Sara Secci , Piergiuseppe Liuzzi , Bahia Hakiki , Rachele Burali , Francesca Draghi , Anna Maria Romoli , Azzurra di Palma , Maenia Scarpino , Antonello Grippo , Francesca Cecchi , Andrea Frosini , Andrea Mannini
{"title":"基于低密度脑电图的功能连接性可区分微意识状态的正负。","authors":"Sara Secci , Piergiuseppe Liuzzi , Bahia Hakiki , Rachele Burali , Francesca Draghi , Anna Maria Romoli , Azzurra di Palma , Maenia Scarpino , Antonello Grippo , Francesca Cecchi , Andrea Frosini , Andrea Mannini","doi":"10.1016/j.clinph.2024.04.021","DOIUrl":null,"url":null,"abstract":"<div><p><strong>Objective</strong>: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. <strong>Methods</strong>: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (<span><math><mrow><mi>α</mi><mo>,</mo><mspace></mspace><mi>θ</mi><mo>,</mo><mspace></mspace><mi>δ</mi></mrow></math></span>). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. <strong>Results</strong>: A lower level of brain activity integration was found in the MCS− group in the <span><math><mrow><mi>α</mi></mrow></math></span> band. Instead, in the <span><math><mrow><mi>δ</mi></mrow></math></span> band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of <span><math><mrow><mn>79</mn><mo>%</mo></mrow></math></span> (sensitivity and specificity of <span><math><mrow><mn>74</mn><mo>%</mo></mrow></math></span> and <span><math><mrow><mn>85</mn><mo>%</mo></mrow></math></span> respectively). <strong>Conclusion</strong>: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. <strong>Significance</strong>: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.</p></div>","PeriodicalId":10671,"journal":{"name":"Clinical Neurophysiology","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus\",\"authors\":\"Sara Secci , Piergiuseppe Liuzzi , Bahia Hakiki , Rachele Burali , Francesca Draghi , Anna Maria Romoli , Azzurra di Palma , Maenia Scarpino , Antonello Grippo , Francesca Cecchi , Andrea Frosini , Andrea Mannini\",\"doi\":\"10.1016/j.clinph.2024.04.021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><strong>Objective</strong>: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. <strong>Methods</strong>: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (<span><math><mrow><mi>α</mi><mo>,</mo><mspace></mspace><mi>θ</mi><mo>,</mo><mspace></mspace><mi>δ</mi></mrow></math></span>). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. <strong>Results</strong>: A lower level of brain activity integration was found in the MCS− group in the <span><math><mrow><mi>α</mi></mrow></math></span> band. Instead, in the <span><math><mrow><mi>δ</mi></mrow></math></span> band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of <span><math><mrow><mn>79</mn><mo>%</mo></mrow></math></span> (sensitivity and specificity of <span><math><mrow><mn>74</mn><mo>%</mo></mrow></math></span> and <span><math><mrow><mn>85</mn><mo>%</mo></mrow></math></span> respectively). <strong>Conclusion</strong>: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. <strong>Significance</strong>: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.</p></div>\",\"PeriodicalId\":10671,\"journal\":{\"name\":\"Clinical Neurophysiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Neurophysiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1388245724001469\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Neurophysiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1388245724001469","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus
Objective: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. Methods: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. Results: A lower level of brain activity integration was found in the MCS− group in the band. Instead, in the band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of (sensitivity and specificity of and respectively). Conclusion: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. Significance: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.
期刊介绍:
As of January 1999, The journal Electroencephalography and Clinical Neurophysiology, and its two sections Electromyography and Motor Control and Evoked Potentials have amalgamated to become this journal - Clinical Neurophysiology.
Clinical Neurophysiology is the official journal of the International Federation of Clinical Neurophysiology, the Brazilian Society of Clinical Neurophysiology, the Czech Society of Clinical Neurophysiology, the Italian Clinical Neurophysiology Society and the International Society of Intraoperative Neurophysiology.The journal is dedicated to fostering research and disseminating information on all aspects of both normal and abnormal functioning of the nervous system. The key aim of the publication is to disseminate scholarly reports on the pathophysiology underlying diseases of the central and peripheral nervous system of human patients. Clinical trials that use neurophysiological measures to document change are encouraged, as are manuscripts reporting data on integrated neuroimaging of central nervous function including, but not limited to, functional MRI, MEG, EEG, PET and other neuroimaging modalities.