Enrique Germany, Igor Teixeira, Venethia Danthine, Roberto Santalucia, Inci Cakiroglu, Andres Torres, Michele Verleysen, Jean Delbeke, Antoine Nonclercq, Riëm El Tahry
{"title":"植入前患者低密度脑电图得出的脑功能连通性指标作为VNS预后预测指标。","authors":"Enrique Germany, Igor Teixeira, Venethia Danthine, Roberto Santalucia, Inci Cakiroglu, Andres Torres, Michele Verleysen, Jean Delbeke, Antoine Nonclercq, Riëm El Tahry","doi":"10.1088/1741-2552/acf1cd","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective</i>. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.<i>Approach</i>. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.<i>Main results</i>. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (<i>p</i>< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.<i>Significance</i>. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.</p>","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors.\",\"authors\":\"Enrique Germany, Igor Teixeira, Venethia Danthine, Roberto Santalucia, Inci Cakiroglu, Andres Torres, Michele Verleysen, Jean Delbeke, Antoine Nonclercq, Riëm El Tahry\",\"doi\":\"10.1088/1741-2552/acf1cd\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Objective</i>. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.<i>Approach</i>. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.<i>Main results</i>. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (<i>p</i>< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.<i>Significance</i>. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.</p>\",\"PeriodicalId\":16753,\"journal\":{\"name\":\"Journal of neural engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neural engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1741-2552/acf1cd\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1741-2552/acf1cd","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors.
Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.
期刊介绍:
The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels.
The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.