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Optimizing Stimulus Patterns for Dense Array tDCS With Fewer Sources Than Electrodes Using A branch and Bound

Seyhmus Guler1,2, Moritz Dannhauer1,2,3, Burak Erem4

  • 1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|May 9, 2017
PubMed
Summary
This summary is machine-generated.

Dense array transcranial direct current stimulation (tDCS) can be optimized using fewer current sources. A novel branch and bound algorithm efficiently finds effective, less costly stimulation patterns for brain modulation.

Keywords:
branch and bounddense arrayfocalityoptimizationtDCS

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Dense array transcranial direct current stimulation (tDCS) is a noninvasive brain modulation technique.
  • Optimizing current flow to specific brain regions (ROIs) is crucial for tDCS efficacy.
  • Existing optimization methods often require numerous individually controlled current sources, which is clinically impractical and costly.

Purpose of the Study:

  • To develop an efficient optimization method for tDCS current patterns using fewer current sources than electrodes.
  • To address the non-convex combinatorial optimization problem arising from reduced current source control.
  • To evaluate the effectiveness of the proposed method for both focal and extended cortical ROIs.

Main Methods:

  • Introduction of a novel application of the branch and bound (BB) algorithm for optimizing tDCS current patterns.
  • Simulation of current injection patterns for focal and spatially extended cortical regions of interest.
  • Comparison of BB algorithm results against optimization using a full set of current sources.

Main Results:

  • The branch and bound algorithm successfully identified sub-optimal stimulus patterns using significantly fewer current sources (2-3) compared to a full set (125 sources).
  • Achieved comparable stimulation results to the full set, within a 5% tolerance.
  • Demonstrated computational efficiency, being 3-5 orders of magnitude less demanding than exhaustive search methods.

Conclusions:

  • The branch and bound algorithm offers a computationally efficient and practical solution for optimizing dense array tDCS.
  • Fewer independently controlled current sources can achieve effective brain modulation, reducing clinical costs and complexity.
  • This approach facilitates the wider clinical application of advanced tDCS techniques.