Anatomically mediated variability of hippocampal electric fields in temporal interference stimulation: A predictive...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Modeling

Background:

• Temporal interference stimulation (TIS) offers noninvasive deep brain targeting.
• Individual variability in TIS-induced electric fields (EF), including modulation depth (MD) and directional alignment, is not well understood.

Purpose of the Study:

• To investigate individual variability in TIS-induced EF in the left hippocampus (LHippo).
• To identify anatomical predictors of MD variability and their relationship with demographic and cognitive factors.
• To develop a predictive model for MD based on anatomical features.

Main Methods:

• MRI-guided finite element modeling simulated TIS-induced EF in the LHippo.
• Modulation depth (MD) and EF vector angle were quantified across individuals stratified by age, sex, and cognitive status.
• LASSO and stepwise regression identified anatomical features predicting MD; mediation analysis assessed their role.

Main Results:

• MD varied significantly with age, sex, and cognitive status; directional angle was stable.
• A predictive model using eight anatomical features (e.g., skull thickness, CSF-to-head ratio) showed strong generalizability.
• Mediation analysis confirmed that predicted MD significantly accounted for age, sex, and cognitive status effects.

Conclusions:

• Anatomical variability critically influences individual TIS outcomes.
• Personalized MD can be estimated using clinical features, bypassing the need for subject-specific simulations.
• This framework supports scalable, interpretable strategies for precision brain stimulation.