TY - JOUR
T1 - Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index
AU - Peraza, Luis R.
AU - Asghar, Aziz U. R.
AU - Green, Gary
AU - Halliday, David M.
PY - 2012/6/15
Y1 - 2012/6/15
N2 - In this paper, we test the performance of a synchronicity estimator widely applied in Neuroscience, phase lag index (PLI), for brain network inference in EEG. We implement the four sphere head model to simulate the volume conduction problem present in EEG recordings and measure the activity at the scalp of surrogate sources located at the brain level. Then, networks are estimated under the null hypothesis (independent sources) using PLI, coherence (R) and phase coherence (PC) for the volume conduction and no volume conduction (NVC) cases. It is known that R and PC are highly influenced by volume conduction, leading to the inference of clustered grid networks. PLI was designed to solve this problem. Our simulations show that PLI is partially invariant to volume conduction. The networks found by PLI show small-worldness, with a clustering coefficient higher than random networks. On the contrary, PLI-NVC obtains networks whose distribution is closer to random networks indicating that the high clustering shown by PLI networks are caused by volume conduction. The influence of volume conduction in PLI might lead to biased results in brain network inference from EEG if this behaviour is ignored. (C) 2012 Elsevier B.V. All rights reserved.
AB - In this paper, we test the performance of a synchronicity estimator widely applied in Neuroscience, phase lag index (PLI), for brain network inference in EEG. We implement the four sphere head model to simulate the volume conduction problem present in EEG recordings and measure the activity at the scalp of surrogate sources located at the brain level. Then, networks are estimated under the null hypothesis (independent sources) using PLI, coherence (R) and phase coherence (PC) for the volume conduction and no volume conduction (NVC) cases. It is known that R and PC are highly influenced by volume conduction, leading to the inference of clustered grid networks. PLI was designed to solve this problem. Our simulations show that PLI is partially invariant to volume conduction. The networks found by PLI show small-worldness, with a clustering coefficient higher than random networks. On the contrary, PLI-NVC obtains networks whose distribution is closer to random networks indicating that the high clustering shown by PLI networks are caused by volume conduction. The influence of volume conduction in PLI might lead to biased results in brain network inference from EEG if this behaviour is ignored. (C) 2012 Elsevier B.V. All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=84860817562&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2012.04.007
DO - 10.1016/j.jneumeth.2012.04.007
M3 - Article
SN - 0165-0270
VL - 207
SP - 189
EP - 199
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 2
ER -