Linear Neuroimaging Models

Uses

Risks & contraindications

Evidence quality

Highlighted claims

• Under collinearity, linear-model weights may reflect shared variance rather than region-specific effects. — Improving clinical interpretability of linear neuroimaging models through feature whitening
• Logistic regression was chosen because it is linear and interpretable. — Improving clinical interpretability of linear neuroimaging models through feature whitening
• Correlated input features weaken the clinical interpretability of linear-model coefficients. — Improving clinical interpretability of linear neuroimaging models through feature whitening
• Linear models are widely used in computational neuroimaging because their coefficients can link brain measurements to clinical outcomes. — Improving clinical interpretability of linear neuroimaging models through feature whitening
• The study's conclusions are directly established for L2-regularized logistic regression rather than nonlinear models. — Improving clinical interpretability of linear neuroimaging models through feature whitening