Cerebral small vessel disease: a review focusing on pathophysiology, biomarkers, and machine learning strategies
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- dc.contributor.author Cuadrado-Godia, Elisa
- dc.contributor.author Dwivedi, Pratistha
- dc.contributor.author Sharma, Sanjiv
- dc.contributor.author Ois Santiago, Angel Javier
- dc.contributor.author Roquer, Jaume
- dc.contributor.author Balcells, Mercedes
- dc.contributor.author Laird, John R.
- dc.contributor.author Turk, Monika
- dc.contributor.author Suri, Harman S.
- dc.contributor.author Nicolaides, Andrew
- dc.contributor.author Saba, Luca
- dc.contributor.author Khanna, Narendra N.
- dc.contributor.author Suri, Jasjit S.
- dc.date.accessioned 2019-07-23T08:27:17Z
- dc.date.available 2019-07-23T08:27:17Z
- dc.date.issued 2018
- dc.description.abstract Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer's and Parkinson's disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.
- dc.format.mimetype application/pdf
- dc.identifier.citation Cuadrado-Godia E, Dwivedi P, Sharma S, Ois Santiago A, Roquer Gonzalez J, Balcells M. et al. Cerebral small vessel disease: a review focusing on pathophysiology, biomarkers, and machine learning strategies. J Stroke. 2018 Sep;20(3):302-320. DOI: 10.5853/jos.2017.02922
- dc.identifier.doi http://dx.doi.org/10.5853/jos.2017.02922
- dc.identifier.issn 2287-6391
- dc.identifier.uri http://hdl.handle.net/10230/42141
- dc.language.iso eng
- dc.publisher Korean Stroke Society
- dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/4.0/)whichpermits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
- dc.subject.keyword Biomarkers
- dc.subject.keyword Blood-brain barrier
- dc.subject.keyword Machine learning
- dc.subject.keyword Neuroimaging
- dc.subject.keyword Small vessel disease
- dc.title Cerebral small vessel disease: a review focusing on pathophysiology, biomarkers, and machine learning strategies
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion