Angela Jochems , Susana Muñoz Maniega , Una Clancy , Daniela Jaime Garcia , Carmen Arteaga , Mariadel C. Valdés Hernández , Will Hewins , Rachel Penman , Ellen Backhouse , Stewart Wiseman , Michael Thrippleton , Michael Stringer , Francesca Chappell , Fergus Doubal , Joanna Wardlaw
{"title":"Impact of white matter hyperintensity regression on global cognition, processing speed and executive function","authors":"Angela Jochems , Susana Muñoz Maniega , Una Clancy , Daniela Jaime Garcia , Carmen Arteaga , Mariadel C. Valdés Hernández , Will Hewins , Rachel Penman , Ellen Backhouse , Stewart Wiseman , Michael Thrippleton , Michael Stringer , Francesca Chappell , Fergus Doubal , Joanna Wardlaw","doi":"10.1016/j.cccb.2024.100243","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>White matter hyperintensities (WMH) are related to cognitive decline, particularly processing speed and executive functioning (EF). However, all cognitive domains might be affected. WMH can regress and this might have positive consequences for cognition, but the effects of WMH regression on cognition are unknown. This study aims to assess whether changes in global cognition, processing speed and EF are related to progressing and regressing WMH.</p></div><div><h3>Methods</h3><p>We recruited mild, non-disabling, ischemic stroke patients with sporadic small vessel disease. They underwent MRI and cognitive assessment within 3 months post-stroke and 1 year later. Cognitive assessment included MoCA (global cognition; score 0-30), trail making test (TMT) A (processing speed; seconds) and TMT B (EF; seconds). WMH volumes are reported as % intracranial volume (ICV). WMH volume change (%ICV) is defined as WMH volume difference between baseline and 1 year. We calculated quintiles (Q) of WMH volume change to group volume change. We performed three linear mixed models, with MoCA, TMT-A and B as outcomes, to assess relationships over time with quintile of WMH volume change. Predictors in addition to quintile are age, sex, premorbid intelligence (NART), stroke severity (NIHSS). Extra predictor for TMT-B analysis is the TMT-A time to correct for speed.</p></div><div><h3>Results</h3><p>202 participants had WMH change volumes available (mean age: 66.03 years [SD=11.15), 33% female]. Q1 reflect greatest WMH volume reduction and Q5 greatest volume increase, while Q3 is lowest overall volume change (Figure 1). MoCA score increased over time (Figure 2). Older age (std. B [std. 95%CI]: -0.303 [-0.400, -0.206]), worse NART (-0.463 [-0.557, -0.368]), increasing NIHSS (-0.208 [-0.286, -0.130]) and more WMH increase (Q5; -0.513 [-0.814, -0.211]) predict lower MoCA. Older age (0.257 [0.135, 0.380]), worse NART (0.259 [0.139, 0.378]) and higher NIHSS (0.152 [0.078, 0.226]) predict worse TMT-A time (Figure 3A). Older age (0.224 [0.132, 0.316]), worse NART (0.295 [0.207, 0.383]), worse TMT-A (0.469 [0.384, 0.555]), worsening WMH volume (Q2; 0.382 [0.115, 0.0.649] and Q5; 0.425 [0.150, 0.699]) predict worse TMT-B time (Figure 3B).</p></div><div><h3>Discussion</h3><p>More WMH progression independently predicts worse global cognition and EF but not processing speed. WMH regression (Q1) was not related to worsening or improving global cognition, processing speed and EF. Cognitive consequences of WMH regression might be comparable to stable WMH (Q3).</p></div>","PeriodicalId":72549,"journal":{"name":"Cerebral circulation - cognition and behavior","volume":"6 ","pages":"Article 100243"},"PeriodicalIF":1.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666245024000448/pdfft?md5=1278f4870900323bf0a34ac521701724&pid=1-s2.0-S2666245024000448-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cerebral circulation - cognition and behavior","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666245024000448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction
White matter hyperintensities (WMH) are related to cognitive decline, particularly processing speed and executive functioning (EF). However, all cognitive domains might be affected. WMH can regress and this might have positive consequences for cognition, but the effects of WMH regression on cognition are unknown. This study aims to assess whether changes in global cognition, processing speed and EF are related to progressing and regressing WMH.
Methods
We recruited mild, non-disabling, ischemic stroke patients with sporadic small vessel disease. They underwent MRI and cognitive assessment within 3 months post-stroke and 1 year later. Cognitive assessment included MoCA (global cognition; score 0-30), trail making test (TMT) A (processing speed; seconds) and TMT B (EF; seconds). WMH volumes are reported as % intracranial volume (ICV). WMH volume change (%ICV) is defined as WMH volume difference between baseline and 1 year. We calculated quintiles (Q) of WMH volume change to group volume change. We performed three linear mixed models, with MoCA, TMT-A and B as outcomes, to assess relationships over time with quintile of WMH volume change. Predictors in addition to quintile are age, sex, premorbid intelligence (NART), stroke severity (NIHSS). Extra predictor for TMT-B analysis is the TMT-A time to correct for speed.
Results
202 participants had WMH change volumes available (mean age: 66.03 years [SD=11.15), 33% female]. Q1 reflect greatest WMH volume reduction and Q5 greatest volume increase, while Q3 is lowest overall volume change (Figure 1). MoCA score increased over time (Figure 2). Older age (std. B [std. 95%CI]: -0.303 [-0.400, -0.206]), worse NART (-0.463 [-0.557, -0.368]), increasing NIHSS (-0.208 [-0.286, -0.130]) and more WMH increase (Q5; -0.513 [-0.814, -0.211]) predict lower MoCA. Older age (0.257 [0.135, 0.380]), worse NART (0.259 [0.139, 0.378]) and higher NIHSS (0.152 [0.078, 0.226]) predict worse TMT-A time (Figure 3A). Older age (0.224 [0.132, 0.316]), worse NART (0.295 [0.207, 0.383]), worse TMT-A (0.469 [0.384, 0.555]), worsening WMH volume (Q2; 0.382 [0.115, 0.0.649] and Q5; 0.425 [0.150, 0.699]) predict worse TMT-B time (Figure 3B).
Discussion
More WMH progression independently predicts worse global cognition and EF but not processing speed. WMH regression (Q1) was not related to worsening or improving global cognition, processing speed and EF. Cognitive consequences of WMH regression might be comparable to stable WMH (Q3).