Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy-Reference-Cited by-同舟云学术

Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy

Published:2019-02-08 Issue:7 Volume:29 Page:3496-3505
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Bharath Rose Dawn,Panda Rajanikant,Raj Jeetu,Bhardwaj Sujas,Sinha Sanjib,Chaitanya Ganne,Raghavendra Kenchaiah,Mundlamuri Ravindranadh C.,Arimappamagan Arivazhagan,Rao Malla Bhaskara,Rajeshwaran Jamuna,Thennarasu Kandavel,Majumdar Kaushik K.,Satishchandra Parthasarthy,Gandhi Tapan K.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s00330-019-5997-2.pdf

Reference46 articles.

1. Chiang S, Haneef Z (2014) Graph theory findings in the pathophysiology of temporal lobe epilepsy. Clin Neurophysiol 125:1295–1305

2. Liao W, Zhang Z, Pan Z et al (2010) Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One 5:e8525

3. Vlooswijk MC, Vaessen MJ, Jansen JF et al (2011) Loss of network efficiency associated with cognitive decline in chronic epilepsy. Neurology 77:938–944

4. Liao W, Zhang Z, Pan Z et al (2011) Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp 32:883–895

5. Widjaja E, Zamyadi M, Raybaud C, Snead OC, Smith ML (2013) Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy. AJNR Am J Neuroradiol 34:2386–2392

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