Waveform Optimization and Analysis for Enhancing Transcranial Magnetic Stimulation Selectivity Article Swipe
Ziqi Zhang
,
Hongfa Ding
,
Zhou He
,
S. S. Yu
,
Xiao Fang
,
Chuang Zhao
,
Dandi Zhang
,
Yingzhe Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3618966
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3618966
Transcranial magnetic stimulation (TMS) is a noninvasive technique that stimulates the brain via electric fields induced by pulsed currents in a coil. Beyond coil design, the pulse waveform also affects neuronal activation, though specific mechanisms remain unclear. A waveform optimization method is presented to improve stimulation selectivity, in which a multiscale model with realistic neuronal morphology quantifies the selectivity index, the stimulation waveform is parameterized for a pulse parameter controllable TMS discharge circuit, and particle swarm optimization is applied to refine the waveform parameters. Optimized waveforms show the potential to achieve higher selectivity than monophasic waveforms, which are known for relatively high selectivity. The results further show that waveform polarity, defined as the ratio of the positive to negative integrated areas of the stimulation waveform, plays a key role in selectivity across cortical layers. Other waveform parameters, such as the relative order and amplitude of induced electric field levels, may also affect selectivity, with their influence varying according to neuronal morphology and the local electric field distribution. The neuronal time constant also influences how waveform parameters affect stimulation selectivity. Furthermore, stronger polarity-selectivity correlations are often associated with greater alignment between selectivity and coil heating. The proposed method can enhance TMS selectivity by optimizing stimulation waveforms. The identified patterns may help explain variability in TMS outcomes across different waveforms and between individuals, and support the development of personalized designs that enhance stimulation efficacy while minimizing coil heating.
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- OA Status
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- Title
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Waveform Optimization and Analysis for Enhancing Transcranial Magnetic Stimulation SelectivityWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Ziqi Zhang, Hongfa Ding, Zhou He, S. S. Yu, Xiao Fang, Chuang Zhao, Dandi Zhang, Yingzhe LiuList of authors in order
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https://doi.org/10.1109/access.2025.3618966Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3618966Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.efficacy | 231 |
| abstract_inverted_index.electric | 13, 146, 164 |
| abstract_inverted_index.heating. | 193, 235 |
| abstract_inverted_index.magnetic | 1 |
| abstract_inverted_index.negative | 118 |
| abstract_inverted_index.neuronal | 30, 54, 159, 168 |
| abstract_inverted_index.outcomes | 214 |
| abstract_inverted_index.particle | 74 |
| abstract_inverted_index.patterns | 207 |
| abstract_inverted_index.positive | 116 |
| abstract_inverted_index.proposed | 195 |
| abstract_inverted_index.relative | 140 |
| abstract_inverted_index.specific | 33 |
| abstract_inverted_index.stronger | 180 |
| abstract_inverted_index.unclear. | 36 |
| abstract_inverted_index.waveform | 27, 38, 62, 82, 108, 135, 174 |
| abstract_inverted_index.Optimized | 84 |
| abstract_inverted_index.according | 157 |
| abstract_inverted_index.alignment | 188 |
| abstract_inverted_index.amplitude | 143 |
| abstract_inverted_index.different | 216 |
| abstract_inverted_index.discharge | 71 |
| abstract_inverted_index.influence | 155 |
| abstract_inverted_index.parameter | 68 |
| abstract_inverted_index.polarity, | 109 |
| abstract_inverted_index.potential | 88 |
| abstract_inverted_index.presented | 42 |
| abstract_inverted_index.realistic | 53 |
| abstract_inverted_index.technique | 7 |
| abstract_inverted_index.waveform, | 124 |
| abstract_inverted_index.waveforms | 85, 217 |
| abstract_inverted_index.associated | 185 |
| abstract_inverted_index.identified | 206 |
| abstract_inverted_index.influences | 172 |
| abstract_inverted_index.integrated | 119 |
| abstract_inverted_index.mechanisms | 34 |
| abstract_inverted_index.minimizing | 233 |
| abstract_inverted_index.monophasic | 94 |
| abstract_inverted_index.morphology | 55, 160 |
| abstract_inverted_index.multiscale | 50 |
| abstract_inverted_index.optimizing | 202 |
| abstract_inverted_index.parameters | 175 |
| abstract_inverted_index.quantifies | 56 |
| abstract_inverted_index.relatively | 100 |
| abstract_inverted_index.stimulates | 9 |
| abstract_inverted_index.waveforms, | 95 |
| abstract_inverted_index.waveforms. | 204 |
| abstract_inverted_index.activation, | 31 |
| abstract_inverted_index.development | 224 |
| abstract_inverted_index.noninvasive | 6 |
| abstract_inverted_index.parameters, | 136 |
| abstract_inverted_index.parameters. | 83 |
| abstract_inverted_index.selectivity | 58, 92, 130, 190, 200 |
| abstract_inverted_index.stimulation | 2, 45, 61, 123, 177, 203, 230 |
| abstract_inverted_index.variability | 211 |
| abstract_inverted_index.Furthermore, | 179 |
| abstract_inverted_index.Transcranial | 0 |
| abstract_inverted_index.controllable | 69 |
| abstract_inverted_index.correlations | 182 |
| abstract_inverted_index.individuals, | 220 |
| abstract_inverted_index.optimization | 39, 76 |
| abstract_inverted_index.personalized | 226 |
| abstract_inverted_index.selectivity, | 46, 152 |
| abstract_inverted_index.selectivity. | 102, 178 |
| abstract_inverted_index.distribution. | 166 |
| abstract_inverted_index.parameterized | 64 |
| abstract_inverted_index.polarity-selectivity | 181 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.57309942 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |