建立聯結體預測模型於缺血性中風附件之評估與預後

Stroke is one of the major diseases in Taiwan, and rehabilitation after a stroke is very important. Effective rehabilitation strategies can increase the possibility of stroke patients' recovery. Therefore, it is important to conduct an accurate prediction for making a good treatment decision after stroke. Many neuroimaging studies of individual differences have focused on establishing correlational relationships between brain measurements and traits such as intelligence, memory, and attention, or disease symptoms. Through the advancement of technology and a large amount of collected data, a model can be established to predict the rehabilitation status of stroke patients by brain correlation obtained from neuroimaging. We will present Connectome-based predictive modeling in this research. This method is mainly divided into four parts: feature selection, feature summarization, model building, and assessment of prediction significance. We will use fMRI and rehabilitation scale parameters of stroke patients to complete the establishment of the CPM model, in order to facilitate the improvement and adjustment of rehabilitation strategies in the future.