Alzheimer's and vascular disease classification using regional texture biomarkers in FLAIR MRI

Abstract

Interactions between subcortical vascular disease and dementia due to Alzheimer's disease (AD) are unclear, and clinical overlap between the diseases makes diagnosis challenging. Existing studies have shown regional microstructural changes specific to each disease, and that textures in fluid-attenuated inversion recovery (FLAIR) MRI images may characterize abnormalities in tissue microstructure. This work aims to investigate regional FLAIR biomarkers that can differentiate dementia cohorts with and without subcortical vascular disease. FLAIR and diffusion MRI (dMRI) volumes were obtained in 65 mild cognitive impairment (MCI), 21 AD, 44 subcortical vascular MCI (scVMCI), 22 Mixed etiology, and 48 healthy elderly patients. FLAIR texture and intensity biomarkers were extracted from the normal appearing brain matter (NABM), WML penumbra, blood supply territory (BST), and white matter tract regions of each patient. All FLAIR biomarkers were correlated to dMRI metrics in each region and global WML load, and biomarker means between groups were compared using ANOVA. Binary classifications were performed using Random Forest classifiers to investigate the predictive nature of the regional biomarkers, and SHAP feature analysis was performed to further investigate optimal regions of interest for differentiating disease groups. The regional FLAIR biomarkers were strongly correlated to MD, while all biomarker regions but white matter tracts were strongly correlated to WML burden. Classification between Mixed disease and healthy, AD, and scVMCI patients yielded accuracies of 97%, 81%, and 72% respectively using WM tract biomarkers. Classification between scVMCI and healthy, MCI, and AD patients yielded accuracies of 89%, 84%, and 79% respectively using penumbra biomarkers. Only the classification between AD and healthy patients had optimal results using NABM biomarkers. This work presents novel regional FLAIR biomarkers that may quantify white matter degeneration related to subcortical vascular disease, and which indicate that investigating degeneration in specific regions may be more important than assessing global WML burden in vascular disease groups.

Keywords: Alzheimer’s disease; FLAIR; Neurodegeneration; Regional biomarkers; Subcortical vascular disease; Texture analysis.

Fig. 1

Full pipeline of proposed methodology.

Fig. 2

Visuals of each region of interest in this work. (A) Mean NABM texture maps, (B) Mean BST regional texture maps, (C) Penumbra regions P1 (green) to P5 (light pink) delineated on original FLAIR MRI slices, (D) Mean tract integrity maps. Note only supratentorial regions are analysed in this work.

Fig. 3

Mean classification accuracies by feature category.

Fig. 4

SHAP feature dependence plots of the most important feature for each binary classification. Data points above y = 0 line have a higher likelihood of being classified in the positive class italicized in plot titles. Red dashed lines indicate potential threshold values of each biomarker for differentiation of data point classes (orange points belonging to the positive class and blue triangles belonging to the other class according to their true labels). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Important tract regions selected by SHAP analysis for each Mixed group classification overlaid on FLAIR atlas. Red regions indicate tracts with higher damage or lower integrity contributing to higher likelihood of positive class, blue tracts indicate the opposite. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)