Prediction of hippocampal electric field in time series induced by TI-DMS with temporal convolutional network

• Published in: Cognitive neurodynamics Vol. 18; no. 4; pp. 2031 - 2045
• Main Authors: Xu, Xiangyang, Deng, Bin, Wang, Jiang, Yi, Guosheng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2024; Springer Nature B.V
• Subjects: Accuracy; Alzheimer's disease; Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Cognitive ability; Cognitive Psychology; Computer Science; Electric fields; Finite element method; Hippocampus; Magnetic fields; Mathematical models; Neural networks; Neurosciences; Parameters; Partial differential equations; Predictions; Research Article; Rhythms; Time series; Transcranial magnetic stimulation; Finite element method; Electric field; Temporal convolutional network; Temporal interference deep-brain magnetic stimulation
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-024-10067-3

Temporal interference deep-brain magnetic stimulation (TI-DMS) induces rhythmic electric field (EF) in the hippocampus to normalize cognitive function. The rhythmic time series of the hippocampal EF is essential for the assessment of TI-DMS. However, the finite element method (FEM) takes several hours to obtain the time series of EF. In order to reduce the time cost, the temporal convolutional network (TCN) model is adopted to predict the time series of hippocampal EF induced by TI-DMS. It takes coil configuration and loaded current as input and predicts the time series of maximum and mean values of the left and right hippocampal EF. The prediction takes only a few seconds. The model parameter combination of kernel size and layers is selected optimally by cross-validation method. The experimental results for multiple subjects show that the R 2 of all the time series predicted by the model exceed 0.98. And the prediction accuracy is even higher as the input parameters approach the training set. These results demonstrate that the adopted model can quickly predict the time series of hippocampal EF induced by TI-DMS with relatively high accuracy, which is beneficial for future clinical applications.