00000cab a22000003a 4500 UP-99796217609164126 Buklod 20231007234053.0 m |o d | ta 100219s xx d | ||r ||||| || DENGII eng Rajapakse, J.C. Exploratory analysis of brain connectivity with ICA. pp. 102-111 Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task. Automated method. Functional connectivity. Functional magnetic resonance imaging. Independent component analysis. Language reading task dataset. Spatial ICA. Structural equation modeling. Task-related brain activation. Task-related neural systems. Visual task dataset. IEEE Engineering in medicine and biology magazine 25, 2 (2006). FO UPD DENG-II Article