Dynamic generalized (multiple-set) structured canonical correlation analysis (dynamic GCANO): a structural equation model for simultaneous analysis of multiple-subject effective connectivity in functional neuroimaging studies
Bibliographic record
Abstract
Effective connectivity in functional neuroimaging studies is defined as the time dependent causal influence that a certain brain region of interest (ROI) exerts on another. Structural equation modeling (SEM) is regularly employed to analyze effective connectivity. In recent years, various SEM methods have been proposed to model effective connectivity. However, there has been little attempt to develop SEM methods for analyzing common patterns of effective connectivity across subjects despite the prevalence of multiple-subject research in effective connectivity. This dissertation proposes a method that fills this gap. This method is called dynamic generalized (multiple-set) structured canonical correlation analysis (dynamic GCANO). It combines generalized (multiple-set) canonical correlation analysis (GCANO) with a multivariate autoregressive time series model in a unified framework. This dissertation begins with a brief review of existing SEM techniques, and points out their limitations in analyzing multiple-subject effective connectivity data, which serves as a motivation to develop dynamic GCANO. The technical underpinnings of the proposed method are then stated, including specifications of a modeling framework and an optimization criterion for parameter estimation, which is minimized by an alternating least squares algorithm. The effectiveness of dynamic GCANO is demonstrated by analyzing both synthetic and real data sets. The former reveals reasonably good parameter recoveries by the proposed method, while the latter shows the usefulness of the method in empirical research. Several features of dynamic GCANO are highlighted through these examples. The dissertation concludes with possible extensions of the proposed method.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".