White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (r s = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (r s = 0.41), and many outlier WMH volumes were detected when exploring withinperson trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: r s = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

White matter hyperintensities of presumed vascular origin (WMH) are frequently found in MRIs of patients with various neurological and vascular disorders, but also in healthy elderly subjects. Although automated methods have been developed to replace the challenging task of manually segmenting the WMH, there is still no consensus on which validated algorithm(s) should be used. In this study, we validated and compared three freely available methods for WMH extraction: FreeSurfer, UBO Detector, and the Brain Intensity AbNormality Classification Algorithm, BIANCA (with the two thresholding options: global thresholding vs. LOCally Adaptive Threshold Estimation (LOCATE)) using a standardized protocol. We applied the algorithms to longitudinal MRI data (2D FLAIR, 3D FLAIR, T1w sMRI) of cognitively healthy older people (baseline N = 231, age range 64 - 87 years) with a relatively low WMH load. As a reference for the segmentation accuracy of the algorithms, completely manually segmented gold standards were used separately for each MR image modality. To validate the algorithms, we correlated the automatically extracted WMH volumes with the Fazekas scores, chronological age, and between the time points. In addition, we analyzed conspicuous percentage WMH volume increases and decreases in the longitudinal data between two measurement points to verify the segmentation reliability of the algorithms. All algorithms showed a moderate correlation with chronological age except BIANCA with the 2D FLAIR image input only showed a weak correlation. FreeSurfer fundamentally underestimated the WMH volume in comparison with the gold standard as well as with the other algorithms, and cannot be considered as an accurate substitute for manual segmentation, as it also scored the lowest value in the DSC compared to the other algorithms. However, its WMH volumes correlated strongly with the Fazekas scores and showed no conspicuous WMH volume increases and decreases between measurement points in the longitudinal data. BIANCA performed well with respect to the accuracy metrics - especially the DSC, H95, and DER. However, the correlations of the WMH volumes with the Fazekas scores compared to the other algorithms were weaker. Further, we identified a significant amount of outlier WMH volumes in the within-person change trajectories with BIANCA. The WMH volumes extracted by UBO Detector achieved the best result in terms of cost-benefit ratio in our study. Although there is room for optimization with respect to segmentation accuracy (especially for the metrics DSC, H95 and DER), it achieved the highest correlations with the Fazekas scores and the highest ICCs. Its performance was high for both input modalities, although it relies on a built-in single-modality training dataset, and it showed reliable WMH volume estimations across measurement points.

Markers of cerebral small vessel disease (CSVD) have previously been associated with age-related cognitive decline. Using longitudinal data of cognitively healthy, older adults ( N = 216, mean age at baseline = 70.9 years), we investigated baseline status and change in white matter hyperintensities (WMH) (total, periventricular, deep), normal appearing white matter (NAWM), brain parenchyma volume (BPV) and processing speed over seven years as well as the impact of different covariates by applying latent growth curve (LGC) models. Generally, we revealed a complex pattern of associations between the different CSVD markers. More specifically, we observed that changes of deep WMH (dWMH), as compared to periventricular WMH (pWMH), were more strongly related to the changes of other CSVD markers and also to baseline processing speed performance. Further, the number of lacunes rather than their volume reflected the severity of CSVD. With respect to the studied covariates, we revealed that higher education had a protective effect on subsequent total WMH, pWMH, lacunar number, NAWM volume, and processing speed performance. The indication of antihypertensive drugs was associated with lower lacunar number and volume at baseline and the indication of antihypercholesterolemic drugs came along with higher processing speed performance at baseline. In summary, our results confirm previous findings, and extend them by providing information on true within-person changes, relationships between the different CSVD markers and brain-behavior associations. The moderate to strong associations between changes of the different CSVD markers indicate a common pathological relationship and, thus, support multidimensional treatment strategies.

No abstract