A new method for independent component analysis with priori information based on multi-objective optimization

Yuhu Shi, Weiming Zeng (Corresponding Author), Nizhuan Wang, Le Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)72-82
Number of pages11
JournalJournal of Neuroscience Methods
Volume283
Early online date29 Mar 2017
DOIs
Publication statusPublished - 1 May 2017
Externally publishedYes

Keywords

  • Adaptive weighted summation method
  • Fixed-point learning algorithm
  • Independent component analysis
  • Multi-objective optimization
  • Priori information

ASJC Scopus subject areas

  • General Neuroscience

Fingerprint

Dive into the research topics of 'A new method for independent component analysis with priori information based on multi-objective optimization'. Together they form a unique fingerprint.

Cite this