Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology
Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate t...
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| Format: | Article |
| Language: | English |
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Elsevier
2018-01-01
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| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158218302006 |
| _version_ | 1818444254369808384 |
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| author | Abbas Babajani-Feremi Negar Noorizadeh Basanagoud Mudigoudar James W. Wheless |
| author_facet | Abbas Babajani-Feremi Negar Noorizadeh Basanagoud Mudigoudar James W. Wheless |
| author_sort | Abbas Babajani-Feremi |
| collection | DOAJ |
| description | Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment Keywords: Vagus nerve stimulation (VNS), Seizure outcome, VNS efficacy, Magnetoencephalography (MEG), Functional connectivity, Phase-locking value (PLV), Graph measures, Human connectome project (HCP) |
| first_indexed | 2024-12-14T19:13:01Z |
| format | Article |
| id | doaj.art-eef09d53098346ca85c0b6d6cae0f20a |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-14T19:13:01Z |
| publishDate | 2018-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-eef09d53098346ca85c0b6d6cae0f20a2022-12-21T22:50:41ZengElsevierNeuroImage: Clinical2213-15822018-01-0119990999Predicting seizure outcome of vagus nerve stimulation using MEG-based network topologyAbbas Babajani-Feremi0Negar Noorizadeh1Basanagoud Mudigoudar2James W. Wheless3Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USA; Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA; Corresponding author at Department of Pediatrics, Neuroscience Institute, Le Bonheur Children's Hospital, The University of Tennessee Health Science Center, 51 N. Dunlap St., Suite P320, Memphis, TN 38105, USA.Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USADepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USADepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USAVagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment Keywords: Vagus nerve stimulation (VNS), Seizure outcome, VNS efficacy, Magnetoencephalography (MEG), Functional connectivity, Phase-locking value (PLV), Graph measures, Human connectome project (HCP)http://www.sciencedirect.com/science/article/pii/S2213158218302006 |
| spellingShingle | Abbas Babajani-Feremi Negar Noorizadeh Basanagoud Mudigoudar James W. Wheless Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology NeuroImage: Clinical |
| title | Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology |
| title_full | Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology |
| title_fullStr | Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology |
| title_full_unstemmed | Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology |
| title_short | Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology |
| title_sort | predicting seizure outcome of vagus nerve stimulation using meg based network topology |
| url | http://www.sciencedirect.com/science/article/pii/S2213158218302006 |
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