A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging
Bibliographic record
Abstract
Abstract Introduction Hidden Markov models are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse hidden Markov models have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: intensity-based states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods We addressed this problem by introducing a new hidden Markov model that defines states based on full functional connectivity profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on intensity-based or summed functional connectivity states using the HCP unrelated 100 functional magnetic resonance imaging “resting state” dataset. Results Our ‘full functional connectivity’ model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models can be used to investigate brain states noninvasively. Previous models “recover” connectivity from intensity-based hidden states, or from connectivity ‘summed’ across nodes. Here we introduce a novel connectivity-based hidden Markov model and show how it can reveal true connectivity hidden states under minimal assumptions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".