearly all stability matrices with a specified level of accuracy. On real data, the method was found to select a relatively small (~7) number of scales to explain 95% of the energy of 73 scales ranging from 2 to 1100 clusters. The number of selected scales was very consistent across 43 subjects, and the actual scales also showed some good level of agreement. This approach thus provides a principled approach to mine hierarchical brain networks, in the form of a few scales amenable to detailed examination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".