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

共 50 条

[31] Robust estimation in canonical correlation analysis for multivariate functional data
Krzysko, Miroslaw
Smaga, Lukasz
HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, 2019, 48 (02): : 521 - 535
[32] Novel nonparametric approach to canonical correlation analysis with applications to low CNR functional MRI data
Nandy, RR
Cordes, D
MAGNETIC RESONANCE IN MEDICINE, 2003, 50 (02) : 354 - 365
[33] Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
Witten, Daniela M.
Tibshirani, Robert J.
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 2009, 8 (01)
[34] Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data
Fu, Xiao
Huang, Kejun
Papalexakis, Evangelos E.
Song, Hyun Ah
Talukdar, Partha
Sidiropoulos, Nicholas D.
Faloutsos, Christos
Mitchell, Tom
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2019, 31 (12) : 2304 - 2318
[35] GENERALIZED NONLINEAR CANONICAL CORRELATION ANALYSIS WITH ORDERED CATEGORICAL AND DICHOTOMOUS DATA
Thanoon, Thanoon Y.
Adnan, Robiah
Saffari, Seyed Ehsan
JURNAL TEKNOLOGI, 2015, 75 (01): : 91 - 99
[36] Statistical model-based approaches for functional connectivity analysis of neuroimaging data
Foti, Nicholas J.
Fox, Emily B.
CURRENT OPINION IN NEUROBIOLOGY, 2019, 55 : 48 - 54
[37] GPU accelerated dynamic functional connectivity analysis for functional MRI data
Akgun, Devrim
Sakoglu, Uenal
Esquivel, Johnny
Adinoff, Bryon
Mete, Mutlu
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2015, 43 : 53 - 63
[38] Ambiguous results in functional neuroimaging data analysis due to covariate correlation
Andrade, A
Paradis, AL
Rouquette, S
Poline, JB
NEUROIMAGE, 1999, 10 (04) : 483 - 486
[39] Orthogonal canonical correlation analysis and applications
Wang, Li
Zhang, Lei-hong
Bai, Zhaojun
Li, Ren-Cang
OPTIMIZATION METHODS & SOFTWARE, 2020, 35 (04): : 787 - 807
[40] Revisiting Deep Generalized Canonical Correlation Analysis
Karakasis, Paris A.
Sidiropoulos, Nicholas D.
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2023, 71 : 4392 - 4406

← 1 2 3 4 5 →