Investigation of the effect of physiological factors on resting-state and task-based functional connectivity
Bibliographic record
Abstract
Abstract Understanding the brain’s functional network through functional connectivity (FC) is crucial for gaining deeper insights into brain functional mechanism and identifying a potential biomarker for diagnosing neurological disorders. Despite the development of various FC measures, their reliability under different conditions remains under-explored. Moreover, physiological noise can obscure true neural activity, and accordingly, introduce errors into FC patterns. This issue necessitates further investigation. In this study, we evaluate and compare the performance of various methods using Local Field Potential and Blood-Oxygen-Level-Dependent signals across different conditions. We also examine the impact of physiological artifacts on BOLD-FC results. Our comprehensive assessment covers multiple modalities of brain signals, diverse task paradigms, and varying noise levels. Our findings reveal that while Granger Causality-based methods exhibit significant limitations, particularly with BOLD data, multivariate techniques (e.g. partial correlation) demonstrate greater robustness in distinguishing between different types of connections within the network. Notably, our results indicate that physiological artifacts substantially affect FC values, leading to erroneous connectivity estimates, especially with bivariate methods. This research offers a foundational analysis of the effects of physiological artifacts on FC results and provides valuable insights for future 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".