Association of Plasma Oligomerized Amyloid-β and Cerebral White Matter Lesions in a Health Screening Population

Background: Cerebral white matter lesions (WML) are related to a higher risk of vascular and Alzheimer’s dementia. Moreover, oligomerized amyloid-β (OAβ) can be measured from blood for dementia screening. Objective: We aimed to investigate the relationship of plasma OAβ levels with clinical and radiological variables in a health screening population. Methods: WML, other volumetric parameters of magnetic resonance images, cognitive assessment, and plasma OAβ level were evaluated. Results: Ninety-two participants were analyzed. The majority of participants’ clinical dementia rating was 0 or 0.5 (96.7%). White matter hyperintensities (WMH) increased with age, but OAβ levels did not (r2 = 0.19, p < 0.001, r2 = 0.03, p = 0.10, respectively). No volumetric data, including cortical thickness/hippocampal volume, showed any significant correlation with OAβ. Log-WMH volume was positively correlated with OAβ (r = 0.24, p = 0.02), and this association was significant in the periventricular area. White matter signal abnormalities from 3D-T1 images were also correlated with the OAβ in the periventricular area (p = 0.039). Multivariate linear regression showed that log-WMH values were independently associated with OAβ (B = 0.879 (95% confidence interval 0.098 –1.660, p = 0.028)). Higher tertiles of WMH showed higher OAβ levels than lower tertiles showed (p = 0.044). Using a cutoff of 0.78 ng/mL, the high OAβ group had a larger WMH volume, especially in the periventricular area, than the low OAβ group (p = 0.036). Conclusion: Both WML and plasma OAβ levels can be early markers for neurodegeneration in the healthcare population. The lesions, especially in the periventricular area, might be related to amyloid pathogenesis, which strengthens the importance of WML in the predementia stage.

Sex Differences In The Association Between Hemoglobin A1c And Cerebral White Matter Lesions In The General Japanese Population

The influence of diabetes and associated sex differences on cerebral white matter lesions (WMLs) is unclear. We used data from a cross-sectional study uploaded to the DATADRYAD website by Shinkawa et al. to investigate differences in the association between hemoglobin A1c (HbA1c) levels and cerebral WMLs between men and women. The average age of all participants was56.4±11.5years old, and approximately 51.89 % of them were men. A linear relationship between HbA1c and cerebral WMLs was detected in men. Fully adjusted binary logistic regression showed no association of HbA1c with cerebral WMLs in men. A nonlinear relationship between HbA1c and cerebral WMLs was detected in women, whose cutoff point was 5.6%. The effect sizes and confidence intervals of the left and right sides of the inflection point were OR=0.21 (95%CI 0.06, 0.69, P=0.0098) and OR=3.5 (95%CI 1.50, 8.15, P=0.0037), respectively. In the higher HbA1c group, further subgroup analysis showed a stronger association between HbA1c and cerebral WMLs in women (OR=3.83, 95%CI 1.68, 8.72 P=0.0014) than in men (OR=1.02, 95%CI 0.76, 1.36 P=0.8986) (P for interaction with sex was 0.0004). A stronger effect of HbA1c on the risk of cerebral WMLs in women than in men was found in the higher HbA1c group.

Evaluation of Ultrafast Wave-CAIPI 3D FLAIR in the Visualization and Volumetric Estimation of Cerebral White Matter Lesions

BACKGROUND AND PURPOSETo evaluate an ultrafast 3D-FLAIR sequence using Wave-CAIPI encoding (Wave-FLAIR) compared to standard 3D-FLAIR in the visualization and volumetric estimation of cerebral white matter lesions in a clinical setting.MATERIALS AND METHODS42 consecutive patients underwent 3T brain MRI including standard 3D-FLAIR (acceleration factor R=2, scan time TA=7:15 minutes) and resolution-matched ultrafast Wave-FLAIR sequences (R=6, TA=2:45 minutes for the 20-ch coil; R=9, TA=1:50 minutes for the 32-ch coil) as part of clinical evaluation for demyelinating disease. Automated segmentation of cerebral white matter lesions was performed using the Lesion Segmentation Tool in SPM. Student’s t-test, intra-class correlation coefficient (ICC), relative lesion volume difference (LVD) and Dice similarity coefficients (DSC) were used to compare volumetric measurements between sequences. Two blinded neuroradiologists evaluated the visualization of white matter lesions, artifact and overall diagnostic quality using a predefined 5-point scale.RESULTSStandard and Wave-FLAIR sequences showed excellent agreement of lesion volumes with an ICC of 0.99 and DSC of 0.97±0.05 (range 0.84 to 0.99). Wave-FLAIR was non-inferior to standard-FLAIR for visualization of lesions and motion. The diagnostic quality for Wave-FLAIR was slightly greater than standard-FLAIR for infratentorial lesions (p<0.001), and there was less pulsation artifact on Wave-FLAIR compared to standard FLAIR (p<0.001).CONCLUSIONSUltrafast Wave-FLAIR provides superior visualization of infratentorial lesions while preserving overall diagnostic quality and yields comparable white matter lesion volumes to those estimated using standard-FLAIR. The availability of ultrafast Wave-FLAIR may facilitate the greater use of 3D-FLAIR sequences in the evaluation of patients with suspected demyelinating disease.

Heterogeneity of Cerebral White Matter Lesions and Clinical Correlates in Older Adults

Background and Purpose: Cerebral white matter signal abnormalities (WMSAs) are a significant radiological marker associated with brain and vascular aging. However, understanding their clinical impact is limited because of their pathobiological heterogeneity. We determined whether use of robust reliable automated procedures can distinguish WMSA classes with different clinical consequences. Methods: Data from generally healthy participants aged >50 years with moderate or greater WMSA were selected from the Human Connectome Project-Aging (n=130). WMSAs were segmented on T1 imaging. Features extracted from WMSA included total and regional volume, number of discontinuous clusters, size of noncontiguous lesion, contrast of lesion intensity relative to surrounding normal appearing tissue using a fully automated procedure. Hierarchical clustering was used to classify individuals into distinct classes of WMSA. Radiological and clinical variability was evaluated across the individual WMSA classes. Results: Class I was characterized by multiple, small, lower-contrast lesions predominantly in the deep WM; class II by large, confluent lesions in the periventricular WM; and class III by higher-contrast lesions restricted to the juxtaventricular WM. Class II was associated with lower myelin content than the other 2 classes. Class II was more prevalent in older subjects and was associated with a higher prevalence of hypertension and lower physical activity levels. Poor sleep quality was associated with a greater risk of class I. Conclusions: We classified heterogeneous subsets of cerebral white matter lesions into distinct classes that have different clinical risk factors. This new method for identifying classes of WMSA will be important in understanding the underlying pathophysiology and in determining the impact on clinical outcomes.

White Matter Lesions in Adults – a Differential Diagnostic Approach

Objective Cerebral white matter lesions on MRI in adults are a common finding. On the one hand, they may correspond to a clinically incidental feature, be physiologically or age-associated, or on the other hand they may be the overture to a severe neurological disease. With regard to pathophysiological aspects, practical hints for the differential diagnostic interpretation of lesions in daily clinical practice are presented. Material and Methods With special regard to the vascular architecture and supply of the cerebral white matter, physiological structures are schematically represented and pathophysiological processes are highlighted by comparative image analysis of equally angulated MR sequences. Results The most frequent vascular, inflammatory, metabolic, and neoplastic disease entities are presented on the basis of characteristic imaging findings and corresponding clinical- neurological constellations. The details of signal intensities and localization essential for differential diagnosis are highlighted. Conclusion By means of comparative image analysis and the recognition of characteristic lesion patterns, taking into account anatomical principles and pathophysiological processes, the differential diagnostic classification of cerebral white matter lesions and associated diseases can be significantly facilitated. The additional consideration of clinical and laboratory findings is essential. Key Points: Citation Format