J Stroke.
2018 Sep;20(3):302-320. 10.5853/jos.2017.02922.
Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies
- Affiliations
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- 1Department of Neurology, Hospital del Mar Medical Research Institute, Barcelona, Spain. ecuadrado@parcdesalutmar.cat
- 2Amity Institute of Biotechnology, Amity University, Gwalior, India.
- 3Department of Computer Science & Engineering and Information Technology, Madhav Institute of Technology and Science, Gwalior, India.
- 4Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- 5Department of Biological Engineering, IQS School of Engineering, Barcelona, Spain.
- 6Department of Cardiology, St. Helena Hospital, St. Helena, CA, USA.
- 7Deparment of Neurology, University Medical Centre Maribor, Maribor, Slovenia.
- 8Brown University, Providence, RI, USA.
- 9Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus.
- 10Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy.
- 11Department of Cardiology, Apollo Hospital, New Delhi, India.
- 12Stroke Monitoring Division, AtheroPoint, Roseville, CA, USA.
- KMID: 2428766
- DOI: http://doi.org/10.5853/jos.2017.02922
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.
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