Abstract：
BACKGROUND:Currently the problem of incorporating priori information into an independent component analysis (ICA) model is often solved under the framework of constrained ICA, which utilizes the priori information as a reference signal to form a constraint condition and then introduce it into classical ICA. However, it is difficult to pre-determine a suitable threshold parameter to constrain the closeness between the output signal and the reference signal in the constraint condition. NEW METHOD:In this paper, a new model of ICA with priori information as a reference signal is established on the framework of multi-objective optimization, where an adaptive weighted summation method is introduced to solve this multi-objective optimization problem with a new fixed-point learning algorithm. RESULTS:The experimental results of fMRI hybrid data and task-related data on the single-subject level have demonstrated that the proposed method has a better overall performance on the recover abilities of both spatial source and time course. COMPARISON WITH EXISTING METHODS:At the same time, compared with traditional ICA with reference methods and classical ICA method, the experimental results of resting-state fMRI data on the group-level have showed that the group independent component calculated by the proposed method has a higher correlation with the corresponding independent component of each subject through T-test. CONCLUSIONS:The proposed method does not need us to select a threshold parameter to constrain the closeness between the output signal and the reference signal. In addition, the performance of functional connectivity detection has a great improvement in comparison with traditional methods.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Shi Y,Zeng W,Wang N,Zhao Ldoi
10.1016/j.jneumeth.2017.03.018subject
Has Abstractpub_date
2017-05-01 00:00:00pages
72-82eissn
0165-0270issn
1872-678Xpii
S0165-0270(17)30083-3journal_volume
283pub_type
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