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848 results on '"Haueisen J"'

5. Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines

Author

Antal, A., Alekseichuk, I., Bikson, M., Brockmöller, J., Brunoni, A.R., Chen, R., Cohen, L.G., Dowthwaite, G., Ellrich, J., Flöel, A., Fregni, F., George, M.S., Hamilton, R., Haueisen, J., Herrmann, C.S., Hummel, F.C., Lefaucheur, J.P., Liebetanz, D., Loo, C.K., McCaig, C.D., Miniussi, C., Miranda, P.C., Moliadze, V., Nitsche, M.A., Nowak, R., Padberg, F., Pascual-Leone, A., Poppendieck, W., Priori, A., Rossi, S., Rossini, P.M., Rothwell, J., Rueger, M.A., Ruffini, G., Schellhorn, K., Siebner, H.R., Ugawa, Y., Wexler, A., Ziemann, U., Hallett, M., and Paulus, W.

Published
2017
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6. An Improved GPU-Optimized Fictitious Surface Charge Method for Transcranial Magnetic Stimulation

Author

Muller, E., Petkovic, B., Ziolkowski, M., Weise, K., Toepfer, H., and Haueisen, J.

Abstract

The fictitious surface charge method (FSCM) is used for the calculation of the induced electrical field in magnetic stimulation. The method was embedded and optimized in Python. It was designed to allow for the computation of large problems. An element-wise Jacobi method was combined with vectorized matrix operations to increase the parallelization capabilities and enable GPU computing. The induced fields are compared against an analytical solution for a homogeneous sphere and an FEM solution on a realistic head model. The results for both cases show that the normalized root mean square error (NRMSE) of less than ${0.5}{\%}$ can be achieved with the integral-free FSCM even on low-performance computer hardware.

Published
2024
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11. Modeling the Effects of Transcranial Magnetic Stimulation on Spatial Attention

Author

Jing, Y., Numssen, O., https://orcid.org/0000-0001-7164-2682, Weise, K., https://orcid.org/0000-0003-1283-2042, Kalloch, B., https://orcid.org/0000-0002-9856-1230, Buchberger, L., Haueisen, J., Hartwigsen, G., https://orcid.org/0000-0002-8084-1330, Knösche, T., and https://orcid.org/0000-0001-9668-3261

Abstract

Objectives: Transcranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure-function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes. Approach: Thirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation. Main results: We did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome. Significance: This study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.

Published
2023

37. Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Author

Ng, CR, Fiedler, P, Kuhlmann, L, Liley, D, Vasconcelos, B, Fonseca, C, Tamburro, G, Comani, S, Lui, TK-Y, Tse, C-Y, Warsito, IF, Supriyanto, E, Haueisen, J, Ng, CR, Fiedler, P, Kuhlmann, L, Liley, D, Vasconcelos, B, Fonseca, C, Tamburro, G, Comani, S, Lui, TK-Y, Tse, C-Y, Warsito, IF, Supriyanto, E, and Haueisen, J

Abstract

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Published
2022

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