Quantitative analysis of transcranial temporal interference stimulation in rodents: A simulation study on electrode configurations_Journal of Biomedical Engineering

Authors：

LIU Xiaoxi ^1,2,3 ,  YU Hongli ^1,2,3 , GOU Fushuai ^1,2,3 , DU Boai ^1,2,3 , LU Pengyi ^1,2,3 , WANG Chunfang ^4,5

1. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
2. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P. R. China;
3. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China;
4. Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin 300121, P. R. China;
5. Rehabilitation Medical Research Center of Tianjin, Tianjin 300121, P. R. China;

Corresponding author：

YU Hongli, Email: yhlzyn@hebut.edu.cn

Keywords：

Transcranial electrical stimulation; Temporal interference; Electrode configuration

DOI：

10.7507/1001-5515.202411054

Video：

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Transcranial temporal interference stimulation (tTIS) is a novel non-invasive transcranial electrical stimulation technique that achieves deep brain stimulation through multiple electrodes applying electric fields of different frequencies. Current studies on the mechanism of tTIS effects are primarily based on rodents, but experimental outcomes are often significantly influenced by electrode configurations. To enhance the performance of tTIS within the limited cranial space of rodents, we proposed various electrode configurations for tTIS and conducted finite element simulations using a realistic mouse model. Results demonstrated that ventral-dorsal, four-channel bipolar, and two-channel configurations performed best in terms of focality, diffusion of activated brain regions, and scalp impact, respectively. Compared to traditional transcranial direct current stimulation (tDCS), these configurations improved by 94.83%, 50.59%, and 3 514.58% in the respective evaluation metrics. This study provides a reference for selecting electrode configurations in future tTIS research on rodents.

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26.	Lee S, Park J, Lee C, et al. Multipair transcranial temporal interference stimulation for improved focalized stimulation of deep brain regions: A simulation study. Comput Biol Med, 2022, 143: 105337.
27.	Reato D, Rahman A, Bikson M, et al. Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing. J Neurosci, 2010, 30(45): 15067-15079.
28.	Nielsen J D, Madsen K H, Puonti O, et al. Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art. Neuroimage, 2018, 174: 587-598.
29.	Windhoff M, Opitz A, Thielscher A. Electric field calculations in brain stimulation based on finite elements: an optimized processing pipeline for the generation and usage of accurate individual head models. Hum Brain Mapp, 2013, 34(4): 923-935.

1. Steinmetz J D, Seeher K M, Schiess N, et al. Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol, 2024, 23(4): 344-381.
2. Naish K R, Vedelago L, MacKillop J, et al. Effects of neuromodulation on cognitive performance in individuals exhibiting addictive behaviors: A systematic review. Drug Alcohol Depend, 2018, 192: 338-351.
3. Bari A, DiCesare J, Babayan D, et al. Neuromodulation for substance addiction in human subjects: A review. Neurosci Biobehav Rev, 2018, 95: 33-43.
4. McGirr A, Berlim M T. Clinical usefulness of therapeutic neuromodulation for major depression: a systematic meta-review of recent meta-analyses. Psychiatr Clin North Am, 2018, 41(3): 485-503.
5. Knotkova H, Hamani C, Sivanesan E, et al. Neuromodulation for chronic pain. Lancet, 2021, 397(10289): 2111-2124.
6. Grossman N, Okun M S, Boyden E S. Translating temporal interference brain stimulation to treat neurological and psychiatric conditions. JAMA Neurol, 2018, 75(11): 1307-1308.
7. Hutcheon B, Yarom Y. Resonance, oscillation and the intrinsic frequency preferences of neurons. Trends Neurosci, 2000, 23(5): 216-222.
8. Grossman N, Bono D, Dedic N, et al. Noninvasive deep brain stimulation via temporally interfering electric fields. Cell, 2017, 169(6): 1029-1041.
9. Liu X, Qiu F, Hou L, et al. Review of noninvasive or minimally invasive deep brain stimulation. Front Behav Neurosci, 2022, 15: 820017.
10. Guo W, He Y, Zhang W, et al. A novel non-invasive brain stimulation technique: "temporally interfering electrical stimulation". Front Neurosci, 2023, 17: 1092539.
11. Cao J, Grover P. STIMULUS: noninvasive dynamic patterns of neurostimulation using spatio-temporal interference. IEEE Trans Biomed Eng, 2020, 67(3): 726-737.
12. Song X, Zhao X, Li X, et al. Multi-channel transcranial temporally interfering stimulation (tTIS): application to living mice brain. J Neural Eng, 2021, 18(3): 036003.
13. Huang Y, Parra L C. Can transcranial electric stimulation with multiple electrodes reach deep targets?. Brain Stimul, 2019, 12(1): 30-40.
14. Rampersad S, Roig-Solvas B, Yarossi M, et al. Prospects for transcranial temporal interference stimulation in humans: A computational study. Neuroimage, 2019, 202: 116124.
15. Lee S, Lee C, Park J, et al. Individually customized transcranial temporal interference stimulation for focused modulation of deep brain structures: a simulation study with different head models. Sci Rep, 2020, 10(1): 11730.
16. Huang Y, Datta A. Comparison of optimized interferential stimulation using two pairs of electrodes and two arrays of electrodes. Annu Int Conf IEEE Eng Med Biol Soc, 2021: 4180-4183.
17. Stoupis D, Samaras T. Non-invasive stimulation with temporal interference: optimization of the electric field deep in the brain with the use of a genetic algorithm. J Neural Eng, 2022, 19(5): 056018.
18. Dogdas B, Stout D, Chatziioannou A F, et al. Digimouse: a 3D whole body mouse atlas from ct and cryosection data. Phys Med Biol, 2007, 52(3): 577-587.
19. Wang Q, Ding S L, Li Y, et al. The allen mouse brain common coordinate framework: a 3D reference atlas. Cell, 2020, 181(4): 936-953.
20. Tran A P, Yan S, Fang Q. Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models. Neurophotonics, 2020, 7(1): 015008.
21. Barchanski1 A, De Gersem1 H, Gjonaj E, et al. Impact of the displacement current on low-frequency electromagnetic fields computed using high-resolution anatomy models. Phys Med Biol, 2005, 50(19): N243-N249.
22. Plonsey R, Heppner D B. Considerations of quasi-stationarity in electrophysiological systems. Bull Math Biophys, 1967, 29: 657-664.
23. Bossetti C A, Birdno M J, Grill W M. Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation. J Neural Eng, 2007, 5(1): 44-53.
24. Alekseichuk I, Mantell K, Shirinpour S, et al. Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human. Neuroimage, 2019, 194: 136-148.
25. Monai H, Ohkura M, Tanaka M, et al. Calcium imaging reveals glial involvement in transcranial direct current stimulation-induced plasticity in mouse brain. Nat Commun, 2016, 7(1): 11100.
26. Lee S, Park J, Lee C, et al. Multipair transcranial temporal interference stimulation for improved focalized stimulation of deep brain regions: A simulation study. Comput Biol Med, 2022, 143: 105337.
27. Reato D, Rahman A, Bikson M, et al. Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing. J Neurosci, 2010, 30(45): 15067-15079.
28. Nielsen J D, Madsen K H, Puonti O, et al. Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art. Neuroimage, 2018, 174: 587-598.
29. Windhoff M, Opitz A, Thielscher A. Electric field calculations in brain stimulation based on finite elements: an optimized processing pipeline for the generation and usage of accurate individual head models. Hum Brain Mapp, 2013, 34(4): 923-935.

Journal of Biomedical Engineering

Latest ArticlesQuantitative analysis of transcranial temporal interference stimulation in rodents: A simulation study on electrode configurations

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