可扩展且稳健的自发立体脑电图数据张量分解。
Scalable and Robust Tensor Decomposition of Spontaneous Stereotactic EEG Data.
出版信息
IEEE Trans Biomed Eng. 2019 Jun;66(6):1549-1558. doi: 10.1109/TBME.2018.2875467. Epub 2018 Oct 11.
OBJECTIVE
Identification of networks from resting brain signals is an important step in understanding the dynamics of spontaneous brain activity. We approach this problem using a tensor-based model.
METHODS
We develop a rank-recursive scalable and robust sequential canonical polyadic decomposition (SRSCPD) framework to decompose a tensor into several rank-1 components. Robustness and scalability are achieved using a warm start for each rank based on the results from the previous rank.
RESULTS
In simulations we show that SRSCPD consistently outperforms the multi-start alternating least square (ALS) algorithm over a range of ranks and signal-to-noise ratios (SNRs), with lower computation cost. When applying SRSCPD to resting in-vivo stereotactic EEG (SEEG) data from two subjects with epilepsy, we found components corresponding to default mode and motor networks in both subjects. These components were also highly consistent within subject between two sessions recorded several hours apart. Similar components were not obtained using the conventional ALS algorithm.
CONCLUSION
Consistent brain networks and their dynamic behaviors were identified from resting SEEG data using SRSCPD.
SIGNIFICANCE
SRSCPD is scalable to large datasets and therefore a promising tool for identification of brain networks in long recordings from single subjects.
目的
从静息态脑信号中识别网络是理解自发脑活动动力学的重要步骤。我们使用基于张量的模型来解决这个问题。
方法
我们开发了一种基于秩递归可扩展和鲁棒序贯典型多胞分解（SRSCPD）框架的方法，将张量分解为几个秩-1 分量。通过基于前一阶的结果为每一阶进行热启动，实现了稳健性和可扩展性。
结果
在模拟中，我们表明 SRSCPD 在一系列阶数和信噪比（SNR）下始终优于多起点交替最小二乘法（ALS）算法，计算成本更低。当将 SRSCPD 应用于来自两名癫痫患者的静息立体脑电图（SEEG）数据时，我们在两名患者中均发现了与默认模式和运动网络对应的成分。这些成分在两次记录之间相隔数小时的两个会话中在受试者内也非常一致。使用传统的 ALS 算法则无法获得类似的成分。
结论
使用 SRSCPD 可以从静息 SEEG 数据中识别出一致的大脑网络及其动态行为。
意义
SRSCPD 可扩展到大型数据集，因此是从单个受试者的长记录中识别大脑网络的有前途的工具。
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