Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.
Objectives: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonanc...
| Publicado en: | European Radiology Vol. 29; no. 7; pp. 3496 - 3506 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136842398&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136842398 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2019 vid: 29 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136842398 136842398 NLM30734849 10.1007/s00330-019-5997-2 NLM30734849 136842398 ppf: 3496 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy. aug: au: 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. affil: Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, 560029, Bangalore, Karnataka, India sug: subj: Thalamus Cerebellum Magnetic Resonance Imaging Methods Epilepsy, Temporal Lobe Diagnosis Young Adult Electroencephalography Thalamus Physiopathology Male Cerebellum Physiopathology Female Adult Clinical Assessment Tools Scales Adult: 19-44 years Male Female ab: Objectives: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE.Methods: Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks."Results: SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs.Conclusions: IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE.Key Points: • ICA of resting-state fMRI carries disease-specific information about epilepsy. • Machine learning can classify these components with 97.5% accuracy. • "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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