On the variability of dynamic functional connectivity assessment methods
Bibliographic record
Abstract
Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly-used dFC methods. We implemented seven dFC assessment methods in Python and used them to analyze fMRI data of 395 subjects from the Human Connectome Project. We measured the pairwise similarity of dFC results using several similarity metrics in terms of overall, temporal, spatial, and inter-subject similarity. Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Surprisingly, the observed variability in dFC estimates was comparable to the expected natural variation over time, emphasizing the impact of methodological choices on the results. Our findings revealed three distinct groups of methods with significant inter-group variability, each exhibiting distinct assumptions and advantages. These findings highlight the need for multi-analysis approaches to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds, and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox that enables multi-analysis dFC assessment. This study sheds light on the impact of dFC assessment analytical flexibility, emphasizing the need for careful method selection and validation, and promoting the use of multi-analysis approaches to enhance reliability and interpretability of dFC studies.
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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 | 0.061 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".