Estimating cortical connectivity in functional near infrared spectroscopy using multivariate autoregressive modeling
Bibliographic record
Abstract
Performing cognitive tasks requires close interaction between cortical regions of the brain. Monitoring the functional connectivity during a particular task could contribute to a better understanding of the role and interactions of underlying brain regions. In this paper, we employ time variant Multi-variate Autoregressive (MAR) modeling to establish functional connectivity between regions involved in a cognitive task. Data is collected from neonates whose brain activity was monitored by functional Near Infrared Spectroscopy (fNIRS) while being exposed to 2 different types of auditory stimuli. The method was applied to data from 3 subjects on a predefined set of channels known to be involved in auditory and structural processing. The connectivity analysis reveals a common connectivity pattern among the subjects which is neuroanatomically and functionally relevant. Moreover, investigation of temporal evolution of connectivity between temporal and frontal areas shows an increase in connection strength towards the end of the experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".