Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data

被引：4

作者：

Zhou, Lixing ^{[1
]}

Takane, Yoshio ^{[1
]}

Hwang, Heungsun ^{[1
]}

机构：

[1] McGill Univ, Montreal, PQ H3A 2T5, Canada

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2016年 / 101卷

关键词：

Dynamic generalized structured component analysis (Dynamic GSCA); Structural equation modeling (SEM); Longitudinal and time series data; Alternating least squares (ALS) algorithm; fMRI (functional magnetic resonance imaging) data; Brain connectivity; COMPONENT ANALYSIS; NETWORK; AREA;

D O I：

10.1016/j.csda.2016.03.001

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Effective connectivity in functional neuroimaging studies is defined as the time dependent causal influence that a certain brain region of interest (ROI) exerts on another. A new method of structural equation modeling (SEM) is proposed for analyzing common patterns among multiple subjects' effective connectivity. The proposed method, called Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) incorporates contemporaneous and lagged effects between ROIs, direct and modulating effects of stimuli, as well as interaction effects among ROIs. An alternating least squares (ALS) algorithm is developed for estimating parameters. Synthetic and real data are analyzed to demonstrate the feasibility and usefulness of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：93 / 109

页数：17

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