Sina Khanmohammadi, Terrance T Kummer, ShiNung Ching
{"title":"Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury.","authors":"Sina Khanmohammadi, Terrance T Kummer, ShiNung Ching","doi":"10.1109/ACSSC.2017.8335197","DOIUrl":null,"url":null,"abstract":"<p><p>Recent studies suggest that disruptions in resting state functional connectivity - a measure of stationary statistical association between brain regions - can be used as an objective marker of brain injury. However, fewer characterizations have examined the disruption of intrinsic brain dynamics after brain injury. Here, we examine this issue using electroencephalographic (EEG) data from brain-injured patients, together with a control analysis wherein we quantify the effect of the injury on the ability of intrinsic event responses to traverse their respective state spaces. More specifically, the lability of intrinsically evoked brain activity was assessed by collapsing three sigma event responses in all channels of the obtained EEG signals into a low-dimensional space. The directional derivative of these responses was then used to assay the extent to which brain activity reaches low-variance subspaces. Our findings suggest that intrinsic dynamics extracted from resting state EEG signals can differentiate various levels of consciousness in severe cases of coma. More specifically the cost of moving from one state to another in the state-space trajectories of the underlying dynamics becomes lower as the level of consciousness of patients deteriorates.</p>","PeriodicalId":72692,"journal":{"name":"Conference record. Asilomar Conference on Signals, Systems & Computers","volume":"2017 ","pages":"344-348"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ACSSC.2017.8335197","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference record. Asilomar Conference on Signals, Systems & Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2017.8335197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/4/16 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Recent studies suggest that disruptions in resting state functional connectivity - a measure of stationary statistical association between brain regions - can be used as an objective marker of brain injury. However, fewer characterizations have examined the disruption of intrinsic brain dynamics after brain injury. Here, we examine this issue using electroencephalographic (EEG) data from brain-injured patients, together with a control analysis wherein we quantify the effect of the injury on the ability of intrinsic event responses to traverse their respective state spaces. More specifically, the lability of intrinsically evoked brain activity was assessed by collapsing three sigma event responses in all channels of the obtained EEG signals into a low-dimensional space. The directional derivative of these responses was then used to assay the extent to which brain activity reaches low-variance subspaces. Our findings suggest that intrinsic dynamics extracted from resting state EEG signals can differentiate various levels of consciousness in severe cases of coma. More specifically the cost of moving from one state to another in the state-space trajectories of the underlying dynamics becomes lower as the level of consciousness of patients deteriorates.