Exploratory analysis of time-varying functional connectivity in a visual task

• Published in: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (Online) pp. 1 - 5
• Main Authors: Jingjun Wong, Zhiguo Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2015
• Subjects: Correlation; Correlation coefficient; functional connectivity; functional magnetic resonance imaging; Kalman filtering; Kalman filters; Magnetic resonance imaging; Noise; Smoothing methods; time-varying signal processing; Visualization
• ISSN: 2377-9314
• DOI: 10.1109/CIVEMSA.2015.7158633

Functional magnetic resonance imaging (fMRI) has become a common tool in investigating brain activities on human subjects. Although each region of the brain was thought to be independent and responsible only for particular tasks and specialized functions, recent studies have shown that different regions of the brain interact with each other in performing specific tasks or even during the resting state. However, the temporal variations in functional connectivity have been largely overlooked by most studies. This study aims to explore time-varying properties of functional connectivity through comparing results from different correlation and regression analysis methods on a sample set of fMRI data acquired from a visual task. The results clearly show that functional connectivity in the visual task is transient, which suggests that simply assuming a sustained connectivity change during task period might not be sufficient to capture dynamic functional connectivity changes induced by tasks.