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  Rapid quantification of white matter disconnection in the human brain

Zayed, A., Iturria-Medina, Y., Villringer, A., Sehm, B., & Steele, C. J. (2020). Rapid quantification of white matter disconnection in the human brain. In Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/EMBC44109.2020.9176229.

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 Creators:
Zayed, Abdelrahman1, Author
Iturria-Medina, Yasser2, Author
Villringer, Arno3, Author           
Sehm, Bernhard3, Author           
Steele, Christopher J.4, Author
Affiliations:
1PERFORM Center, Concordia University, Montréal, QC, Canada, ou_persistent22              
2Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada, ou_persistent22              
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
4Department of Psychology, Concordia University, Montréal, QC, Canada, ou_persistent22              

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Free keywords: Lesions; White matter; Brain modeling; Computational modeling; Tools; Sociology; Statistics
 Abstract: With an estimated five million new stroke survivors every year and a rapidly aging population suffering from hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for assessing the impact of lesions consider only location, type, and extent, while ignoring how the affected region was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity. Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion-based white matter disconnection maps that require only moderate computational resources. We achieve this by creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small regions. We then interrupt these connectivity models by projecting patients' lesions into them to compute predicted white matter disconnection. A quantified disconnection map can be computed for an individual patient in approximately 35 seconds using a single core CPU-based computation. In comparison, a similar quantification performed with other tools provided by MRtrix3 takes 5.47 minutes.

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Language(s): eng - English
 Dates: 2020-08-27
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/EMBC44109.2020.9176229
PMID: 33018324
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Title: Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Source Genre: Proceedings
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