Modelling Theta-Band Connectivity Between Occipital and Frontal Lobes: A Methodological MEG Study
Bibliographic record
Abstract
Abstract Measuring functional connectivity between cortical regions of the human brain has become an important area of research. Modern theory suggests that brain networks exhibit non-stationarity, constantly forming and reforming depending on task demands. A robust means of determining effective connectivity in the short-lived neural responses that occur in event related paradigms would allow the investigation of event related cortico-cortical dynamics. We present such a mathematical model of wave propagation, motivated by current neuroscience literature, and demonstrate the utility of the method in a clinical sample of schizophrenia patients. MEG data were acquired in 10 patients with schizophrenia and 12 healthy controls during a relevance modulation task. Data were filtered into the theta band (4-8Hz) and source localised using a beamformer. The model was implemented using Fourier analysis methods which uncovered an event related travelling wave moving from the visual to frontal cortices. The model was validated using Monte Carlo phase randomisation and compared with another plausible model of wave propagation in the cortex. Results from the clinical sample revealed that wave speed was modulated by task condition and patients were found to have less difference between conditions (ANOVA revealing a significant interaction between group and condition, p<0.05). In conclusion, our method provides a simple and robust means to investigate event related cortico-cortical brain dynamics in individuals and groups in the task positive state.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".