Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers
Bibliographic record
Abstract
Sparse multivariate regression techniques like Lasso and Elastic Net are among the most popular approaches for the identification of biomarkers related to brain diseases like Alzheimer's. Because they use L1norm to enforce sparsity, these approaches are often sensitive to differences in voxel intensities within the same scan or across subjects. Also, when few samples are available, such approaches can select voxels that are only correlated by chance, leading to disconnected features that do not correspond to any significant brain structure. To address these challenges, we propose a novel sparse regression method that uses the L0norm for sparse regularization, and imposes spatial consistency constraints on the selected features without requiring an atlas of pre-defined regions. This method uses an efficient optimization strategy based on the Alternating Direction Method of Multipliers (ADMM), that can scale to large data matrices. The performance of the proposed method is evaluated using synthetic data and 3429 T1-weighted (MP-RAGE) images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show our method to outperform Lasso and Elastic Net regression in the recovery of spatially consistent features corresponding to known neuroimaging biomarkers.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".