Iterative Dual-Regression with Sparse Prior to Estimate Individual Neuronal Activations from Group Functional Magnetic Resonance Imaging (fMRI) Data

نویسندگان

  • Yong-Hwan Kim
  • Dong-Youl Kim
  • Jong-Hwan Lee
چکیده

An iterative dual-regression (DR) with a sparse prior was proposed to estimate individual neuronal activations from an ICA application to a group functional magnetic resonance imaging (fMRI) data. Compared to an original DR with two steps of least-squares to estimate both spatial and temporal patterns, our approach showed enhanced true positive rates while reducing false positive rates across all individual results as quantitatively evaluated using semi-artificial fMRI data. Keywords― Independent component analysis, ICA, Group ICA, Dual Regression, Alternating LeastSquares, Sparse Prior

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تاریخ انتشار 2011