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Title: Disconnectomics: Stroke-related disconnection and dysfunction in distributed brain networks.
Austin Authors: Veldsman, Michele;Brodtmann, Amy 
Affiliation: Department of Experimental Psychology, University of Oxford, Oxford, UK
The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
Austin Health, Heidelberg, Victoria, Australia
Eastern Cognitive Disorders Clinic, Monash University, Melbourne, Australia
Royal Melbourne Hospital, Melbourne, Australia
Issue Date: 2019
Date: 2018-10-05
Publication information: International Journal of Stroke 2019; 14(1): 6-8
Abstract: Modern clinical neuroscience was built on observations of how localized damage caused specific functional, cognitive and behavioral deficits. Stroke neurology was a cornerstone of understanding this functional specialization in the brain. But most lesion-symptom mapping provides little prognostic value above clinical observations. Stroke topography remains a poor indicator of long-term outcome, and with stroke a major risk factor for dementia, there is strong incentive to find markers of predictive value. There is now growing recognition that the damage caused by stroke does not occur in isolation but is embedded within a complex, highly interconnected, organized and dynamic system: the connectome. Early theories of the widespread effect of focal lesions are resurfacing, buoyed by sophisticated new methods and large-scale data sets. As with all emerging methods and technologies, there may be healthy skepticism as to the appropriateness of the method to the population under investigation or doubt that connectivity-derived metrics will ever be clinically translatable. While we acknowledge that there remain significant technical challenges to overcome, we argue that the methods provide real potential to illuminate our understanding of the widespread effects and clinical syndromes that can arise from diverse focal damage.
DOI: 10.1177/1747493018806166
Journal: International Journal of Stroke
PubMed URL: 30289363
Type: Journal Article
Subjects: Connectivity
Appears in Collections:Journal articles

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