Neural Transition Metric in fMRI: Categorization and Application
Bibliographic record
Abstract
Recent advances in technology allow us to examine brain network dynamics in fMRI on a large-scale. Our lab has recently developed a data-driven method, the neural transition metric (Tseng & Poppenk, 2020), which allows us to identify transitions in individuals’ mental states. My project employed the neural transition metric to 1. investigate the distinction between the externally and internally driven transitions (i.e., transitions induced by task stimuli vs resulted from internal mind-wandering), and 2. examine cognitive dynamics between the novel and repeated viewings of naturalistic movie stimuli. I found that there was no difference in brain correlates for the internally and externally driven transitions, whereas the event related transitions were associated with distinct profile of brain activations compared to the others. Moreover, through comparing the properties of neural transitions between the novel and repeated viewing, I found that movie stimuli are less effective at aligning viewers’ cognition in repeated viewing. This suggests that with repeated viewing, participants’ thoughts became more idiosyncratic and more internally driven. Additionally, degree of conformities (i.e., the alignment of transitions timing between the individual and the group) in the novel and repeated viewings were correlated with volumes of the anterior/posterior hippocampus (a/pHPC) and amygdala, such that individuals with bigger aHPC and amygdala displayed higher conformity in the novel run, whereas individuals with bigger pHPC have lower conformity in the repeated run. This collectively demonstrated that our neural transition metric could have a broader application in experiments involving naturalistic stimuli by offering unique insights into our cognitive dynamics.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".