Chunyun Zhang , Li Bie , Shuai Han , Dexiao Zhao , Peidong Li , Xinjun Wang , Bin Jiang , Yongkun Guo
{"title":"从静息状态脑电图的不同时空动态解码意识","authors":"Chunyun Zhang , Li Bie , Shuai Han , Dexiao Zhao , Peidong Li , Xinjun Wang , Bin Jiang , Yongkun Guo","doi":"10.1016/j.jnrt.2024.100095","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>Functional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states.</p></div><div><h3>Methods</h3><p>Resting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness.</p></div><div><h3>Results</h3><p>There was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3 %) was achieved.</p></div><div><h3>Conclusion</h3><p>Loss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.</p></div>","PeriodicalId":44709,"journal":{"name":"Journal of Neurorestoratology","volume":"12 1","pages":"Article 100095"},"PeriodicalIF":3.1000,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2324242624000020/pdfft?md5=6ba4764e4e80f3804a44bd03957d2cc1&pid=1-s2.0-S2324242624000020-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram\",\"authors\":\"Chunyun Zhang , Li Bie , Shuai Han , Dexiao Zhao , Peidong Li , Xinjun Wang , Bin Jiang , Yongkun Guo\",\"doi\":\"10.1016/j.jnrt.2024.100095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>Functional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states.</p></div><div><h3>Methods</h3><p>Resting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness.</p></div><div><h3>Results</h3><p>There was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3 %) was achieved.</p></div><div><h3>Conclusion</h3><p>Loss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.</p></div>\",\"PeriodicalId\":44709,\"journal\":{\"name\":\"Journal of Neurorestoratology\",\"volume\":\"12 1\",\"pages\":\"Article 100095\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2324242624000020/pdfft?md5=6ba4764e4e80f3804a44bd03957d2cc1&pid=1-s2.0-S2324242624000020-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neurorestoratology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2324242624000020\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neurorestoratology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2324242624000020","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram
Introduction
Functional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states.
Methods
Resting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness.
Results
There was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3 %) was achieved.
Conclusion
Loss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.