Comparing the performance of beamformer algorithms in estimating orientations of neural sources

Yvonne Buschermöhle; Malte B Höltershinken; Tim Erdbrügger; Jan-Ole Radecke; Andreas Sprenger; Till R Schneider; Rebekka Lencer; Joachim Gross; Carsten H Wolters

doi:10.1016/j.isci.2024.109150

Comparing the performance of beamformer algorithms in estimating orientations of neural sources

iScience. 2024 Feb 6;27(3):109150. doi: 10.1016/j.isci.2024.109150. eCollection 2024 Mar 15.

Authors

Yvonne Buschermöhle^{1

2}, Malte B Höltershinken^{1

3}, Tim Erdbrügger^{1

3}, Jan-Ole Radecke^{4

5}, Andreas Sprenger^{5

6

7}, Till R Schneider⁸, Rebekka Lencer^{2

4

5

9}, Joachim Gross^{1

2}, Carsten H Wolters^{1

2}

Affiliations

¹ Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany.
² Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany.
³ Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany.
⁴ Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany.
⁵ Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany.
⁶ Department of Neurology, University of Lübeck, 23562 Lübeck, Germany.
⁷ Institute of Psychology II, University of Lübeck, 23562 Lübeck, Germany.
⁸ Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany.
⁹ Institute of Translational Psychiatry, University of Münster, 48149 Münster, Germany.

Abstract

The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking. For fixed brain locations, EEG and MEG data from sources with randomized orientations were simulated. The orientation was then estimated (1) with an EEG and (2) with a combined EEG-MEG approach. Three commonly used beamformer algorithms were evaluated with respect to their abilities to estimate the correct orientation: Unit-Gain (UG), Unit-Noise-Gain (UNG), and Array-Gain (AG) beamformer. Performance depends on the signal-to-noise ratios for the modalities and on the chosen beamformer. Overall, the UNG and AG beamformers appear as the most reliable. With increasing noise, the UG estimate converges to a vector determined by the leadfield, thus leading to insufficient orientation estimates.

Keywords: Neuroscience; Sensory neuroscience.