White matter hyperintensity reduction and outcomes after minor stroke

Wardlaw, Joanna M.; Chappell, Francesca M; Hernández, María del C. Valdés; Makin, Stephen; Staals, Julie; Shuler, Kirsten; Thrippleton, Michael J.; Armitage, Paul A.; Muñoz‐Maniega, Susana; Heye, Anna K.; Sakka, Eleni; Dennis, Martin

doi:10.1212/wnl.0000000000004328

Cited by 120 publications

(156 citation statements)

References 26 publications

(43 reference statements)

Supporting

Mentioning

147

Contrasting

Order By: Relevance

“…These factors are causally-linked and are known to associate with poor neurologic outcomes including balance difficulties in older individuals at risk for neurodegeneration [17, 18]. Clinical trial evidence also suggests both may be modifiable using targeted approaches with antihypertensive medications [19, 20]. …”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Risk Factor Burden in Veterans and Non-Veterans with Parkinson Disease

Kotagal

Albin

Müller

et al. 2018

JPD

View full text Add to dashboard Cite

Background Medical comorbidities, including cardiovascular risk factors such as hypertension and diabetes, influence disease progression in Parkinson disease (PD) and may be variably present in different clinical populations. Objective/Methods We conducted a retrospective nested case-control study of 29 Veterans with PD and 29 non-Veteran PD controls. The groups were matched for age, gender, and disease duration. Both groups underwent clinical and imaging testing as part of their participation in a larger cross-sectional PD observational study at our research center. Veterans were recruited primarily from movement disorders neurology clinics at the Ann Arbor Veterans Affairs (VA) Health System. Non-Veterans were recruited primarily from analogous clinics at the University of Michigan Health System. We explored differences in cardiovascular risks factor burden between the groups. Results Veterans with PD showed higher scores on the simplified Framingham 10-year general cardiovascular disease risk calculator (FR score; 27.3% (11.5) vs. 20.7% (6.8); t = −2.66, p = 0.011) and fewer years of self-reported education (14.5 (2.5) vs. 16.7 (2.6); t = 3.33, p = 0.002). After adjusting for age, disease duration, education, and the use of antihypertensive medications, Veterans showed higher FR scores (t = 2.95, p = 0.005) and a higher intra-subject ratio of FR score to age-and-gender normalized FR score (t = 2.49, p = 0.016), representing an elevated component of modifiable cardiovascular risk factor burden. Conclusion Cardiovascular comorbidities are common in Veterans with PD and may be more severe than in non-Veteran PD populations. These findings merit replication in other representative cohorts. Veterans may be a preferred population for clinical trials evaluating cardiovascular risk factor management on PD progression.

show abstract

Section: Discussionmentioning

confidence: 99%

Cardiovascular Risk Factor Burden in Veterans and Non-Veterans with Parkinson Disease

Kotagal

Albin

Müller

et al. 2018

JPD

View full text Add to dashboard Cite

show abstract

“…The image processing protocol followed to generate these masks is fully explained in (Valdés Hernández et al, 2015). Extracranial tissues, SL, and skull were removed from the baseline and follow-up T2-FLAIR images using the SL and ICV binary masks from previous analyses Wardlaw et al, 2017). Furthermore, binary WMH labels produced for the primary study that provided the data (Valdés Hernández et al, 2015) were used as the gold standard (i.e.…”

Section: Subjects and Datamentioning

confidence: 99%

“…Clinically, WMH have been commonly associated with stroke, ageing, and dementia progression (Wardlaw et al, 2013;Prins and Scheltens, 2015). Furthermore, recent studies have shown that WMH may decrease (i.e., shrink/regress), stay unchanged (i.e., stable), or increase (i.e., grow/progress) over a period of time (Ramirez et al, 2016;Chappell et al, 2017;Wardlaw et al, 2017). In this study, we refer to theses changes as "evolution of WMH".…”

Section: Introductionmentioning

confidence: 98%

“…Predicting the evolution of WMH is challenging because the rate and direction of WMH evolution varies considerably across studies (Schmidt et al, 2016;van Leijsen et al, 2017a,b) and several risk factors, either commonly or not fully known, could be involved in their progression . For example, some risk factors and predictors that have been commonly associated with WMH progression are baseline WMH volume (Schmidt et al, 2003;Sachdev et al, 2007;van Dijk et al, 2008;Wardlaw et al, 2017;Chappell et al, 2017), blood pressure or hypertension (Veldink et al, 1998;Schmidt et al, 2002b;van Dijk et al, 2008;Godin et al, 2011;Verhaaren et al, 2013), age (van Dijk et al, 2008), current smoking status (Power C et al, 2015), previous stroke and diabetes (Gouw et al, 2008;Wardlaw et al, 2017), and genetic properties (Schmidt et al, 2002a(Schmidt et al, , 2011Godin et al, 2009;Luo et al, 2017). Surrounding regions of WMH that may appear like normal appearing white matter (NAWM) with less structural integrity, usually called the "penumbra of WMH" , have also been reported as having a high risk of becoming WMH over time (Maillard et al, 2014;Pasi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Surrounding regions of WMH that may appear like normal appearing white matter (NAWM) with less structural integrity, usually called the "penumbra of WMH" , have also been reported as having a high risk of becoming WMH over time (Maillard et al, 2014;Pasi et al, 2016). On the other hand, regression of WMH volume has been reported in several radiological observations on MRI, such as after cerebral infraction (Moriya et al, 2009), strokes (Durand-Birchenall et al, 2012;Cho et al, 2015;Wardlaw et al, 2017), improved hepatic encephalopathy , lower blood pressure , liver transplantation (Rovira Cañellas et al, 2007), and carotid artery stenting (Yamada et al, 2010). While a recent study suggested that areas of shrinking WMH were actually still damaged (Jiaerken et al, 2018), a more recent study showed that WMH regression did not accompany brain atrophy and suggested that WMH regression follows a relatively benign clinical course (van Leijsen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Rachmadi

Valdés-Hernández²,

Makin³

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractPrevious studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely “Disease Evolution Predictor” (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called “Disease Evolution Map” (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model’s architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.

show abstract

Advances in Understanding the Pathophysiology of Lacunar Stroke

et al. 2018

View full text Add to dashboard Cite

troke is the second leading cause of death in the world and the leading cause of disability in the United States. 1 Among ischemic strokes, there are different subtypes, including large artery atherosclerosis, cardioembolism, and cerebral small vessel disease (CSVD). Although CSVD has several clinical and radiographic manifestations, lacunar stroke (LS) is prototypical and accounts for 20% to 30% of ischemic strokes. 2,3 Clinically, LS can manifest with several syndromes depending on lesion location. 4 Silent LSs are found in 20% to 50% of healthy elderly people. 5 Lacunar strokes are particularly burdensome, with a 20% recurrence rate, 25% fiveyear mortality, and associated morbidities, such as vascular cognitive impairment. 3 Lacunar strokes, appropriately named given their propensity to form cavities (lacunes), were first described in 1838 (Figure 1). 3 More recently, the Standards for Reporting Vascular Changes on Neuroimaging definitions were developed to standardize terms that describe the appearance of sequelae of CSVD, including recent small subcortical infarcts, lacunes, white matter hyperintensities (WMHs), perivascular spaces, microbleeds, and brain atrophy, on imaging. 6 Lacunar strokes primarily encompass 2 terms: recent small subcortical infarcts, defined as recent infarctions in the territory of 1 perforating arteriole with imaging features or clinical symptoms consistent with occurrence in the previous few weeks, and lacunes, defined as round or ovoid, subcortical, fluid-filled cavities 3 to 15 mm in diameter, consistent with previous acute small subcortical infarcts or hemorrhages in the territory of 1 perforating arteriole. 6 Although this review focuses on LS, other abnormalities that result from CSVD are relevant because there are interrelated mechanisms and treatment implications. For example, recent small subcortical infarcts can also contribute to WMH, 3 and many intracerebral hemorrhages (ICHs) likely result from similar vessel abnormalities. 7 This suggests that patients with LS may have an increased risk for bleeding when taking antithrombotic medications. IMPORTANCE Stroke is the second leading cause of death in the world, and nearly one-third of ischemic strokes are lacunar strokes (LSs) or small subcortical infarcts. Although smaller in size, they create large problems, leaving many patients with intellectual and physical disabilities. Because there are limitations in understanding the underlying pathophysiology of LS, the development of novel therapies has been slow.OBSERVATIONS When the term lacune was described in the 1800s, its underlying pathophysiological basis was obscure. In the 1960s, C. Miller Fisher, MD, performed autopsy studies that showed that vessels supplying lacunes displayed segmental arteriolar disorganization, characterized by vessel enlargement, hemorrhage, and fibrinoid deposition. For these pathologic changes, he coined the term lipohyalinosis. Since that time, few attempts have been made to reconcile this pathologic description with modern mechanisms of cerebr...

show abstract

White matter hyperintensity reduction and outcomes after minor stroke

Cited by 120 publications

References 26 publications

Cardiovascular Risk Factor Burden in Veterans and Non-Veterans with Parkinson Disease

Cardiovascular Risk Factor Burden in Veterans and Non-Veterans with Parkinson Disease

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Advances in Understanding the Pathophysiology of Lacunar Stroke

Contact Info

Product

Resources

About