Adaptive constrained independent vector analysis: An effective solution for analysis of large-scale medical imaging data.
Adaptive constrained independent vector analysis: An effective solution for analysis of large-scale medical imaging data.
复制标题
自适应约束独立矢量分析：用于分析大规模医学成像数据的有效解决方案。
DOI：
10.1109/jstsp.2020.3003891
复制
发表时间：
2020-10
影响因子：
7.5
通讯作者：
Adalı T
中科院分区：
文献类型：
--
作者：
Bhinge S;Long Q;Calhoun VD;Adalı T
越来越需要灵活的方法来分析大规模功能性磁共振成像（fMRI）数据，以估计概括人口的全球签名，同时保留个体特异性特征。独立向量分析（Independent Vector Analysis，IVA）是一种数据驱动的方法，能够从多受试者的fMRI数据中联合估计全局时空模式，并有效地保留受试者的变异性。然而，正如我们所展示的，当数据集和组件的数量增加时，IVA性能会受到负面影响，特别是当数据集之间的组件相关性较低时。我们研究了这个问题及其与数据集之间相关性的关系，并提出了一种有效的方法，通过将估计模式的参考信息纳入到制定中来解决这个问题，作为高维场景的指导。约束IVA（cIVA）提供了一个有效的框架，纳入参考，但其性能取决于用户定义的约束参数，这强制执行的参考信号和估计模式之间的关联到一个固定的水平。我们提出了自适应cIVA（acIVA），调整的约束参数，以允许灵活的参考和估计模式之间的关联，并能够将多个参考信号，而不强制执行不准确的条件。我们的研究结果表明，acIVA可以可靠地估计高维多变量源从大规模的模拟数据集，与标准IVA相比。它还成功地从一个大规模的fMRI数据集中提取有意义的功能网络，标准IVA没有收敛。该方法还有效地捕获特定于受试者的信息，这是通过观察到的频谱功率的性别差异，男性在低频率和女性在高频率下，在运动，注意力，视觉和默认模式网络内的较高的频谱功率来证明的。
There is a growing need for flexible methods for the analysis of large-scale functional magnetic resonance imaging (fMRI) data for the estimation of global signatures that summarize the population while preserving individual-specific traits. Independent vector analysis (IVA) is a data-driven method that jointly estimates global spatio-temporal patterns from multi-subject fMRI data, and effectively preserves subject variability. However, as we show, IVA performance is negatively affected when the number of datasets and components increases especially when there is low component correlation across the datasets. We study the problem and its relationship with respect to correlation across the datasets, and propose an effective method for addressing the issue by incorporating reference information of the estimation patterns into the formulation, as a guidance in high dimensional scenarios. Constrained IVA (cIVA) provides an efficient framework for incorporating references, however its performance depends on a user-defined constraint parameter, which enforces the association between the reference signals and estimation patterns to a fixed level. We propose adaptive cIVA (acIVA) that tunes the constraint parameter to allow flexible associations between the references and estimation patterns, and enables incorporating multiple reference signals, without enforcing inaccurate conditions. Our results indicate that acIVA can reliably estimate high-dimensional multivariate sources from large-scale simulated datasets, when compared with standard IVA. It also successfully extracts meaningful functional networks from a large-scale fMRI dataset for which standard IVA did not converge. The method also efficiently captures subject-specific information, which is demonstrated through observed gender differences in spectral power, higher spectral power in males at low frequencies and in females at high frequencies, within the motor, attention, visual and default mode networks.
登录
查看更多内容
量化多个数据集在融合中的相互作用和贡献：应用精神分裂症的检测。
影响因子：
10.6
作者：
Levin-Schwartz Y;Calhoun VD;Adali T
通讯作者：
Adali T
影响因子：
2.9
作者：
Rashid B;Damaraju E;Pearlson GD;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
3
作者：
Michael AM;Anderson M;Miller RL;Adalı T;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
3.1
作者：
Menon, Vinod;Uddin, Lucina Q.
通讯作者：
Uddin, Lucina Q.
影响因子：
17.6
作者：
Calhoun VD;Adalı T
通讯作者：
Adalı T