论文标题
共享的神经编码模型，用于预测主题特异性fMRI响应
A shared neural encoding model for the prediction of subject-specific fMRI response
论文作者
论文摘要
自然主义范式在fMRI中的普及（例如电影观看）需要多种主体数据分析的新策略，例如使用神经编码模型。在本研究中，我们提出了一种共同的卷积神经编码方法，该方法解释了个人级别的差异。我们的方法利用多主体数据来改善视觉或听觉刺激引起的特定主题反应的预测。我们展示了来自人类Connectome项目电影观察协议的高分辨率7T fMRI数据的方法，并显示了对单人物编码模型的显着改进。我们进一步证明了共享编码模型成功捕获有意义的个体差异，以响应传统的基于任务的面部和场景刺激的能力。综上所述，我们的发现表明，受试者间知识转移可能对特定于主题的预测模型有益。
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models. In the present study, we propose a shared convolutional neural encoding method that accounts for individual-level differences. Our method leverages multi-subject data to improve the prediction of subject-specific responses evoked by visual or auditory stimuli. We showcase our approach on high-resolution 7T fMRI data from the Human Connectome Project movie-watching protocol and demonstrate significant improvement over single-subject encoding models. We further demonstrate the ability of the shared encoding model to successfully capture meaningful individual differences in response to traditional task-based facial and scenes stimuli. Taken together, our findings suggest that inter-subject knowledge transfer can be beneficial to subject-specific predictive models.
