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Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

Authors :
Elisa Cuadrado-Godia
Pratistha Dwivedi
Sanjiv Sharma
Angel Ois Santiago
Jaume Roquer Gonzalez
Mercedes Balcells
John Laird
Monika Turk
Harman S. Suri
Andrew Nicolaides
Luca Saba
Narendra N. Khanna
Jasjit S. Suri
Source :
Journal of Stroke, Vol 20, Iss 3, Pp 302-320 (2018)
Publication Year :
2018
Publisher :
Korean Stroke Society, 2018.

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.

Details

Language :
English
ISSN :
22876391 and 22876405
Volume :
20
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Journal of Stroke
Publication Type :
Academic Journal
Accession number :
edsdoj.7096af260e114ff0a2cc455a29f93727
Document Type :
article
Full Text :
https://doi.org/10.5853/jos.2017.02922