FMRI group studies of brain connectivity via a group robust Lasso
Bibliographic record
Abstract
Inferring effective brain connectivity from neuroimaging data such as functional Magnetic Resonance Imaging (fMRI) has been attracting increasing interest due to its critical role in understanding brain functioning. Incorporating sparsity into connectivity modeling to make models more biologically realistic and performing group analysis to deal with inter-subject variability are still challenges associated with fMRI brain connectivity modeling. To address the above two crucial challenges, the attractive computational and theoretical properties of the least absolute shrinkage and selection operator (LASSO) in sparse linear regression provide a suitable starting point. We propose a group robust LASSO (grpRLASSO) model by combining advantages of the popular group-LASSO and our recently developed robust-LASSO. Here group analysis is formulated as a grouped variable selection procedure. Superior performance of the proposed grpRLASSO in terms of group selection and robustness is demonstrated by simulations with large noise variance. The grpRLASSO is also applied to a real fMRI data set for brain connectivity study in Parkinson's disease, resulting in biologically plausible networks.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".