Abstract：
: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.
journal_name
Neurosci Resjournal_title
Neuroscience researchauthors
Ye S,Kitajo K,Kitano Kdoi
10.1016/j.neures.2019.09.003subject
Has Abstractpub_date
2020-07-01 00:00:00pages
197-205eissn
0168-0102issn
1872-8111pii
S0168-0102(19)30522-Xjournal_volume
156pub_type
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