Background: Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in recent year. Moreover, the supervised dictionary learning (SDL) that fixed the task stimulus curves as predefined atoms was proposed to improve ODL for functional networks separation. However, SDL cannot estimate the real time courses underlying the brain networks and cannot be applied to the inter-network connectivity analysis. This study aimed at investigating how to add the temporal prior information to ODL to extract the accurate task-related brain networks and the corresponding time courses.
New Method: To improve the performance of ODL, we propose a semi-blind ODL (semi-ODL) method that incorporates temporal prior information of the task paradigm into the dictionary updating process and optimizes the direction of one or more specific atoms "close" to the task time courses.
Results: Results of the simulated and real fMRI experiment revealed that semi-ODL extracted more accurate task-related component and time courses than ODL and SDL. For one-task fMRI data, semi-ODL and Infomax-ICA showed similar detection power in most cases.
Comparison With Existing Methods: The semi-ODL outperformed ODL, SDL in robustness to noise, spatial detection power and time course estimation. Moreover, semi-ODL showed comparable performance to Infomax-ICA for one-task fMRI data and outperformed Infomax-ICA in extracting the components related to each task from multi-task fMRI data.
Conclusions: The semi-ODL method is potentially useful to reveal brain networks underlying various cognitive tasks and the interactions between task-related brain networks.
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| http://dx.doi.org/10.1016/j.jneumeth.2019.03.014 | DOI Listing |
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