Heading for data-driven measures of effective connectivity in functional MRI
Bibliographic record
Abstract
A recent issue in functional magnetic resonance imaging (fMRI) data analysis has been the investigation of functional brain interactivity. Two standpoints have been considered so far. On the one hand, effective connectivity describes the influence that regions exert on each other. Yet, it requires the prior definition of a structural model that often turns out to be unknown. On the other hand, functional connectivity, based on marginal correlation, sets the framework for exploratory data-driven measures of statistical interdependency between regions. Unfortunately, one usually cannot use this knowledge to infer potential patterns of effective connectivity from the data. In this abstract, we emphasize the main reason why effective connectivity remains out of reach of functional connectivity. More precisely, we show that marginal correlation is unable to deal with mediation. Using a simple instance of structural equation modeling (SEM), we demonstrate how this model entails certain patterns of functional interaction that can be discriminated by functional connectivity and how other patterns cannot be differentiated. We then introduce conditional correlation as a way to achieve such a differentiation and show how it is related to mediated interaction.
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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 | 0.038 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".