Second-order instantaneous causal analysis of spontaneous MEG
Bibliographic record
Abstract
Despite decades of research, discovering instantaneous causal relationships from observational brain imaging data, such as spontaneous MEG energies or fMRI, remains a difficult problem. Popular methods, such as Granger Causality and Non-Gaussian Structural Equation Models (SEM), either are unable to handle instantaneous effects or do not work because the data are not non-Gaussian enough. Here, we propose a model with instantaneous causality for temporally dependent variables; these are both very common properties in neuroimaging data. Then, we propose a method to estimate the causal directions based on likelihood ratios, which are related to mutual information between the residual and data variables. We thus construct a simple decision criterion that allows for instantaneous causal discovery in time-dependent data. The proposed method is computationally and conceptually very simple, and we show with simulated data that it performs well even in the case of limited sample sizes, presumably due to the general optimality properties of likelihood. We further apply it to an MEG dataset from the Cam-CAN repository, for which the method gives consistent causal directionalities of energies both intra-subject and inter-subject, as measured by split-half tests. It also gives better performance than Granger causality and non-Gaussian SEM methods in a brain age prediction task. The results also demonstrate that our method might be useful in analyzing causal brain connectomes in functional brain-imaging data.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".