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PreprintCC BY-NC-NDbioRxiv

Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective

  1. 1School of Advanced Manufacturing, Nanchang University, Nanchang, China

Abstract

Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.

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BibTeX
@misc{chen2026fieldbased,
  title = {{Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective}},
  author = {T. Chen and C. Huo and G. Shao and Z. Cao and C. Li and J. Liu and Z. Li},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.16.752209},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.16.752209v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective
AU  - T. Chen
AU  - C. Huo
AU  - G. Shao
AU  - Z. Cao
AU  - C. Li
AU  - J. Liu
AU  - Z. Li
PY  - 2026
DA  - 2026/09/16
PB  - bioRxiv
DO  - 10.64898/2026.09.16.752209
UR  - https://www.biorxiv.org/content/10.64898/2026.09.16.752209v1
AB  - Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.
ER  -