Do We Really Need Robust and Alternative Inference Methods for Brain MRI?

Area of Science:

• Neuroimaging
• Statistical Inference
• Medical Imaging

Background:

• Voxel-wise statistical inference is crucial for quantitative multi-modal brain imaging.
• The general linear model (GLM) is widely used but relies on Gaussian error assumptions.
• Alternative inference methods with relaxed distributional assumptions exist but face practical challenges.

Purpose of the Study:

• To discuss challenges in applying robust and alternative statistical methods to medical imaging inference.
• To characterize conditions necessitating these alternative approaches.
• To introduce a quantitative framework for empirically justifying inference method selection.

Main Methods:

• Review of statistical inference approaches in medical imaging.
• Discussion of challenges associated with non-Gaussian error assumptions.
• Development of a novel quantitative framework for method selection.

Main Results:

• Relaxing Gaussian assumptions in brain imaging inference can reduce statistical power and increase computational complexity.
• Robust and non-parametric methods are necessary under specific conditions not met by standard GLM.
• A new framework is proposed to empirically guide the choice between standard and alternative inference methods.

Conclusions:

• The selection of statistical inference methods in medical imaging requires careful consideration of distributional assumptions.
• Robust statistical methods offer valuable alternatives to the traditional GLM when assumptions are violated.
• The proposed framework aids researchers in making informed decisions for more reliable brain imaging analysis.