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Topological Measures of Connectomics for Low Grades Glioma

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 10154)

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 connectome-based 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.

Keywords

  • Autism Spectrum Disorder
  • Autism Spectrum Disorder
  • Fractional Anisotropy
  • Functional Connectivity
  • Cluster Coefficient

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    http://cancerimagingarchive.net.

  2. 2.

    http://fcon_1000.projects.nitrc.org/indi/pro/nki.html.

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Correspondence to Alessandro Crimi .

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Amoah, B., Crimi, A. (2016). Topological Measures of Connectomics for Low Grades Glioma. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_3

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