Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Mahdi Rajabioun | Ali Motie-Nasrabadi | Mohammad Bagher Shamsollahi | Robert Coben

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=116996
Date :  2019/10/16
Publish in :    Biomedical Engineering / Biomedizinische Technik
DOI :  https://doi.org/doi.org/10.1515/bmt-2019-0062
Link :  http://dx.doi.org/doi.org/10.1515/bmt-2019-0062
Keywords : autism; dual Kalman filter; effective connectivity; multivariate autoregressive model; source localization methods.

Abstract :

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Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others