title: A new method for independent component analysis with priori information based on multi-objective optimization.
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.
  
  
  METHODS
  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.
  
  
  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.
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
authors.forename: Yuhu
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China. Electronic address: zengwm86@163.com.
authors.forename: Weiming
authors.initials: W
authors.lastname: Zeng
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
authors.forename: Nizhuan
authors.initials: N
authors.lastname: Wang
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
authors.forename: Le
authors.initials: L
authors.lastname: Zhao
mesh_terms: D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D025341:Principal Component Analysis; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*
keywords: Adaptive weighted summation method; Fixed-point learning algorithm; Independent component analysis; Multi-objective optimization; Priori information