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High-Resolution Modeling Assisted Design of Customized and Individualized Transcranial Direct Current Stimulation Protocols
Article first published online: 10 JUL 2012
© 2012 International Neuromodulation Society
Neuromodulation: Technology at the Neural Interface
Volume 15, Issue 4, pages 306–315, July/August 2012
How to Cite
Bikson, M., Rahman, A., Datta, A., Fregni, F. and Merabet, L. (2012), High-Resolution Modeling Assisted Design of Customized and Individualized Transcranial Direct Current Stimulation Protocols. Neuromodulation: Technology at the Neural Interface, 15: 306–315. doi: 10.1111/j.1525-1403.2012.00481.x
Conflict of Interest and Sources of Funding Statement: Dr. Datta is cofounder of Soterix Medical. The City University of New York has patent applications in Dr. Datta's name on brain stimulation. Dr. Bikson is funded by the National Institutes of Health (nos. S06 GM008168 NS054783, CRCNS 41771), the Andy Grove Foundation, and the Wallace H Coulter Foundation. The City University of New York has patent applications in Dr. Bikson's name on brain stimulation. Dr. Bikson is cofounder of Soterix Medical.
- Issue published online: 6 AUG 2012
- Article first published online: 10 JUL 2012
- Received: October 10, 2011 Revised: April 12, 2012 Accepted: May 23, 2012
- Electrical stimulation;
- head model;
- skull defects;
- traumatic brain injury
Objectives: Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that delivers low-intensity currents facilitating or inhibiting spontaneous neuronal activity. tDCS is attractive since dose is readily adjustable by simply changing electrode number, position, size, shape, and current. In the recent past, computational models have been developed with increased precision with the goal to help customize tDCS dose. The aim of this review is to discuss the incorporation of high-resolution patient-specific computer modeling to guide and optimize tDCS.
Methods: In this review, we discuss the following topics: 1) The clinical motivation and rationale for models of transcranial stimulation is considered pivotal in order to leverage the flexibility of neuromodulation; 2) the protocols and the workflow for developing high-resolution models; 3) the technical challenges and limitations of interpreting modeling predictions; and 4) real cases merging modeling and clinical data illustrating the impact of computational models on the rational design of rehabilitative electrotherapy.
Conclusions: Though modeling for noninvasive brain stimulation is still in its development phase, it is predicted that with increased validation, dissemination, simplification, and democratization of modeling tools, computational forward models of neuromodulation will become useful tools to guide the optimization of clinical electrotherapy.