Linking cortical atrophy to white matter hyperintensities of presumed vascular origin

Mayer, Christine M.; Frey, Benedikt M.; Schlemm, Eckhard; Petersen, Marvin; Engelke, Kristin; Hanning, Uta; Jagodzinski, Annika; Borof, Katrin; Fiehler, Jens; Gerloff, Christian; Thomalla, Götz; Cheng, Bastian

doi:10.1177/0271678x20974170

Cited by 22 publications

(16 citation statements)

References 45 publications

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“…When classified according to the deep subcortical (D-WMH) vs periventricular region (PV-WMH) distribution proposed by Fazekas and colleagues (1987) 15 , several studies revealed that both burden and progression of PV-WMH were more strongly related to cognitive decline and incident MCI than were D-WMH burden and progression 11 , 16 , 17 . PV-WMH have also been associated with reduced cortical thickness and volume 18 , 19 . Additionally, there is also evidence that lobar anatomy might influence the effect of WMH on cognitive impairment: a previous study by Moura et al (2019) that assessed the relation between cognition and WMH according to the lobar region found that parietal WMH mediated the relation between age and performance on the speed of processing tasks 20 .…”

Section: Introductionmentioning

confidence: 99%

Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment

Jiménez-Baladó

Corlier

Habeck

et al. 2022

Sci Rep

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White matter hyperintensities (WMH) are a key hallmark of subclinical cerebrovascular disease and are known to impair cognition. Here, we parcellated WMH using a novel system that segments WMH based on both lobar regions and distance from the ventricles, dividing the brain into a coordinate system composed of 36 distinct parcels (‘bullseye’ parcellation), and then investigated the effect of distribution on cognition using two different analytic approaches. Data from a well characterized sample of healthy older adults (58 to 84 years) who were free of dementia were included. Cognition was evaluated using 12 computerized tasks, factored onto 4 indices representing episodic memory, speed of processing, fluid reasoning and vocabulary. We first assessed the distribution of WMH according to the bullseye parcellation and tested the relationship between WMH parcellations and performance across the four cognitive domains. Then, we used a data-driven approach to derive latent variables within the WMH distribution, and tested the relation between these latent components and cognitive function. We observed that different, well-defined cognitive constructs mapped to specific WMH distributions. Speed of processing was correlated with WMH in the frontal lobe, while in the case of episodic memory, the relationship was more ubiquitous, involving most of the parcellations. A principal components analysis revealed that the 36 bullseye regions factored onto 3 latent components representing the natural aggrupation of WMH: fronto-parietal periventricular (WMH principally in the frontal and parietal lobes and basal ganglia, especially in the periventricular region); occipital; and temporal and juxtacortical WMH (involving WMH in the temporal lobe, and at the juxtacortical region from frontal and parietal lobes). We found that fronto-parietal periventricular and temporal & juxtacortical WMH were independently associated with speed of processing and episodic memory, respectively. These results indicate that different cognitive impairment phenotypes might present with specific WMH distributions. Additionally, our study encourages future research to consider WMH classifications using parcellations systems other than periventricular and deep localizations.

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Section: Introductionmentioning

confidence: 99%

Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment

Jiménez-Baladó

Corlier

Habeck

et al. 2022

Sci Rep

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“…21,23,41 Additionally, further studies may evaluate the impact of ADPKD on the glymphatic system function and other SVD pathomechanisms, using more advanced investigation modalities such as glymphatic MRI, dual-echo arterial spin labeling MRI, diffusionweighted image-based brain connectivity maps, and cortical thickness measurement. [42][43][44][45][46] In conclusion, ADPKD is more strongly associated with ePVS than other SVD markers, and its severity also correlated with the severity of SVD. Multiple pathomechanisms of ADPKD-associated cilia dysfunction may induce chronic dysfunction of the cerebral glymphatic system, resulting in the ePVS augmented pattern of SVD.…”

Section: Discussionmentioning

confidence: 79%

Association of autosomal dominant polycystic kidney disease with cerebral small vessel disease

Lee

Jung

Ryu

et al. 2021

J Cereb Blood Flow Metab

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Cilia dysfunction in autosomal-dominant polycystic kidney disease (ADPKD) may impair the integrity of glymphatic system and be implicated in the progression of cerebral small vessel disease (SVD), although the link between the two diseases has not been investigated. We evaluated the association of ADPKD pathology with SVD pattern and severity. Overall, 304 individuals in an ADPKD (chronic kidney disease stage ≤4 and age ≥50 years) cohort and their age, sex, and estimated glomerular filtration rate (eGFR)-matched controls were retrospectively included. ADPKD severity was classified into 1 A-B, 1 C, and 1 D-E, according to age and height-adjusted total kidney volume. SVD parameters included white-matter hyperintensity (WMH) severity scale, enlarged perivascular space (ePVS) score, and degree of lacunes or cerebral microbleeds (CMBs). After adjustments for age, sex, eGFR, and cerebrovascular risk factor parameters, ADPKD was associated with higher ePVS scores ( P < 0.001), but not with the WMH severity or degree of lacunes or CMBs. In the ADPKD subgroup, higher ADPKD severity class was associated with higher ePVS scores ( P < 0.001), WMH severity ( P = 0.003), and degree of lacunes ( P = 0.002). ADPKD associated cilia dysfunction may induce chronic cerebral glymphatic system dysfunction, which may contribute to the specific progression of ePVS compared with other SVD markers.

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“…One of the motivations that drove our study is that brain atrophy has become an important risk factor for brain health with CSVD, and the effects of CSVD can be mediated through the development of brain atrophy ( 33 ). The increased white matter hypersignal burden undermines the integrity of the white matter and causes the loss of volume.…”

Section: Discussionmentioning

confidence: 99%

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

et al. 2022

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ObjectiveBrain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.MethodsA total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared.ResultsAutomated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.ConclusionWe provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.

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Linking cortical atrophy to white matter hyperintensities of presumed vascular origin

Cited by 22 publications

References 45 publications

Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment

Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment

Association of autosomal dominant polycystic kidney disease with cerebral small vessel disease

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

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