title: ICA of full complex-valued fMRI data using phase information of spatial maps.
abstract: BACKGROUND
  ICA of complex-valued fMRI data is challenging because of the ambiguous and noisy nature of the phase. A typical solution is to remove noisy regions from fMRI data prior to ICA. However, it may be more optimal to carry out ICA of full complex-valued fMRI data, since any filtering or voxel-based processing may disrupt information that can be useful to ICA.
  
  
  METHODS
  We enable ICA of the full complex-valued fMRI data by utilizing phase information of estimated spatial maps (SMs). The SM phases are first adjusted to properly represent spatial phase changes of all voxels based on estimated time courses (TCs), and then these are used to segment the voxels into BOLD-related and unwanted voxels based on a criterion of TC real-part power maximization. Single-subject and group phase masks are finally constructed to remove the unwanted voxels from the individual and group SM estimates.
  
  
  RESULTS
  Our method efficiently estimated not only the task-related component but also the non-task-related component DMN.
  
  
  CONCLUSIONS
  Our method extracted 139-331% more contiguous and reasonable activations than magnitude-only infomax for the task-related component and DMN at |Z|>2.5, and detected more BOLD-related voxels, but eliminated more unwanted voxels than ICA of complex-valued fMRI data with pre-ICA de-noising. Our TC-based phase de-ambiguity exhibited higher accuracy and robustness than the SM-based method.
  
  
  CONCLUSIONS
  The TC-based phase de-ambiguity is essential to prepare the SM phases. The SM phases provide a new post-ICA index for reliably identifying and suppressing the unwanted voxels.
authors: School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China.
authors: Mou-Chuan
authors: MC
authors: Yu
authors: School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China. Electronic address: qhlin@dlut.edu.cn.
authors: Qiu-Hua
authors: QH
authors: Lin
authors: School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China.
authors: Li-Dan
authors: LD
authors: Kuang
authors: School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China.
authors: Xiao-Feng
authors: XF
authors: Gong
authors: Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China; Department of Mathematical Information Technology, University of Jyvaskyla, Finland.
authors: Fengyu
authors: F
authors: Cong
authors: The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors: Vince D
authors: VD
authors: Calhoun
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology*
keywords: Complex-valued fMRI data; Independent component analysis (ICA); Phase de-ambiguity; Phase masking; Phase positioning; Spatial map phase