Topological measures of connectomics for low grades Glioma

Amoah, Benjamin and Crimi, Alessandro (2017) Topological measures of connectomics for low grades Glioma. In: Brainlesion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10154 . Springer-Verlag, GRC, pp. 23-31. ISBN 9783319555232

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Abstract

Recent advancements in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. In fact, several neurological disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectomebased approach might be useful in quantifying the status of patients, in supporting surgical procedures, and ultimately shedding light on the underlying mechanisms and the recovery process. In this manuscript, by using graph theoretical methods of segregation and integration, topological structural connectivity is studied comparing patients with low grade glioma to healthy control. These measures suggest that it is possible to quantify the status of patients pre- and post-surgical intervention to evaluate the condition.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
136002
Deposited By:
Deposited On:
30 Aug 2019 12:40
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Sep 2020 06:51