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Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data |
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| Authors: | Kwanghee Jung Yoshio Takane Heungsun Hwang Todd S Woodward |
| Institution: | 1. Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, Canada, H3A 1B1 2. BC Mental Health and Addictions Research Institute, Department of Psychiatry, University of British Columbia, Child and Family Research Institute Building, Room A3-112, 938 West 28th Avenue, Vancouver, BC, Canada, V5Z 4H4 3. BC Mental Health and Addictions Research Institute, Department of Psychiatry, University of British Columbia, Child and Family Research Institute Building, 938 West 28th Avenue, Vancouver, BC, Canada, V5Z 4H4 |
| Abstract: | We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also incorporates direct and modulating effects of input variables on specific latent variables and on connections between latent variables, respectively. An alternating least square (ALS) algorithm is developed for parameter estimation. An improved bootstrap method called a modified moving block bootstrap method is used to assess reliability of parameter estimates, which deals with time dependence between consecutive observations effectively. We analyze synthetic and real data to illustrate the feasibility of the proposed method. |
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