Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales
BackgroundBrain assessment shows great values in prognosis, treatment, resource allocation, and decision-making for patients with disorders of consciousness (DOC). However, less research focused on cortical conditions of patients with unresponsive wakefulness syndrome (UWS).MethodsWe recorded restin...
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| Format: | Article |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1071594/full |
| _version_ | 1797956076776194048 |
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| author | Chen Li Yong Wang Wende Li Wende Li Yi Yang Xiaoyu Xia Xiaoyu Xia |
| author_facet | Chen Li Yong Wang Wende Li Wende Li Yi Yang Xiaoyu Xia Xiaoyu Xia |
| author_sort | Chen Li |
| collection | DOAJ |
| description | BackgroundBrain assessment shows great values in prognosis, treatment, resource allocation, and decision-making for patients with disorders of consciousness (DOC). However, less research focused on cortical conditions of patients with unresponsive wakefulness syndrome (UWS).MethodsWe recorded resting-state EEG and TMS-EEG from post-anoxic patients with UWS, diagnosed by repeated Coma Recovery Scale-Revised (CRS-R). Measurements of functional connectivity and networks were performed by phase lock value (PLV) and network parameters of graph theory (average path length, clustering coefficient, and small-world). Global cortical reactivity values (GCRV) were used to assess cortical excitability.ResultsThe coefficient of variation (CV) presented marked inter-individual variations of PLV (CV = 0.285), network parameters (CV > 0.2), and GCRV (CV = 0.929) within these patients. The patients’ PLV and network parameters at theta and alpha bands significantly correlated with their GCRV values. Patients with higher PLV (r = 0.560, 0.406), as well as better preserved network (lower average path length (r = −0.522, −0.483), higher clustering coefficient (r = 0.522, 0.445), and small-world (r = 0.522, 0.445) at theta and alpha bands, presented higher GCRV. The functional connectivity, which is significantly correlated with frontal GCRV, is also mainly located in the frontal region. These correlations were not significant at other frequency bands: Delta, beta, and gamma bands.ConclusionThese findings suggested that the CRS-R-diagnosed post-anoxic patients with UWS had very different cortical conditions. Functional networks and cortical excitability measured by TMS-EEG could complement behavioral assessment to assess these patients’ cortical conditions.SignificanceIt provides a deeper understanding of neurophysiological dysfunction in patients with UWS and hints to the clinics that neural-electrophysiological assessment for such patients may be necessary to acquire their brain conditions, which may benefit stratified management for them. |
| first_indexed | 2024-04-10T23:43:12Z |
| format | Article |
| id | doaj.art-f852e98798804fdd8a05c932a2a99e36 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-10T23:43:12Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-f852e98798804fdd8a05c932a2a99e362023-01-11T05:40:19ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-01-011610.3389/fnins.2022.10715941071594Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scalesChen Li0Yong Wang1Wende Li2Wende Li3Yi Yang4Xiaoyu Xia5Xiaoyu Xia6Department of Interventional and Vascular Neurosurgery, The Characteristic Medical Center of People’s Liberation Army (PLA) Rocket Force, Beijing, ChinaZhuhai University of Macau (UM) Science & Technology Research Institute, Zhuhai, ChinaSenior Department of Neurosurgery, The First Medical Center of People’s Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Neurosurgery, The Seventh Medical Center of People’s Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Beijing, ChinaSenior Department of Neurosurgery, The First Medical Center of People’s Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Neurosurgery, The Seventh Medical Center of People’s Liberation Army (PLA) General Hospital, Beijing, ChinaBackgroundBrain assessment shows great values in prognosis, treatment, resource allocation, and decision-making for patients with disorders of consciousness (DOC). However, less research focused on cortical conditions of patients with unresponsive wakefulness syndrome (UWS).MethodsWe recorded resting-state EEG and TMS-EEG from post-anoxic patients with UWS, diagnosed by repeated Coma Recovery Scale-Revised (CRS-R). Measurements of functional connectivity and networks were performed by phase lock value (PLV) and network parameters of graph theory (average path length, clustering coefficient, and small-world). Global cortical reactivity values (GCRV) were used to assess cortical excitability.ResultsThe coefficient of variation (CV) presented marked inter-individual variations of PLV (CV = 0.285), network parameters (CV > 0.2), and GCRV (CV = 0.929) within these patients. The patients’ PLV and network parameters at theta and alpha bands significantly correlated with their GCRV values. Patients with higher PLV (r = 0.560, 0.406), as well as better preserved network (lower average path length (r = −0.522, −0.483), higher clustering coefficient (r = 0.522, 0.445), and small-world (r = 0.522, 0.445) at theta and alpha bands, presented higher GCRV. The functional connectivity, which is significantly correlated with frontal GCRV, is also mainly located in the frontal region. These correlations were not significant at other frequency bands: Delta, beta, and gamma bands.ConclusionThese findings suggested that the CRS-R-diagnosed post-anoxic patients with UWS had very different cortical conditions. Functional networks and cortical excitability measured by TMS-EEG could complement behavioral assessment to assess these patients’ cortical conditions.SignificanceIt provides a deeper understanding of neurophysiological dysfunction in patients with UWS and hints to the clinics that neural-electrophysiological assessment for such patients may be necessary to acquire their brain conditions, which may benefit stratified management for them.https://www.frontiersin.org/articles/10.3389/fnins.2022.1071594/fullfunctional connectivityTMS-EEGunresponsive wakefulness syndromeglobal mean field amplitudegraph theory |
| spellingShingle | Chen Li Yong Wang Wende Li Wende Li Yi Yang Xiaoyu Xia Xiaoyu Xia Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales Frontiers in Neuroscience functional connectivity TMS-EEG unresponsive wakefulness syndrome global mean field amplitude graph theory |
| title | Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales |
| title_full | Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales |
| title_fullStr | Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales |
| title_full_unstemmed | Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales |
| title_short | Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales |
| title_sort | measure functional network and cortical excitability in post anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales |
| topic | functional connectivity TMS-EEG unresponsive wakefulness syndrome global mean field amplitude graph theory |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1071594/full |
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