Sparse representation of whole-brain fMRI signals for identification of functional networks
Sparse representation of whole-brain fMRI signals for identification of functional networks
复制标题
用于识别功能网络的全脑 fMRI 信号的稀疏表示
DOI：
10.1016/j.media.2014.10.011
复制
发表时间：
2015-02-01
影响因子：
10.9
通讯作者：
Liu, Tianming
中科院分区：
文献类型：
--
作者：
Lv, Jinglei;Jiang, Xi;Liu, Tianming
最近有几项研究基于每个体素的 fMRI 信号由稀疏分量线性组成的假设，使用稀疏表示进行 fMRI 信号分析和激活检测。先前的研究已经采用稀疏编码来对各种模式和规模的功能网络进行建模。这些先前的贡献激发了对是否/如何使用稀疏表示以体素方式和在整个大脑规模上识别功能网络的探索。本文提出了一种新颖的替代方法，通过基于全脑任务的功能磁共振成像信号的稀疏表示来识别多个功能网络。我们的基本思想是，将一个受试者全脑内的所有功能磁共振成像信号聚合成一个大数据矩阵，然后通过有效的在线词典学习算法将其分解为超完备词典基础矩阵和参考权重矩阵。我们广泛的实验结果表明，这种新颖的方法可以揭示多个功能网络，这些网络可以根据当前的脑科学知识在空间、时间和频率域中得到很好的表征和解释。重要的是，这些特征明确的功能网络组件在不同的大脑中具有相当的可重复性。总的来说，我们的方法为多种功能磁共振成像数据分析任务提供了一种新颖、有效和统一的解决方案，包括激活检测、去激活检测和功能网络识别。 (C) 2014 Elsevier B.V. 保留所有权利。
There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. (C) 2014 Elsevier B.V. All rights reserved.