Population-Based Case–Control Study of White Matter Changes on Brain Imaging in Transient Ischemic Attack and Ischemic Stroke

Li, Linxin; Simoni, Michela; Küker, Wilhelm; Schulz, Ursula G.; Christie, Sharon; Wilcock, Gordon; Rothwell, Peter M.

doi:10.1161/strokeaha.113.002775

Cited by 35 publications

(26 citation statements)

References 44 publications

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“…Cerebral WMI is detected in more than half of normal elderly individuals (de Leeuw et al, 2001) and in 64-86% of stroke patients (Fu et al, 2005; Li et al, 2013). Furthermore, WMI progression is present in 8-28% of non-demented elderly patients (Enzinger et al, 2007) and 32% of ischemic stroke patients (Cho et al, 2015), implying that WMI has a dynamic course through late life and after injury.…”

Section: The Relationship Between Wmi and Ischemic Strokementioning

confidence: 99%

White matter injury in ischemic stroke

Wang

Liu

Hong

et al. 2016

Progress in Neurobiology

219

206

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Stroke is one of the major causes of disability and mortality worldwide. It is well known that ischemic stroke can cause gray matter injury. However, stroke also elicits profound white matter injury, a risk factor for higher stroke incidence and poor neurological outcomes. The majority of damage caused by stroke is located in subcortical regions and, remarkably, white matter occupies nearly half of the average infarct volume. Indeed, white matter is exquisitely vulnerable to ischemia and is often injured more severely than gray matter. Clinical symptoms related to white matter injury include cognitive dysfunction, emotional disorders, sensorimotor impairments, as well as urinary incontinence and pain, all of which are closely associated with destruction and remodeling of white matter connectivity. White matter injury can be noninvasively detected by MRI, which provides a three-dimensional assessment of its morphology, metabolism, and function. There is an urgent need for novel white matter therapies, as currently available strategies are limited to preclinical animal studies. Optimal protection against ischemic stroke will need to encompass the fortification of both gray and white matter. In this review, we discuss white matter injury after ischemic stroke, focusing on clinical features and tools, such as imaging, manifestation, and potential treatments. We also briefly discuss the pathophysiology of WMI and future research directions.

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Section: The Relationship Between Wmi and Ischemic Strokementioning

confidence: 99%

White matter injury in ischemic stroke

Wang

Liu

Hong

et al. 2016

Progress in Neurobiology

219

206

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“…White matter hyperintensities (WMHs) of presumed vascular origin appear as bright localised areas in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images (Wardlaw et al, 2013), and could appear hypointense on T1-weighted images. WMHs occur commonly in patients with cerebrovascular diseases (Li et al, 2013;Simoni et al, 2012) and have been associated with cognitive decline, atrophy and neurodegenerative diseases such as dementia (Debette et al, 2010;Prins and Scheltens, 2015;Pantoni et al, 2005). However, they are also found commonly in healthy elderly subjects (Zamboni et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

Sundaresan

Zamboni

Rothwell³

et al. 2020

Preprint

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White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. Also, the network uses anatomical information regarding WMH spatial distribution in loss functions for improving the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning method of MWSC 2017.

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“…The distribution and degree of WMLs are assessed on CT, and scored using the Blennow scale, where the final score is a mean value (11,12). Periventricular and deep WMLs are seen as hyperintensities on T2-weighted MR images and are scored separately using the Fazekas scale, where the total score (13,14) reflects the sum of both types of WM injury.…”

Section: Discussionmentioning

confidence: 99%

Association between the extent of white matter damage and early cognitive impairment following acute ischemic stroke

Yong

Mao

2017

Experimental and Therapeutic Medicine

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White matter (WM) injury following acute ischemic stroke (AIS) is associated with cognitive decline. Establishing relationships between the specific cognitive tests used to assess post-AIS cognition and various clinical indices of WM injury severity and distribution may aid in prognosis and early treatment decisions. We enrolled 62 patients with AIS to Weifang People's Hospital between September 2014 and August 2015. WM lesion severity and distribution were examined by computed tomography (CT) and magnetic resonance imaging (MRI). The Blennow scale was used for scoring the distribution and degree of WM lesions (WMLs) on CT images, the Fazekas scale for scoring periventricular and deep WMLs on MRI, and the Cholinergic Pathways Hyperintensities Scale (CHIPS) for scoring MRI manifestation of cholinergic fiber damage. The 8-domain Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive function. Mean ± standard deviation scores on the Blennow scale was 1.6±0.5; Fazekas scale, 3.4±0.8; and CHIPS, 65.7±12.5. The proportion of patients with a MoCA score <26 (indicating cognitive dysfunction) was significantly higher in subgroups with Blennow scale score >2, Fazekas scale score >4, and CHIPS score >51 (all P<0.001). The MoCA score was negatively correlated with Blennow scale score (r=−0.326, P=0.002), Fazekas scale score (r=−0.404, P=0.031), and CHIPS score (r=−0.234, P=0.042). Thus, the degree and distribution of whole-brain, deep, and cholinergic WMLs were associated with cognitive impairment. The Blennow scale, Fazekas scale, and CHIPS all provide good predictive efficacy of post-AIS cognitive dysfunction.

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Population-Based Case–Control Study of White Matter Changes on Brain Imaging in Transient Ischemic Attack and Ischemic Stroke

Cited by 35 publications

References 44 publications

White matter injury in ischemic stroke

White matter injury in ischemic stroke

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

Association between the extent of white matter damage and early cognitive impairment following acute ischemic stroke

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