Authors
Chen, T., Huo, C., Shao, G., Cao, Z., Li, C., Liu, J., Li, Z.
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.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 23 Sep 2026.
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