Assessment of the Role of Connection Length on Methodological Variance in Dynamic Functional Connections
Bibliographic record
Abstract
Dynamic functional connectivity (dFC) is the study of changes in the brain’s functional organization over time. dFC has been a growing field of research due to its importance in understanding cognitive processes and its potential applications as a biomarker for neurodegenerative diseases. However, the choice of dFC assessment methodology has been found to significantly impact dFC results, putting into question the reliability of the findings using these methods. Considering recent studies revealing the impact of structural connectivity on functional connectivity, we speculated that connection length, as a structural aspect, may indirectly influence dFC magnitudes and variability. We examined the impact that connection length had on dFC variability across methods. Furthermore, these connections were inspected according to whether they are intra- or inter- brain networks (i.e., the connection is between two regions that belong to the same or different brain network). We conducted our analysis in Python using resting-state functional MRI data of 395 subjects taken from the Human Connectome Project and evaluated them using seven well-known dFC assessment methodologies. The study revealed that longer connections lead to greater variation in dFC over methods for both intra- and inter-network connections. Interestingly, short inter-network connections show increased dFC variance across methods. Current limitations of this study include using Euclidean distance as a measure of connection length and assuming functional connections are independent in parametric statistical analyses. Our investigation is a step toward understanding the factors influencing the observed inconsistency in dFC pattern estimation obtained from different methodologies.
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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 | 0.051 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".