Estimating uncertainty in read‐out patterns: Application to controls‐based denoising and voxel‐based morphometry patterns in neurodegenerative and neuropsychiatric diseases

Abstract

Quantifying pathology‐related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read‐out patterns in terms of the expected squared Euclidean distance between the read‐out pattern and the unknown “true” pattern (squared standard error of the read‐out pattern, SE2). Using SE2, we predicted and optimized the net benefit (NBe) of the recently suggested method controls‐based denoising (CODE) by weighting patterns of nonpathological variance (NPV). Multi‐center MRI (1192 patients with various neurodegenerative and neuropsychiatric diseases, 1832 healthy controls) were analysed with voxel‐based morphometry. For each pathology, accounting for SE2, NBe correctly predicted classification improvement and allowed to optimize NPV pattern weights. Using these weights, CODE improved classification performances in all but one analyses, for example, for prediction of conversion to Alzheimer's disease (AUC 0.81 vs. 0.75, p = .01), diagnosis of autism (AUC 0.66 vs. 0.60, p < .001), and of major depressive disorder (AUC 0.62 vs. 0.50, p = .03). We conclude that the degree of uncertainty in a read‐out pattern should generally be reported in PES‐based analyses and suggest using weighted CODE as a complement to PES‐based analyses.