Patients with WMH exhibit alterations in EEG microstate features that are significantly correlated with WMH burden and cognitive function, and classification models based on these microstate features show promise for practical application in WMH-related dementia.
Abstract
Objectives Although MRI is the optimal imaging method for assessing white matter hyperintensity (WMH), it lacks sensitivity to the underlying pathophysiological changes of WMH. This study aims to investigate EEG changes associated with WMH and identify potential EEG biomarkers for WMH-related dementia. Methods This cross-sectional study enrolled 90 subjects: 30 patients with WMH with dementia (WMHD), 30 patients with WMH without dementia (WMH-ND), and 30 age-and sex-matched healthy controls. Brain MRI was used to segment and quantify deep, periventricular, and total WMH volumes (DWMH, PVWMH, TWMH). Resting-state EEG data were recorded for microstate analysis. Four microstate classes (A, B, C, D) were extracted, and their occurrence, mean duration, time coverage, and transition probabilities were calculated. Partial correlation analysis was used to evaluate the associations between microstate features and WMH volume as well as MMSE scores. Based on microstate features, classification prediction models were constructed using machine learning algorithm. Results In the WMHD group, the temporal parameters (occurrence, mean duration, time coverage) of microstate C were decreased (P < 0.05–0.001), while the mean duration of microstates A and B was prolonged (P < 0.05–0.001). The WMH-ND group also showed reduced microstate C temporal parameters (P < 0.001), but exhibited compensatory increases in mean duration and time coverage of microstate D (P < 0.001). Transition probabilities toward microstate C were reduced in both patient groups (P < 0.001), and all transition probabilities toward microstate D were lower in WMHD than in WMH-ND (P < 0.05–0.001). Temporal parameters of microstates A and B and transition probabilities toward them were positively correlated with WMH volume and negatively correlated with MMSE score. Conversely, those of microstate D showed the opposite correlations. For distinguishing WMHD from WMH-ND, support vector machine (SVM) and logistic regression (LR) performed best (accuracy: 76.67%, AUC: 0.84). Classification performance was highest for WMH-ND vs. HC (LR: 79.17%, AUC 0.86), and lowest for WMHD vs. HC (SVM: 68.33%, AUC 0.77). Conclusion Patients with WMH exhibit alterations in EEG microstate features that are significantly correlated with WMH burden and cognitive function. Classification models based on these microstate features show promise for practical application in WMH-related dementia.
BACKGROUND: The Fazekas score is widely used to grade white matter hyperintensities (WMHs) in cerebral small vessel disease, yet the equivalent volume of each grade is unclear. We quantified the correspondence between Fazekas scores and WMH volume, normalized ratios, and derived conversion equations across populations. METHODS: We analyzed 1220 participants from 4 mostly United Kingdom–based cohorts representing different cerebral small vessel disease severities: community-dwelling (LBC1936 [Lothian Birth Cohort 1936]), stroke (MSS2 [Mild Stroke Study 2] and MSS3 [Mild Stroke Study 3]), and a cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy-enriched cohort (INVESTIGATE [Imaging Neuro-Vascular, Endothelial and Structural Integrity in Preparation to Treat Small Vessel Diseases]). WMH burden was quantified as absolute WMH volume and as normalized ratios: WMH volume as a percentage of brain volume and WMH volume as a percentage of intracranial volume. Transition zones were defined as the overlap of WMH volume interquartile ranges between adjacent Fazekas scores. Linear, exponential (nonlinear least squares), and log-linear (ln WMH≈Fazekas score and zeros excluded) models were fitted; model fit was assessed using mean squared error, (pseudo-)R2, and Vuong tests. Periventricular-versus-deep WMH patterns within each total Fazekas score were compared using the Kruskal-Wallis test. RESULTS: The pooled cohort had a mean age of 69.4±8.9 years; 700 of 1227 records (57.1%) were male. WMH burden increased monotonically with a Fazekas score in all cohorts. Adjacent-grade separation was strongest in LBC1936 (all P≤0.002) but weaker at lower grades in MSS2, MSS3, and INVESTIGATE. The widest transition zones occurred between Fazekas grades of 3 to 4 and 4 to 5, whereas a clear WMH volume gap separated grades 5 and 6. Exponential models generally fit better than linear models, with lower mean squared error and supportive Vuong tests in most cohorts. Log-linear sensitivity analyses showed similar fitted trajectories after back-transformation. WMH burden did not differ consistently by periventricular-versus-deep distribution within Fazekas grades. CONCLUSIONS: WMH burden followed an exponential trajectory across Fazekas scores, with population-dependent progression. The resulting conversion equations enabled bidirectional translation between visual Fazekas score and quantitative WMH volume, facilitating cross-study comparison, large-scale epidemiology, and might facilitate clinical decision-making in cerebral small vessel disease.
Yuyuan Xu, Francesca M. Chappell, M. V. Valdés Hernández et al.· Stroke· 0 citations
Diffusion MRI biomarkers were abnormal at least a decade before WMH abnormality in the population, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.
P. Vemuri, Ming-Zhao Hu, Emily S. Lundt et al.· Stroke· 0 citations
The association between b-WMHs and stroke remained significant after adjustment for age, sex, and supratentorial WMHs (s‑WMHs), but lost statistical significance when additional vascular risk factors were included.
H. Özdemir, E. Kumral, G. Baskan et al.· Turkish Journal of Cerebrova...· 0 citations
WMHs are common MRI findings in patients with migraine, particularly in older patients and those with chronic or long-standing disease, and may improve the interpretation of MRI findings and contribute to a better understanding of migraine-related brain changes.
Aqeela, K. Nawaz, Sania Saeed et al.· International journal of pat...· 0 citations
Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WMH), a multicontrast MRI machine learning framework that characterises white matter pathology through tissue microstructural information rather than lesion contrast alone. Methods: RADAR-WMH was trained on quantitative relaxometry and diffusion-weighted MRI acquired in community-dwelling participants (mean age 59.6 years [SD 22.4], 60.8% women, n=148) using a LightGBM classifier integrating local, textural, and anatomical features at the voxel level. Biological validity was assessed through longitudinal analyses and associations with age, cardio-vascular risk, and cognitive performance in independent cohorts. Results: RADAR-WMH achieved segmentation performance comparable to state-of-the-art FLAIR-based approaches without requiring FLAIR or T1-weighted data. Mean diffusivity was the most influential feature for lesion classification. Beyond FLAIR-defined WMH, RADAR-WMH identified tissue pathology extending outside lesion borders characterised by myelin loss, axonal injury, and increased extracellular water. These microstructural signatures persisted over follow-up and showed stronger association with age, systolic blood pressure, and cognitive performance than corresponding tissue properties restricted to FLAIR-defined WMH extent. Conclusions: RADAR-WMH reveals a significant burden of biologically meaningful white matter injury that remains invisible to FLAIR-defined WMH segmentation. By capturing microstructural pathology linked to vascular risk, cognitive decline, and lesion evolution, RADAR-WMH may provide more sensitive markers of cerebral small vessel disease than FLAIR-visible WMH alone.
V. Roduit, F. Carneiro, A. Lutti et al.· medRxiv· 0 citations
Background
Recent studies have explored the link between white matter hyperintensity (WMH) and gait disorders; however, inconsistent results have arisen due to varied populations and sample sizes. We aimed to examine the relationship between WMH severity and gait in patients with cerebral small vessel disease (CSVD).
Methods
We included 335 patients with CSVD with independent walking ability hospitalized at the Department of Neurology, Tianjin Huanhu Hospital, from August 1, 2018, to May 17, 2021. WMH was graded using the Fazekas scale on magnetic resonance imaging. Patients were categorized into the mild and moderate-to-severe WMH groups. A wearable gait analysis system was employed to evaluate the impact of WMH severity on patients' walking ability.
Results
Patients in the moderate-to-severe WMH group exhibited slower gait speed, shorter length stride, and reduced foot strike and toe-off angles compared to those in the mild WMH group, all with significant differences (p < 0.05). Analysis of gait parameter symmetry and variability showed significant increases in the moderate-to-severe WMH group. Multivariate logistic regression analysis identified toe-off angle, swing phase coefficient of variation, and age as independent risk factors for WMH-induced gait impairment.
Conclusion
The severity of white matter lesions affects the gait of patients with small vessel disease, including spatiotemporal and kinematic parameters, symmetry, and variability. With increasing age, the severity of white matter lesions increases, resulting in a reduced toe-off angle and greater swing variability, leading to decreased stability.
Yajing Wang, Zhizhong Zhu, Jiao-Jiao Zhao et al.· Frontiers in Neurology· 0 citations
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