Early-Stage White Matter Lesions Detected by Multispectral MRI Segmentation Predict Progressive Cognitive Decline

Abstract

White matter lesions (WML) are the main brain imaging surrogate of cerebral small-vessel disease. A new MRI tissue segmentation method, based on a discriminative clustering approach without explicit model-based added prior, detects partial WML volumes, likely representing very early-stage changes in normal-appearing brain tissue. This study investigated how the different stages of WML, from a "pre-visible" stage to fully developed lesions, predict future cognitive decline. MRI scans of 78 subjects, aged 65-84 years, from the Leukoaraiosis and Disability (LADIS) study were analyzed using a self-supervised multispectral segmentation algorithm to identify tissue types and partial WML volumes. Each lesion voxel was classified as having a small (33%), intermediate (66%), or high (100%) proportion of lesion tissue. The subjects were evaluated with detailed clinical and neuropsychological assessments at baseline and at three annual follow-up visits. We found that voxels with small partial WML predicted lower executive function compound scores at baseline, and steeper decline of executive scores in follow-up, independently of the demographics and the conventionally estimated hyperintensity volume on fluid-attenuated inversion recovery images. The intermediate and fully developed lesions were related to impairments in multiple cognitive domains including executive functions, processing speed, memory, and global cognitive function. In conclusion, early-stage partial WML, still too faint to be clearly detectable on conventional MRI, already predict executive dysfunction and progressive cognitive decline regardless of the conventionally evaluated WML load. These findings advance early recognition of small vessel disease and incipient vascular cognitive impairment.

Keywords: MRI; cognition; executive functions; image analysis; neuropsychology; small vessel disease; white matter lesions.

Figure 1

White matter lesions (WML) at a middle level height. (A) FLAIR image for a given subject. (B) Conventionally estimated WML. (C–E) Estimated WML, using the proposed segmentation algorithm, for full, intermediate, and small proportion of lesion. (F–J) Similar images for the zoomed portion depicted by the white box in (A).

Figure 2

White matter lesions (WML) in the centrum semiovale. (A) FLAIR image for a given subject. (B) Conventionally estimated WML. (C–E) Estimated WML using the proposed segmentation algorithm, for full, intermediate, and small proportion of lesion. (F–J) Similar images for the zoomed portion depicted by the white box in (A).

Figure 3

Comparison of the segmentation methods. This image shows the segmentation obtained using the semi-automated volumetric analysis (V_FLAIR) and the discriminative clustering (V_DCHARD) for the subject of Figure 1. The regions depicted in green correspond to the overlapping segmentation between both approaches. In red are shown regions classified as lesion only by the conventional method, while blue corresponds to voxel classified as lesion only by DC.