PMID- 31526850
OWN - NLM
STAT- MEDLINE
DCOM- 20210514
LR  - 20210514
IS  - 1872-8111 (Electronic)
IS  - 0168-0102 (Linking)
VI  - 156
DP  - 2020 Jul
TI  - Information-theoretic approach to detect directional information flow in EEG 
      signals induced by TMS.
PG  - 197-205
LID - S0168-0102(19)30522-X [pii]
LID - 10.1016/j.neures.2019.09.003 [doi]
AB  - Effective connectivity analysis has been widely applied to noninvasive recordings 
      such as functional magnetic resonance imaging and electroencephalograms (EEGs). 
      Previous studies have aimed to extract the causal relations between brain 
      regions, but the validity of the derived connectivity has not yet been fully 
      determined. This is because it is generally difficult to identify causality in 
      the usual experimental framework based on observations alone. Transcranial 
      magnetic stimulation (TMS) provides a framework in which a controllable 
      perturbation is applied to a local brain region and the effect is examined by 
      comparing the neural activity with and without this stimulation. This study 
      evaluates two methods for effective connectivity analysis, symbolic transfer 
      entropy (STE) and vector autoregression (VAR), by applying them to TMS-EEG data. 
      In terms of the consistency of results from different experimental sessions, STE 
      is found to yield robust results irrespective of sessions, whereas VAR produces 
      less correlation between sessions. Furthermore, STE preferentially detects the 
      directional information flow from the TMS target. Taken together, our results 
      suggest that STE is a reliable method for detecting the effect of TMS, implying 
      that it would also be useful for identifying neural activity during cognitive 
      tasks and resting states.
CI  - Copyright (c) 2019 The Authors. Published by Elsevier B.V. All rights reserved.
FAU - Ye, Song
AU  - Ye S
AD  - Graduate School of Information Science and Engineering, Ritsumeikan University, 
      Japan.
FAU - Kitajo, Keiichi
AU  - Kitajo K
AD  - CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Japan; Division 
      of Neural Dynamics, National Institutes for Physiological Sciences, National 
      Institutes of Natural Sciences, Japan; Department of Physiological Sciences, 
      School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), 
      Japan.
FAU - Kitano, Katsunori
AU  - Kitano K
AD  - Department of Information Science and Engineering, Ritsumeikan University, Japan. 
      Electronic address: kkt23219@is.ritsumei.ac.jp.
LA  - eng
PT  - Journal Article
DEP - 20190914
PL  - Ireland
TA  - Neurosci Res
JT  - Neuroscience research
JID - 8500749
SB  - IM
MH  - Brain
MH  - *Brain Mapping
MH  - Electroencephalography
MH  - Magnetic Resonance Imaging
MH  - *Transcranial Magnetic Stimulation
OTO - NOTNLM
OT  - Causal relation
OT  - Effective connectivity
OT  - Electroencephalogram (EEG)
OT  - Granger causality
OT  - Transcranial magnetic stimulation (TMS)
OT  - Transfer entropy
EDAT- 2019/09/19 06:00
MHDA- 2021/05/15 06:00
CRDT- 2019/09/19 06:00
PHST- 2019/07/22 00:00 [received]
PHST- 2019/08/31 00:00 [revised]
PHST- 2019/09/07 00:00 [accepted]
PHST- 2019/09/19 06:00 [pubmed]
PHST- 2021/05/15 06:00 [medline]
PHST- 2019/09/19 06:00 [entrez]
AID - S0168-0102(19)30522-X [pii]
AID - 10.1016/j.neures.2019.09.003 [doi]
PST - ppublish
SO  - Neurosci Res. 2020 Jul;156:197-205. doi: 10.1016/j.neures.2019.09.003. Epub 2019 
      Sep 14.