Mixtures of Dirichlet processes for joint spatial modelling of transcranial magnetic stimulation mapping data
研究开发了一种狄利克雷过程混合模型，用于区分经颅磁刺激（TMS）数据中患者特异性和共享的空间模式，帮助识别一致的反应区域，为个性化治疗提供依据。
Abstract A patient’s responses to Transcranial Magnetic Stimulation (TMS) pulses on the motor cortex have a complex spatial pattern, making it challenging to understand the response patterns across multiple patients. We developed a mixture of Dirichlet process models to distinguish between patient-specific and shared spatial patterns across multiple patients to provide insight into consistent response patterns essential for developing personalized treatment procedures. The Metropolis–Hastings within Gibbs sampler of the Markov Chain Monte Carlo algorithm was developed for estimation. The model was used to analyse the TMS data of 3 healthy subjects. The study revealed that the primary motor cortex of the hand consistently emerges as a promising region for eliciting optimal responses. This area serves as a key target for brain mapping using TMS to identify cortical hotspots. However, the excitability patterns in this region can vary significantly among patients.