Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis

Journal of Neuroscience Methods
Fengyu CongTapani Ristaniemi

Abstract

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. 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. 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. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model ...Continue Reading

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Citations

Nov 10, 2015·Neuroscience·H PoikonenM Huotilainen
Apr 4, 2015·Journal of Neuroscience Methods·Fengyu CongTapani Ristaniemi
Jun 24, 2014·Journal of Neuroscience Methods·Lukas BreuerN Jon Shah
May 25, 2016·Social Cognitive and Affective Neuroscience·Lars RogenmoserLutz Jäncke
Apr 11, 2015·Journal of Neuroscience Methods·Mou-Chuan YuVince D Calhoun
Jan 10, 2018·Frontiers in Human Neuroscience·Chao LiuAsoke K Nandi

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