On the importance of precise electrode placement for targeted transcranial electric stimulation
Transcranial electric stimulation (TES) is an increasingly popular method for non-invasive modulation of brain activity and a potential treatment for neuropsychiatric disorders. However, there are concerns about the reliability of its application because of variability in TES-induced intracranial el...
Saved in:
Published in | NeuroImage (Orlando, Fla.) Vol. 181; pp. 560 - 567 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.11.2018
Elsevier Limited |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Transcranial electric stimulation (TES) is an increasingly popular method for non-invasive modulation of brain activity and a potential treatment for neuropsychiatric disorders. However, there are concerns about the reliability of its application because of variability in TES-induced intracranial electric fields across individuals. While realistic computational models offer can help to alleviate these concerns, their direct empirical validation is sparse, and their practical implications are not always clear. In this study, we combine direct intracranial measurements of electric fields generated by TES in surgical epilepsy patients with computational modeling. First, we directly validate the computational models and identify key parameters needed for accurate model predictions. Second, we derive practical guidelines for a reliable application of TES in terms of the precision of electrode placement needed to achieve a desired electric field distribution. Based on our results, we recommend electrode placement accuracy to be < 1 cm for a reliable application of TES across sessions.
•Validation of computational models for TES•Investigation of electrode placement accuracy in 25 head models•Recommendation for electrode placements guidelines for TES |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 1053-8119 1095-9572 1095-9572 |
DOI: | 10.1016/j.neuroimage.2018.07.027 |