Literature DB >> 21971477

Complexity of MRI white matter hyperintensity assessments in relation to cognition in aging and dementia from the Sunnybrook Dementia Study.

Fu-qiang Gao1, Richard H Swartz, Philip Scheltens, Farrell S Leibovitch, Alex Kiss, Kie Honjo, Sandra E Black.   

Abstract

PURPOSE: Quantification methods for white matter hyperintensities (WMH) on Magnetic Resonance Imaging are heterogeneous, deterring their application. This study compared three WMH rating scales, varying in complexity, and a volumetric method, to evaluate trade-offs between complexity and clinical utility in differentiating dementia subgroups and in correlating with cognition.
METHODS: WMH were rated using the Fazekas, Age-Related White Matter Changes (ARWMC) and Scheltens scales, and segmented by computational volumetry in 108 patients with Alzheimer's Disease (AD), 23 with Mild Cognitive Impairment (MCI) and 34 normal controls (NC). Global and hippocampal atrophy, age and education, were accounted for in correlations of WMH with cognitive domains.
RESULTS: Intra- and inter-rater reliability were high (intraclass correlation coefficients = 0.88-0.97) across rating scales. WMH scores of all scales were highly correlated with volumes (Spearman r = 0.78-0.90, Ps < 0.001), as well as with each other (Spearman r = 0.86-0.91, Ps < 0.001). The Fazekas scale showed significant separation between AD, MCI and NC using non-parametric analysis, while the ARWMC and Scheltens' scales, and WMH volumes demonstrated significant correlations (standardized β = -0.19 to -0.24, Ps < 0.05) with cognitive domain scores using multivariate regression analysis, controlling for age, education, global and hippocampal atrophy in patients with AD.
CONCLUSIONS: This study suggests that the degree of complexity of WMH rating scales did not affect validation against WMH volumes, but did vary in validation against cognition. The simplest scale performed best in separating cognitive subgroups, but the more complex scales and quantification correlated better with cognitive measures, especially executive function. Hence the best choice of scale depends on the particular application.

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Year:  2011        PMID: 21971477     DOI: 10.3233/JAD-2011-0058

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  18 in total

1.  Reply to: "Periodic Limb Movements During Sleep and White Matter MRI Hyperintensity in Minor Stroke or TIA".

Authors:  Mark I Boulos; Ryan T Muir; Fuqiang Gao; Andrew S Lim; Richard H Swartz; Sandra E Black; Arthur S Walters; Brian J Murray
Journal:  Sleep       Date:  2017-05-01       Impact factor: 5.849

2.  Imaging the Alzheimer brain.

Authors:  J Wesson Ashford; Ahmad Salehi; Ansgar Furst; Peter Bayley; Giovanni B Frisoni; Clifford R Jack; Osama Sabri; Maheen M Adamson; Kerry L Coburn; John Olichney; Norbert Schuff; Daniel Spielman; Steven D Edland; Sandra Black; Allyson Rosen; David Kennedy; Michael Weiner; George Perry
Journal:  J Alzheimers Dis       Date:  2011       Impact factor: 4.472

3.  White matter hyperintensities associated with small vessel disease impair social cognition beside attention and memory.

Authors:  Jana Kynast; Leonie Lampe; Tobias Luck; Stefan Frisch; Katrin Arelin; Karl-Titus Hoffmann; Markus Loeffler; Steffi G Riedel-Heller; Arno Villringer; Matthias L Schroeter
Journal:  J Cereb Blood Flow Metab       Date:  2017-07-07       Impact factor: 6.200

4.  Combining structural brain changes improves the prediction of Alzheimer's disease and mild cognitive impairment.

Authors:  Ningnannan Zhang; Xiaowei Song; Yunting Zhang
Journal:  Dement Geriatr Cogn Disord       Date:  2012-07-02       Impact factor: 2.959

5.  MR Imaging-based Multimodal Autoidentification of Perivascular Spaces (mMAPS): Automated Morphologic Segmentation of Enlarged Perivascular Spaces at Clinical Field Strength.

Authors:  Erin L Boespflug; Daniel L Schwartz; David Lahna; Jeffrey Pollock; Jeffrey J Iliff; Jeffrey A Kaye; William Rooney; Lisa C Silbert
Journal:  Radiology       Date:  2017-08-29       Impact factor: 11.105

Review 6.  Imaging the Perivascular Space as a Potential Biomarker of Neurovascular and Neurodegenerative Diseases.

Authors:  Joel Ramirez; Courtney Berezuk; Alicia A McNeely; Fuqiang Gao; JoAnne McLaurin; Sandra E Black
Journal:  Cell Mol Neurobiol       Date:  2016-03-18       Impact factor: 5.046

7.  White Matter Changes are Associated with Ventricular Expansion in Aging, Mild Cognitive Impairment, and Alzheimer's Disease.

Authors:  Jean-Philippe Coutu; Alison Goldblatt; H Diana Rosas; David H Salat
Journal:  J Alzheimers Dis       Date:  2016       Impact factor: 4.472

8.  Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging.

Authors:  Daniel L Schwartz; Erin L Boespflug; David L Lahna; Jeffrey Pollock; Natalie E Roese; Lisa C Silbert
Journal:  Neuroimage       Date:  2019-08-25       Impact factor: 6.556

9.  Topographic distribution of white matter changes and lacunar infarcts in neurodegenerative and vascular dementia syndromes: A post-mortem 7.0-tesla magnetic resonance imaging study.

Authors:  Jacques De Reuck; Florent Auger; Nicolas Durieux; Charlotte Cordonnier; Vincent Deramecourt; Florence Pasquier; Claude-Alain Maurage; Didier Leys; Regis Bordet
Journal:  Eur Stroke J       Date:  2016-05-18

10.  White Matter Lesion Assessment in Patients with Cognitive Impairment and Healthy Controls: Reliability Comparisons between Visual Rating, a Manual, and an Automatic Volumetrical MRI Method-The Gothenburg MCI Study.

Authors:  Erik Olsson; Niklas Klasson; Josef Berge; Carl Eckerström; Ake Edman; Helge Malmgren; Anders Wallin
Journal:  J Aging Res       Date:  2013-01-16
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