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White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.

ABSTRACT: White matter hyperintensities (WMHs) on T₂-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T₁-weighted images (T₁w) may also indicate the most severe component of WMHs. We developed an automatic method that sub-classifies WMHs into four categories (periventricular/deep and T₁w-hypointense/nonT₁w-hypointense) using MRI data from 684 community-dwelling older adults from the Whitehall II study. To test if location and intensity information can impact cognition, we derived two general linear models using either overall or subdivided volumes. Results showed that periventricular T₁w-hypointense WMHs were significantly associated with poorer performance in the trail making A (p = 0.011), digit symbol (p = 0.028) and digit coding (p = 0.009) tests. We found no association between total WMH volume and cognition. These findings suggest that sub-classifying WMHs according to both location and intensity in T₁w reveals specific associations with cognitive performance.

SUBMITTER: Melazzini L

PROVIDER: S-EPMC7995650 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.

Melazzini Luca L Mackay Clare E CE Bordin Valentina V Suri Sana S Zsoldos Enikő E Filippini Nicola N Mahmood Abda A Sundaresan Vaanathi V Codari Marina M Duff Eugene E Singh-Manoux Archana A Kivimäki Mika M Ebmeier Klaus P KP Jenkinson Mark M Sardanelli Francesco F Griffanti Ludovica L

NeuroImage. Clinical 20210307

White matter hyperintensities (WMHs) on T2-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T1-weighted images (T1w) may also indicate the most severe component of W ...[more]

PMID: 33743476

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Project description:BackgroundWhite matter hyperintensities are an important marker of cerebral small vessel disease. This disease burden is commonly described as hyperintense areas in the cerebral white matter, as seen on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging data. Studies have demonstrated associations with various cognitive impairments, neurological diseases, and neuropathologies, as well as clinical and risk factors, such as age, sex, and hypertension. Due to their heterogeneous appearance in location and size, studies have started to investigate spatial distributions and patterns, beyond summarizing this cerebrovascular disease burden in a single metric-its volume. Here, we review the evidence of association of white matter hyperintensity spatial patterns with its risk factors and clinical diagnoses.Design/methodsWe performed a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement. We used the standards for reporting vascular changes on neuroimaging criteria to construct a search string for literature search on PubMed. Studies written in English from the earliest records available until January 31st, 2023, were eligible for inclusion if they reported on spatial patterns of white matter hyperintensities of presumed vascular origin.ResultsA total of 380 studies were identified by the initial literature search, of which 41 studies satisfied the inclusion criteria. These studies included cohorts based on mild cognitive impairment (15/41), Alzheimer's disease (14/41), Dementia (5/41), Parkinson's disease (3/41), and subjective cognitive decline (2/41). Additionally, 6 of 41 studies investigated cognitively normal, older cohorts, two of which were population-based, or other clinical findings such as acute ischemic stroke or reduced cardiac output. Cohorts ranged from 32 to 882 patients/participants [median cohort size 191.5 and 51.6% female (range: 17.9-81.3%)]. The studies included in this review have identified spatial heterogeneity of WMHs with various impairments, diseases, and pathologies as well as with sex and (cerebro)vascular risk factors.ConclusionThe results show that studying white matter hyperintensities on a more granular level might give a deeper understanding of the underlying neuropathology and their effects. This motivates further studies examining the spatial patterns of white matter hyperintensities.

Project description:White matter hyperintensities (WMHs) have a heterogeneous aetiology, associated with both vascular risk factors and amyloidosis due to Alzheimer's disease. While spatial distribution of both amyloid and WM lesions carry important information for the underlying pathogenic mechanisms, the regional relationship between these two pathologies and their joint contribution to early cognitive deterioration remains largely unexplored. We included 662 non-demented participants from three Amyloid Imaging to Prevent Alzheimer's disease (AMYPAD)-affiliated cohorts: EPAD-LCS (N = 176), ALFA+ (N = 310), and EMIF-AD PreclinAD Twin60++ (N = 176). Using PET imaging, cortical amyloid burden was assessed regionally within early accumulating regions (medial orbitofrontal, precuneus, and cuneus) and globally, using the Centiloid method. Regional WMH volume was computed using Bayesian Model Selection. Global associations between WMH, amyloid, and cardiovascular risk scores (Framingham and CAIDE) were assessed using linear models. Partial least square (PLS) regression was used to identify regional associations. Models were adjusted for age, sex, and APOE-e4 status. Individual PLS scores were then related to cognitive performance in 4 domains (attention, memory, executive functioning, and language). While no significant global association was found, the PLS model yielded two components of interest. In the first PLS component, a fronto-parietal WMH pattern was associated with medial orbitofrontal-precuneal amyloid, vascular risk, and age. Component 2 showed a posterior WMH pattern associated with precuneus-cuneus amyloid, less related to age or vascular risk. Component 1 was associated with lower performance in all cognitive domains, while component 2 only with worse memory. In a large pre-dementia population, we observed two distinct patterns of regional associations between WMH and amyloid burden, and demonstrated their joint influence on cognitive processes. These two components could reflect the existence of vascular-dependent and -independent manifestations of WMH-amyloid regional association that might be related to distinct primary pathophysiology.

Project description:Background and Purpose: White matter hyperintensities (WMH) are commonly seen on structural MRI of older adults and are a manifestation of underlying and adjacent tissue damage. WMH may contribute to cortical disconnection and cognitive dysfunction, but it is unclear how WMH affect intersecting or nearby white matter tract integrity. This study investigated the effects of WMH on tract microstructure by determining the spatial distribution of water diffusion characteristics in white matter tract areas adjacent to both intersecting and nearby WMH. Methods: We used diffusion and structural MRI data from 52 representative participants from the Lothian Birth Cohort 1936 (72.2 ± 0.7 years) including a range of WMH burden. We segmented WMH, reconstructed 18 main white mater tracts using automated quantitative tractography and identified intersections between tracts and WMH. We measured mean diffusivity (MD) and fractional anisotropy (FA) in tract tissue at 2 mm incremental distances from tract-intersecting and non-intersecting (nearby) WMH. Results: We observed a spatial gradient of FA and MD abnormalities for most white matter tracts which diminished with a similar distance pattern for tract-intersecting and nearby WMH. Overall, FA was higher, while MD was lower around nearby WMH compared with tract-intersecting WMH. However, for some tracts, FA was lower in areas immediately surrounding nearby WMH, although with faster normalization than in FA values surrounding tract-intersecting WMH. Conclusion: WMH have similar effects on tract infrastructure, whether they be intersecting or nearby. However, the observed differences in tract water diffusion properties around WMH suggest that degenerative processes in small vessel disease may propagate further along the tract for intersecting WMH, while in some areas of the brain there is a larger and more localized accumulation of axonal damage in tract tissue nearby a non-connected WMH. Longitudinal studies should address differential effects of intersecting vs. nearby WMH progression and how they contribute to cognitive aging.

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White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.

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White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.

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