IndivSTATIS: A multivariate approach to analyze brain network configurations with individualized parcellation
Bibliographic record
Abstract
A critical step in the analysis of large-scale functional brain networks in neuroimaging is parcellation, which defines the nodes of a brain network. Group or atlas-based parcellation schemes use a shared common space, ensuring that each individual has the same number of brain parcels, which facilitates standard analytic approaches. However, studies reveal individual differences in the boundaries of brain areas. Extracting signals using atlas-based schemes can result in varying levels of blurring of signals across homogeneous areas within a specific individual's brain. Individualized parcellation schemes can be obtained when sufficient data are available; however, these approaches introduce a significant analytical challenge: the number of parcels and networks differ across individuals. Here, we introduce IndivSTATIS, a new multivariate method based on the STATIS framework, designed to integrate individualized parcellation schemes while maintaining comparability across participants in a shared component space. The resulting network/node component scores can be used to predict individual differences measures (e.g., age, behavior). By allowing individualized parcellations to be compared within a common component space, IndivSTATIS provides a solution for incorporating individual network variability into larger studies, with potential to improve the sensitivity and interpretability of functional brain markers across both basic neuroscience and clinical applications.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".