title: Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.
abstract: BACKGROUND
  Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA.
  
  
  METHODS
  For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering.
  
  
  RESULTS
  The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features.
  
  
  CONCLUSIONS
  Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA.
  
  
  CONCLUSIONS
  Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps.
authors: Department of Mathematical Information Technology, University of Jyväskylä, Finland. Electronic address: Fengyu.Cong@jyu.fi.
authors: Fengyu
authors: F
authors: Cong
authors: Department of Mathematical Information Technology, University of Jyväskylä, Finland.
authors: Tuomas
authors: T
authors: Puoliväli
authors: Department of Mathematical Information Technology, University of Jyväskylä, Finland; Finnish Centre of Excellence in Interdisciplinary Music Research, University of Jyväskylä, Finland.
authors: Vinoo
authors: V
authors: Alluri
authors: Department of Mathematical Information Technology, University of Jyväskylä, Finland.
authors: Tuomo
authors: T
authors: Sipola
authors: Department of Mathematical Information Technology, University of Jyväskylä, Finland; Finnish Centre of Excellence in Interdisciplinary Music Research, University of Jyväskylä, Finland.
authors: Iballa
authors: I
authors: Burunat
authors: Finnish Centre of Excellence in Interdisciplinary Music Research, University of Jyväskylä, Finland.
authors: Petri
authors: P
authors: Toiviainen
authors: Department of Electronic and Computer Engineering, Brunel University, UK; Department of Mathematical Information Technology, University of Jyväskylä, Finland.
authors: Asoke K
authors: AK
authors: Nandi
authors: Finnish Centre of Excellence in Interdisciplinary Music Research, University of Jyväskylä, Finland; Cognitive Brain Research Unit, Institute of Behavioral Sciences, University of Helsinki, Finland.
authors: Elvira
authors: E
authors: Brattico
authors: Department of Mathematical Information Technology, University of Jyväskylä, Finland.
authors: Tapani
authors: T
authors: Ristaniemi
mesh_terms: D000161:Acoustic Stimulation; D000328:Adult; D001307:Auditory Perception / Q000502:physiology*; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009146:Music; D010100:Oxygen; D025341:Principal Component Analysis; D055815:Young Adult
chemical_list: D010100:Oxygen
keywords: Diffusion map; FFT filter; Fast model order selection; ICA; Real-world experiences; fMRI