论文标题
RS-FMRI的贝叶斯复发状态空间模型
Bayesian recurrent state space model for rs-fMRI
论文作者
论文摘要
我们提出了一个分层贝叶斯经常性状态空间模型，用于在静止状态fMRI数据中建模切换网络连接。我们的模型使我们能够在疾病条件下发现共享的网络模式。我们通过推断出与轻度认知障碍（MCI）的个体的神经回路改变的潜在状态模式（MCI）来评估ADNI2数据集上的方法。除了在健康和MCI中共享的国家外，我们还发现了在MCI患者中主要观察到的潜在状态。我们的模型优于ADNI2数据集上艺术深度学习方法的当前状态。
We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network patterns across disease conditions. We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI). In addition to states shared across healthy and individuals with MCI, we discover latent states that are predominantly observed in individuals with MCI. Our model outperforms current state of the art deep learning method on ADNI2 dataset.
