TY - GEN
T1 - Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury
AU - Khanmohammadi, Sina
AU - Kummer, Terranee T.
AU - Ching, Shinung
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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 electroencephalo-graphic (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.
AB - 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 electroencephalo-graphic (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.
UR - http://www.scopus.com/inward/record.url?scp=85050974464&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2017.8335197
DO - 10.1109/ACSSC.2017.8335197
M3 - Conference contribution
C2 - 31896930
AN - SCOPUS:85050974464
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 344
EP - 348
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -