A Sticky Weighted Regression Model for Time-Varying Resting-State Brain Connectivity Estimation
Bibliographic record
Abstract
Despite recent progress on brain connectivity modeling using neuroimaging data such as fMRI, most current approaches assume that brain connectivity networks have time-invariant topology/coefficients. This is clearly problematic as the brain is inherently nonstationary. Here, we present a time-varying model to investigate the temporal dynamics of brain connectivity networks. The proposed method allows for abrupt changes in network structure via a fused least absolute shrinkage and selection operator (LASSO) scheme, as well as recovery of time-varying networks with smoothly changing coefficients via a weighted regression technique. Simulations demonstrate that the proposed method yields improved accuracy on estimating time-dependent connectivity patterns when compared to a static sparse regression model or a weighted time-varying regression model. When applied to real resting-state fMRI datasets from Parkinson's disease (PD) and control subjects, significantly different temporal and spatial patterns were found to be associated with PD. Specifically, PD subjects demonstrated reduced network variability over time, which may be related to impaired cognitive flexibility previously reported in PD. The temporal dynamic properties of brain connectivity in PD subjects may provide insights into brain dynamics associated with PD and may serve as a potential biomarker in future studies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".