A Multicollinearity-Aware Signal-Processing Framework for Cross-$β$ Identification via X-ray Scattering of Alzheimer's Tissue
This work addresses the problem of automated detection of Alzheimer's disease biomarkers in experimental data for researchers, though it is incremental as it builds on existing methods for handling correlated features in data-limited scenarios.
The paper tackled the challenge of automatically detecting cross-β structural inclusions, a hallmark of Alzheimer's disease, in X-ray scattering profiles of human brain tissue by developing a three-stage classification framework that achieved an F1-score of 84.30% using only 11 features and 174 parameters.
X-ray scattering measurements of in situ human brain tissue encode structural signatures of pathological cross-$β$ inclusions, yet systematic exploitation of these data for automated detection remains challenging due to substrate contamination, strong inter-feature correlations, and limited sample sizes. This work develops a three-stage classification framework for identifying cross-$β$ structural inclusions-a hallmark of Alzheimer's disease-in X-ray scattering profiles of post-mortem human brain. Stage 1 employs a Bayes-optimal classifier to separate mica substrate from tissue regions on the basis of their distinct scattering signatures. Stage 2 introduces a multicollinearityaware, class-conditional correlation pruning scheme with formal guarantees on the induced Bayes risk and approximation error, thereby reducing redundancy while retaining class-discriminative information. Stage 3 trains a compact neural network on the pruned feature set to detect the presence or absence of cross-$β$ fibrillar ordering. The top-performing model, optimized with a composite loss combining Focal and Dice objectives, attains a test F1-score of 84.30% using 11 of 211 candidate features and 174 trainable parameters. The overall framework yields an interpretable, theory-grounded strategy for data-limited classification problems involving correlated, high-dimensional experimental measurements, exemplified here by X-ray scattering profiles of neurodegenerative tissue.