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Wang J, Lai Q, Han J, Qin P, Wu H. Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness. Brain Res 2024; 1843:149133. [PMID: 39084451 DOI: 10.1016/j.brainres.2024.149133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/29/2024] [Accepted: 07/25/2024] [Indexed: 08/02/2024]
Abstract
The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.
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Affiliation(s)
- Jiaying Wang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qiantu Lai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China; Pazhou Lab, Guangzhou 510330, China.
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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2
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Szirmai D, Zabihi A, Kói T, Hegyi P, Wenning AS, Engh MA, Molnár Z, Csukly G, Horváth AA. EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis. Heliyon 2024; 10:e31277. [PMID: 38826755 PMCID: PMC11141356 DOI: 10.1016/j.heliyon.2024.e31277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 06/04/2024] Open
Abstract
Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.
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Affiliation(s)
- Danuta Szirmai
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Arashk Zabihi
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Tamás Kói
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
- Mathematical Institute, Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary (Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Péter Hegyi
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
- Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungary (Tömő u. 25-29, Budapest, H-1083, Hungary
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary (Szigeti út 12., Pécs, H-7624, Hungary
| | - Alexander Schulze Wenning
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Marie Anne Engh
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Zsolt Molnár
- Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary (Üllői út 78., Budapest, H-1082, Hungary
- Department of Anesthesiology and Intensive Therapy, Poznan University of Medical Sciences, Poznan, Poland (49 Przybyszewskiego St, Poznan, Poland, 60-355, Poland
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary (Balassa u. 6, Budapest, H-1083, Hungary
| | - András Attila Horváth
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
- Neurocognitive Research Center, National Institute of Mental Health, Neurology, Neurosurgery, Budapest, Hungary (Amerikai út 57., Budapest, H-1145, Hungary
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary (Üllői út 26., Budapest, H-1085, Hungary
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3
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Abe T, Asai Y, Lintas A, Villa AEP. Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions. Sci Rep 2024; 14:8521. [PMID: 38609457 PMCID: PMC11372163 DOI: 10.1038/s41598-024-59004-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.
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Affiliation(s)
- Takeshi Abe
- AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Division of Systems Medicine and Informatics, Research Institute of Cell Design Medical Science, Yamaguchi University, Yamaguchi, 755-8505, Japan
| | - Yoshiyuki Asai
- AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Division of Systems Medicine and Informatics, Research Institute of Cell Design Medical Science, Yamaguchi University, Yamaguchi, 755-8505, Japan
| | - Alessandra Lintas
- HEC-LABEX, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland
- Neuroheuristic Research Group & Complexity Sciences Research Group, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland
| | - Alessandro E P Villa
- Neuroheuristic Research Group & Complexity Sciences Research Group, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland.
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4
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Liu G, Chi B. Technological Modalities in the Assessment and Treatment of Disorders of Consciousness. Phys Med Rehabil Clin N Am 2024; 35:109-126. [PMID: 37993182 DOI: 10.1016/j.pmr.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Over the last 10 years, there have been rapid advances made in technologies that can be utilized in the diagnosis and treatment of patients with a disorder of consciousness (DoC). This article provides a comprehensive review of these modalities including the evidence supporting their potential use in DoC. This review specifically addresses diagnostic, non-invasive therapeutic, and invasive therapeutic technological modalities except for neuroimaging, which is discussed in another article. While technologic advances appear promising for both assessment and treatment of patients with a DoC, high-quality evidence supporting widespread clinical adoption remains limited.
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Affiliation(s)
- Gang Liu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Bradley Chi
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, 7200 Cambridge Street, Houston, TX 77030, USA.
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5
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Xiong Q, Le K, Wang Y, Tang Y, Dong X, Zhong Y, Zhou Y, Feng Z. A prediction model of clinical outcomes in prolonged disorders of consciousness: A prospective cohort study. Front Neurosci 2023; 16:1076259. [PMID: 36817098 PMCID: PMC9936154 DOI: 10.3389/fnins.2022.1076259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/31/2022] [Indexed: 02/05/2023] Open
Abstract
Objective This study aimed to establish and validate a prediction model for clinical outcomes in patients with prolonged disorders of consciousness (pDOC). Methods A total of 170 patients with pDOC enrolled in our rehabilitation unit were included and divided into training (n = 119) and validation sets (n = 51). Independent predictors for improved clinical outcomes were identified by univariate and multivariate logistic regression analyses, and a nomogram model was established. The nomogram performance was quantified using receiver operating curve (ROC) and calibration curves in the training and validated sets. A decision curve analysis (DCA) was performed to evaluate the clinical usefulness of this nomogram model. Results Univariate and multivariate logistic regression analyses indicated that age, diagnosis at entry, serum albumin (g/L), and pupillary reflex were the independent prognostic factors that were used to construct the nomogram. The area under the curve in the training and validation sets was 0.845 and 0.801, respectively. This nomogram model showed good calibration with good consistency between the actual and predicted probabilities of improved outcomes. The DCA demonstrated a higher net benefit in clinical decision-making compared to treating all or none. Conclusion Several feasible, cost-effective prognostic variables that are widely available in hospitals can provide an efficient and accurate prediction model for improved clinical outcomes and support clinicians to offer suitable clinical care and decision-making to patients with pDOC and their family members.
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Affiliation(s)
- Qi Xiong
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Kai Le
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yong Wang
- Department of Medical Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yunliang Tang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiaoyang Dong
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yuan Zhong
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yao Zhou
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Zhen Feng
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China,*Correspondence: Zhen Feng ✉
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Ballanti S, Campagnini S, Liuzzi P, Hakiki B, Scarpino M, Macchi C, Oddo CM, Carrozza MC, Grippo A, Mannini A. EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review. Clin Neurophysiol 2022; 144:98-114. [PMID: 36335795 DOI: 10.1016/j.clinph.2022.09.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting. METHODS We conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases. RESULTS The search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery. CONCLUSIONS This study's findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations. SIGNIFICANCE This study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC.
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Affiliation(s)
- Sara Ballanti
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Silvia Campagnini
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy.
| | | | - Claudio Macchi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; Department of Experimental and Clinical Medicine, University of Florence, Firenze 50143, Italy.
| | - Calogero Maria Oddo
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Maria Chiara Carrozza
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | | | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy.
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7
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Zhang C, Han S, Li Z, Wang X, Lv C, Zou X, Zhu F, Zhang K, Lu S, Bie L, Lv G, Guo Y. Multidimensional Assessment of Electroencephalography in the Neuromodulation of Disorders of Consciousness. Front Neurosci 2022; 16:903703. [PMID: 35812212 PMCID: PMC9260110 DOI: 10.3389/fnins.2022.903703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
In the present study, we aimed to elucidate changes in electroencephalography (EEG) metrics during recovery of consciousness and to identify possible clinical markers thereof. More specifically, in order to assess changes in multidimensional EEG metrics during neuromodulation, we performed repeated stimulation using a high-density transcranial direct current stimulation (HD-tDCS) protocol in 42 patients with disorders of consciousness (DOC). Coma Recovery Scale-Revised (CRS-R) scores and EEG metrics [brain network indicators, spectral energy, and normalized spatial complexity (NSC)] were obtained before as well as fourteen days after undergoing HD-tDCS stimulation. CRS-R scores increased in the responders (R +) group after HD-tDCS stimulation. The R + group also showed increased spectral energy in the alpha2 and beta1 bands, mainly at the frontal and parietal electrodes. Increased graphical metrics in the alpha1, alpha2, and beta1 bands combined with increased NSC in the beta2 band in the R + group suggested that improved consciousness was associated with a tendency toward stronger integration in the alpha1 band and greater isolation in the beta2 band. Following this, using NSC as a feature to predict responsiveness through machine learning, which yielded a prediction accuracy of 0.929, demonstrated that the NSC of the alpha and gamma bands at baseline successfully predicted improvement in consciousness. According to our findings reported herein, we conclude that neuromodulation of the posterior lobe can lead to an EEG response related to consciousness in DOC, and that the posterior cortex may be one of the key brain areas involved in the formation or maintenance of consciousness.
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Affiliation(s)
- Chunyun Zhang
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Shuai Han
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Zean Li
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - XinJun Wang
- Department of Neurosurgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chuanxiang Lv
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Xiangyun Zou
- Department of Pediatrics, Qilu Hospital of Shandong University, Qingdao, China
| | - Fulei Zhu
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Kang Zhang
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Shouyong Lu
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Li Bie
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yongkun Guo
- Department of Neurosurgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou, China
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Servider J, Saadon JR, Adachi J, Shen E, Mikell CB, Mofakham S. Cortical recordings reveal hidden early signs of recovery following traumatic brain injury: A case report. Brain Res 2022; 1786:147903. [PMID: 35381214 DOI: 10.1016/j.brainres.2022.147903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 11/02/2022]
Abstract
Prognosticating recovery of consciousness after severe traumatic brain injury (TBI) is a difficult task. Understanding the mechanism of recovery of consciousness in these patients will undoubtedly help clarify this issue. Recent research has underscored the importance of electrophysiological data in characterizing the state of the brain during this period of unconsciousness. Here, we investigated cortical electrophysiological recordings from a single TBI patient and discovered that high-frequency activity associated with the return of consciousness reappeared in a spatiotemporal fashion. We observed a shift toward higher frequencies first in the anterior cingulate cortex, and then later in the dorsolateral prefrontal cortex. This finding suggests that recovery may originate in more internal cortices and progress to superficial ones. Although this observation occurred in a single patient, it points to a potential mechanism for recovery of normal cortical activity in the return of consciousness following TBI.
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Affiliation(s)
- John Servider
- Department of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Jordan R Saadon
- Department of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Joseph Adachi
- Department of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Erica Shen
- Department of Neurosurgery, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Charles B Mikell
- Department of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Sima Mofakham
- Department of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA; Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA.
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9
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Liu Y, Li Z, Bai Y. Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: A perspective from electroencephalogram studies. Front Neurosci 2022; 16:1024278. [PMID: 36778900 PMCID: PMC9909102 DOI: 10.3389/fnins.2022.1024278] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 12/19/2022] [Indexed: 01/27/2023] Open
Abstract
Background Electroencephalogram (EEG) studies have established many characteristics relevant to consciousness levels of patients with disorder of consciousness (DOC). Although the frontal and parietal brain regions were often highlighted in DOC studies, their electro-neurophysiological roles in constructing human consciousness remain unclear because of the fragmented information from literatures and the complexity of EEG characteristics. Methods Existing EEG studies of DOC patients were reviewed and summarized. Relevant findings and results about the frontal and parietal regions were filtered, compared, and concluded to clarify their roles in consciousness classification and outcomes. The evidence covers multi-dimensional EEG characteristics including functional connectivity, non-linear dynamics, spectrum power, transcranial magnetic stimulation-electroencephalography (TMS-EEG), and event-related potential. Results and conclusion Electroencephalogram characteristics related to frontal and parietal regions consistently showed high relevance with consciousness: enhancement of low-frequency rhythms, suppression of high-frequency rhythms, reduction of dynamic complexity, and breakdown of networks accompanied with decreasing consciousness. Owing to the limitations of EEG, existing studies have not yet clarified which one between the frontal and parietal has priority in consciousness injury or recovery. Source reconstruction with high-density EEG, machine learning with large samples, and TMS-EEG mapping will be important approaches for refining EEG awareness locations.
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Affiliation(s)
- Yesong Liu
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Zhaoyi Li
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Yang Bai
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
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10
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Mohammadi E, Makkiabadi B, Shamsollahi MB, Reisi P, Kermani S. Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification. Int J Neural Syst 2021; 32:2250004. [PMID: 34967704 DOI: 10.1142/s0129065722500046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification.
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Affiliation(s)
- Ehsan Mohammadi
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan, University of Medical Sciences, Isfahan, Iran
| | - Bahador Makkiabadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical, Sciences, Tehran, Iran
| | - Mohammad Bagher Shamsollahi
- Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Parham Reisi
- Department of Physiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeed Kermani
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan, University of Medical Sciences, Isfahan, Iran
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11
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Wutzl B, Golaszewski SM, Leibnitz K, Langthaler PB, Kunz AB, Leis S, Schwenker K, Thomschewski A, Bergmann J, Trinka E. Narrative Review: Quantitative EEG in Disorders of Consciousness. Brain Sci 2021; 11:brainsci11060697. [PMID: 34070647 PMCID: PMC8228474 DOI: 10.3390/brainsci11060697] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/17/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.
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Affiliation(s)
- Betty Wutzl
- Graduate School of Information Science and Technology, Osaka University, Suita 565-0871, Japan; (B.W.); (K.L.)
- Symbiotic Intelligent Systems Research Center, Osaka University, Suita 565-0871, Japan
| | - Stefan M. Golaszewski
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Kenji Leibnitz
- Graduate School of Information Science and Technology, Osaka University, Suita 565-0871, Japan; (B.W.); (K.L.)
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan
| | - Patrick B. Langthaler
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Department of Mathematics, Paris Lodron University of Salzburg, 5020 Salzburg, Austria
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Alexander B. Kunz
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
| | - Stefan Leis
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Kerstin Schwenker
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Jürgen Bergmann
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
- Correspondence: ; Tel.: +43-5-7255-34600
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Bai Y, Lin Y, Ziemann U. Managing disorders of consciousness: the role of electroencephalography. J Neurol 2020; 268:4033-4065. [PMID: 32915309 PMCID: PMC8505374 DOI: 10.1007/s00415-020-10095-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/18/2020] [Accepted: 07/18/2020] [Indexed: 02/07/2023]
Abstract
Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.
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Affiliation(s)
- Yang Bai
- International Vegetative State and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
- Department of Neurology and Stroke, University of Tübingen, Hoppe‑Seyler‑Str. 3, 72076, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Yajun Lin
- International Vegetative State and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Hoppe‑Seyler‑Str. 3, 72076, Tübingen, Germany.
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany.
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13
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Nadin D, Duclos C, Mahdid Y, Rokos A, Badawy M, Létourneau J, Arbour C, Plourde G, Blain-Moraes S. Brain network motif topography may predict emergence from disorders of consciousness: a case series. Neurosci Conscious 2020; 2020:niaa017. [PMID: 33376599 PMCID: PMC7751128 DOI: 10.1093/nc/niaa017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 03/18/2020] [Accepted: 06/03/2020] [Indexed: 11/16/2022] Open
Abstract
Neuroimaging methods have improved the accuracy of diagnosis in patients with disorders of consciousness (DOC), but novel, clinically translatable methods for prognosticating this population are still needed. In this case series, we explored the association between topographic and global brain network properties and prognosis in patients with DOC. We recorded high-density electroencephalograms in three patients with acute or chronic DOC, two of whom also underwent an anesthetic protocol. In these two cases, we compared functional network motifs, network hubs and power topography (i.e. topographic network properties), as well as relative power and graph theoretical measures (i.e. global network properties), at baseline, during exposure to anesthesia and after recovery from anesthesia. We also compared these properties to a group of healthy, conscious controls. At baseline, the topographic distribution of nodes participating in alpha motifs resembled conscious controls in patients who later recovered consciousness and high relative power in the delta band was associated with a negative outcome. Strikingly, the reorganization of network motifs, network hubs and power topography under anesthesia followed by their return to a baseline patterns upon recovery from anesthesia, was associated with recovery of consciousness. Our findings suggest that topographic network properties measured at the single-electrode level might provide more prognostic information than global network properties that are averaged across the brain network. In addition, we propose that the brain network's capacity to reorganize in response to a perturbation is a precursor to the recovery of consciousness in DOC patients.
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Affiliation(s)
- Danielle Nadin
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Catherine Duclos
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Yacine Mahdid
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Alexander Rokos
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Mohamed Badawy
- Montreal Neurological Hospital and Institute, McGill University Health Center, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
| | - Justin Létourneau
- Montreal Neurological Hospital and Institute, McGill University Health Center, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
| | - Caroline Arbour
- Centre de recherche, CIUSSS du-Nord-de-l’Île-de-Montréal, Montreal, QC, Canada
- Faculty of Nursing, Université de Montréal, Montreal, QC, Canada
| | - Gilles Plourde
- Montreal Neurological Hospital and Institute, McGill University Health Center, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, QC, Canada
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14
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Li Z, Dong Z, Bai X, Liu M. Characterizing the orientation selectivity in V1 and V4 of macaques by quadratic phase coupling. J Neural Eng 2020; 17:036028. [PMID: 32480396 DOI: 10.1088/1741-2552/ab9843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Orientation selectivity is one of the significant characteristics of neurons in the primary visual cortex (V1). Some neurons in extrastriate visual cortical areas also exhibit certain orientation selectivity. But it is still not well understood that how the orientation selectivity generates. Most previous studies about the orientation selectivity are based on the spike firing rate. However, the spikes are prone to be biased by the detection and sorting algorithms. Then, in this paper, the local field potential (LFP) is adopted to investigate the mechanism of orientation selectivity. APPROACH We used the quadratic phase coupling (QPC), which was calculated by wavelet bicoherence, to describe the characteristics of orientation selectivity available in V1 and V4. The raw wideband neural signals were recorded by two chronically implanted multi-electrode arrays, which were placed in V1 and V4 respectively in two macaques performing a selective visual attention task. MAIN RESULTS There is a strong correlation between the total bicoherence (TotalBic), which is a quantization for the overall QPC of frequency pairs in gamma band, and the grating orientation. Furthermore, the QPC distribution at the non-preferred orientation is mainly concentrated in the low frequencies (30-40 Hz) of gamma; while the QPC distribution at the preferred orientation concentrates in both the low frequencies and high frequencies (60-80 Hz) of gamma. In addition, the TotalBic of the gamma-band LFP between V1 and V4 varies with the grating orientations, indicating that the QPC is available in the feedforward link and the gamma-band LFP in V1 modulates the QPC in V4. SIGNIFICANCE The QPC reflects the orientations of the sinusoidal grating and describes the interaction of gamma-band LFP between different brain regions. Our results suggest that the QPC is an alternative avenue to explore the mechanism for generating orientation selectivity of visual neurons effectively.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, People's Republic of China. Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, People's Republic of China
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15
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Hayashi K, Indo K, Sawa T. Anaesthesia-dependent oscillatory EEG features in the super-elderly. Clin Neurophysiol 2020; 131:2150-2157. [PMID: 32682243 DOI: 10.1016/j.clinph.2020.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/06/2020] [Accepted: 05/23/2020] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Although the characteristics of electroencephalograms (EEGs) have been reported to change with age, anaesthesia-dependent oscillatory features and reactivity of the super-elderly EEG to anaesthesia have not been examined in detail. METHODS Participants comprised 20 super-elderly patients (age; mean ± standard deviation, 87.1 ± 3.8 years) and 20 young adult patients (35.5 ± 8.5 years). At three levels of sevoflurane anaesthesia (minimum alveolar concentration [MAC] of 0.3, 0.7, and 1.4), oscillatory features of the frontal EEG were examined by analysing quadratic phase coupling (bicoherence) and power spectrum in α and δ-θ areas and compared in an anaesthesia-dependent manner, using the Friedman test. RESULTS Among super-elderly individuals, bicoherences in the δ-θ area showed anaesthesia-dependent increases (median [interquartile range], 12.9% [5.2%], 19.2% [9.1%], 23.3% [8.7%]; 0.3, 0.7, 1.4 MAC sevoflurane, p = 0.000), whereas bicoherence in the α area did not change at these different anaesthesia levels (11.2% [3.9%], 12.5% [4.4%], 14.1% [5.7%], respectively; p = 0.142), counter to the results found in young adult patients, where both δ-θ and α bicoherences changed with anaesthesia. CONCLUSIONS In the super-elderly, δ-θ bicoherence of EEG shows anaesthesia- dependent changes, whereas α activity remains small irrespective of anaesthesia level. SIGNIFICANCE Quantification of δ-θ bicoherence is a candidate for anaesthesia monitoring in the super-elderly.
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Affiliation(s)
- K Hayashi
- Department of Anesthesiology, Kyoto Chubu Medical Center, Yagi, Ueno 25, Nantan City, Kyoto, Japan; Medical Education and Research Center, Meiji University of Integrative Medicine, Kyoto, Japan.
| | - K Indo
- Department of Anesthesiology, Kyoto Chubu Medical Center, Yagi, Ueno 25, Nantan City, Kyoto, Japan.
| | - T Sawa
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
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