Universiti Teknologi Malaysia Institutional Repository

Identifying dynamic effective connectivity states in fMRI based on time-varying vector autoregressive models

Samdin, S. B. and Ting, C. M. and Salleh, S. H. and Hamedi, M. and Noor, A. M. (2016) Identifying dynamic effective connectivity states in fMRI based on time-varying vector autoregressive models. In: International Conference for Innovation in Biomedical Engineering and Life Sciences, ICIBEL 2015, 6-8 Dec 2015, Putrajaya, Malaysia.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

We propose a framework to estimate the transition of effective connectivity states in functional magnetic resonance imaging (fMRI), with the changing experimental conditions. The fMRI effective connectivity is traditionally assumed to be stationary across the entire scanning timecourse. However, recent evidence shows that it exhibits dynamic changes over time. In this study, we employ a non-stationary model based on time-varying autoregression (TV-VAR) to capture the dynamic effective connectivity, and K-means clustering to identify the change-points of the connectivity states. The TV-VAR parameters are estimated sequentially in time using the Kalman filtering and the expectation- maximization (EM) algorithm. The extracted directed connectivities between brain regions are then used as features to the K-means algorithm to be partitioned into a finite number of states and to produce the state change-points, assuming the task condition boundaries are unknown. Experimental results on motor-task fMRI data show the ability of the proposed method in estimating the state-related changes in the motor regions during the resting-state and active conditions, with low squared estimation errors. The estimated brain-state connectivity also reveals different patterns between the healthy subjects and the stroke patients.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Dynamic Brain Connectivity, FMRI, Kalman Filters, State-space Models, Vector Autoregressive Model
Subjects:Q Science > QH Natural history
Q Science > QH Natural history > QH301 Biology
Divisions:Biosciences and Medical Engineering
ID Code:73490
Deposited By: Mohd Zulaihi Zainudin
Deposited On:26 Nov 2017 11:37
Last Modified:26 Nov 2017 11:37

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