Association of Data-Driven White Matter Hyperintensity Spatial Signatures With Distinct Cerebral Small Vessel Disease Etiologies.

Topographical distribution of white matter hyperintensities (WMH) are hypothesized to vary by cerebrovascular risk factors. We used an unbiased pattern discovery approach to identify distinct WMH spatial patterns and investigate their association with different WMH etiologies. We performed a cross-sectional study on participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify spatially distinct WMH distribution patterns using voxel-based spectral clustering analysis of aligned WMH probability maps. We included all participants from the ADNI Grand Opportunity/ADNI 2 study with available baseline 2D-FLAIR MRI scans, without history of stroke or presence of infarction on imaging. We evaluated the associations of these WMH spatial patterns with vascular risk factors, amyloid-β PET, and imaging biomarkers of cerebral amyloid angiopathy (CAA), characterizing different forms of cerebral small vessel disease (CSVD) using multivariable regression. We also used linear regression models to investigate whether WMH spatial distribution influenced cognitive impairment. We analyzed MRI scans of 1,046 ADNI participants with mixed vascular and amyloid-related risk factors (mean age 72.9, 47.7% female, 31.4% hypertensive, 48.3% with abnormal amyloid PET). We observed unbiased partitioning of WMH into 5 unique spatial patterns: deep frontal, periventricular, juxtacortical, parietal, and posterior. Juxtacortical WMH were independently associated with probable CAA, deep frontal WMH were associated with risk factors for arteriolosclerosis (hypertension and diabetes), and parietal WMH were associated with brain amyloid accumulation, consistent with an Alzheimer disease (AD) phenotype. Juxtacortical, deep frontal, and parietal WMH spatial patterns were associated with cognitive impairment. Periventricular and posterior WMH spatial patterns were unrelated to any disease phenotype or cognitive decline. Data-driven WMH spatial patterns reflect discrete underlying etiologies including arteriolosclerosis, CAA, AD, and normal aging. Global measures of WMH volume may miss important spatial distinctions. WMH spatial signatures may serve as etiology-specific imaging markers, helping to resolve WMH heterogeneity, identify the dominant underlying pathologic process, and improve prediction of clinical-relevant trajectories that influence cognitive decline.

Phuah CL ，Chen Y ，Strain JF ，Yechoor N ，Laurido-Soto OJ ，Ances BM ，Lee JM ，Alzheimer's Disease Neuroimaging Initiative ... -  《-》

Can white matter hyperintensities based Fazekas visual assessment scales inform about Alzheimer's disease pathology in the population?

Pradeep A ，Raghavan S ，Przybelski SA ，Preboske GM ，Schwarz CG ，Lowe VJ ，Knopman DS ，Petersen RC ，Jack CR Jr ，Graff-Radford J ，Cogswell PM ，Vemuri P ... -  《Alzheimers Research & Therapy》

Imaging features of small vessel disease in cerebral amyloid angiopathy among patients with Alzheimer's disease.

Cerebral small vessel disease biomarkers including white matter hyperintensities (WMH), lacunes, and enlarged perivascular spaces (ePVS) are under investigation to identify those specific to cerebral amyloid angiopathy (CAA). In subjects with Alzheimer's disease (AD), we assessed characteristic features and amounts of WMH, lacunes, and ePVS in four CAA categories (no, mild, moderate and severe CAA) and correlated these with Clinical Dementia Rating sum of boxes (CDRsb) score, ApoE genotype, and neuropathological changes at autopsy. The study included patients with a clinical diagnosis of dementia due to AD and neuropathological confirmation of AD and CAA in the National Alzheimer's Coordinating Center (NACC) database. The WMH, lacunes, and ePVS were evaluated using semi-quantitative scales. Statistical analyses compared the WMH, lacunes, and ePVS values in the four CAA groups with vascular risk factors and AD severity treated as covariates, and to correlate the imaging features with CDRsb score, ApoE genotype, and neuropathological findings. The study consisted of 232 patients, of which 222 patients had FLAIR data available and 105 patients had T2-MRI. Occipital predominant WMH were significantly associated with the presence of CAA (p = 0.007). Among the CAA groups, occipital predominant WMH was associated with severe CAA (β = 1.22, p = 0.0001) compared with no CAA. Occipital predominant WMH were not associated with the CDRsb score performed at baseline (p = 0.68) or at follow-up 2-4 years after the MRI (p = 0.92). There was no significant difference in high grade ePVS in the basal ganglia (p = 0.63) and centrum semiovale (p = 0.95) among the four CAA groups. The WMH and ePVS on imaging did not correlate with the number of ApoE ε4 alleles but the WMH (periventricular and deep) correlated with the presence of infarcts, lacunes and microinfarcts on neuropathology. Among patients with AD, occipital predominant WMH is more likely to be found in patients with severe CAA than in those without CAA. The high-grade ePVS in centrum semiovale were common in all AD patients regardless of CAA severity.

Nagaraja N ，DeKosky S ，Duara R ，Kong L ，Wang WE ，Vaillancourt D ，Albayram M ... -  《-》

White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition.

White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer's disease (AD) risk factors, and cognition. In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant's white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present.

Bachmann D ，von Rickenbach B ，Buchmann A ，Hüllner M ，Zuber I ，Studer S ，Saake A ，Rauen K ，Gruber E ，Nitsch RM ，Hock C ，Treyer V ，Gietl A ... -  《Alzheimers Research & Therapy》

Hypertensive Arteriopathy and Cerebral Amyloid Angiopathy in Patients with Cognitive Decline and Mixed Cerebral Microbleeds.

Ii Y ，Ishikawa H ，Matsuyama H ，Shindo A ，Matsuura K ，Yoshimaru K ，Satoh M ，Taniguchi A ，Matsuda K ，Umino M ，Maeda M ，Tomimoto H ... -  《-》