Pseudo-real fMRI data generation and its utility toward quantitative evaluation of analytical methods

Functional magnetic resonance imaging (fMRI) modality has been widely employed to measure neuronal activations of the human brain using such as a model-based general linear model (GLM) and data-driven independent component analysis (ICA) approaches. In this study, we were motivated to investigate the performance of two popular methods with a hypothesis that these methods would have advantages and disadvantages depending on the variability of the fMRI data across subjects in both temporal and spatial domain. To quantitatively evaluate two methods, the pseudo-real fMRI data were generated by combining the decomposed non-neuronal components estimated from real resting-state fMRI data and artificially generated neuronal components with varying degree of temporal and spatial pattern variability of task related activation patterns in an individual level. Using the pseudo-real fMRI data, the assessment of each method was conducted by comparing the estimated activations to reference neuronal activations. Our results indicated that the degree of spatial overlap size across subjects and degree of temporal pattern variability would be important factor to choose a proper analytical method.

[1]  Irene Tracey,et al.  Quantitative assessment of the reproducibility of functional activation measured with BOLD and MR perfusion imaging: Implications for clinical trial design , 2005, NeuroImage.

[2]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[3]  Wilkin Chau,et al.  An Empirical Comparison of SPM Preprocessing Parameters to the Analysis of fMRI Data , 2002, NeuroImage.

[4]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[5]  Vincent J Schmithorst,et al.  Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data , 2004, Journal of magnetic resonance imaging : JMRI.

[6]  Jong-Hwan Lee,et al.  Are posterior default-mode networks more robust than anterior default-mode networks? Evidence from resting-state fMRI data analysis , 2011, Neuroscience Letters.

[7]  Alan C. Evans,et al.  Bootstrap generation and evaluation of an fMRI simulation database. , 2009, Magnetic resonance imaging.

[8]  Habib Benali,et al.  CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. , 2007, Magnetic resonance imaging.

[9]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[10]  Tülay Adali,et al.  A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework , 2009, Human brain mapping.

[11]  Tülay Adali,et al.  Comparison of multi‐subject ICA methods for analysis of fMRI data , 2010, Human brain mapping.

[12]  Thomas E. Nichols,et al.  Non-white noise in fMRI: Does modelling have an impact? , 2006, NeuroImage.

[13]  Karl J. Friston,et al.  Slice-timing effects and their correction in functional MRI , 2011, NeuroImage.