Sparse ICA Based on Infinite Norm for fMRI Analysis

Functional MRI (fMRI) is a functional neuroimaging technique that measures the brain activity by detecting the associated changes in blood flow. Independent component analysis (ICA) provides a feasible approach to analyze the collected data sets. In this paper, we introduce a novel criterion via infinity norm to achieve the sparse solution. The experimental result has been shown that the approach can be successfully applied in fMRI data. In memory-imagine cognitive experiment, the activated regions for different tasks are different in brain. But some regions are activated in each runs, which suggests that these brain regions may play an important role in cognition functions of memory-imagine.

[1]  Erkki Oja,et al.  Blind Separation of Positive Sources by Globally Convergent Gradient Search , 2004, Neural Computation.

[2]  Dietrich Lehmann,et al.  Nonsmooth nonnegative matrix factorization (nsNMF) , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  T. Sejnowski,et al.  Single-Trial Variability in Event-Related BOLD Signals , 2002, NeuroImage.

[4]  Yew-Soon Ong,et al.  Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I , 2005, ICNC.

[5]  Karl J. Friston,et al.  Convolution Models for fMRI , 2007 .

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

[7]  T. Sejnowski,et al.  Human Brain Mapping 6:368–372(1998) � Independent Component Analysis of fMRI Data: Examining the Assumptions , 2022 .

[8]  Aapo Hyvärinen,et al.  Learning Natural Image Structure with a Horizontal Product Model , 2009, ICA.

[9]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

[10]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[11]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[12]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[13]  R. Tibshirani,et al.  A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.

[14]  Pierre Comon,et al.  Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast With Algebraic Optimal Step Size , 2010, IEEE Transactions on Neural Networks.

[15]  Adeel Razi,et al.  On nodes and modes in resting state fMRI , 2014, NeuroImage.

[16]  Pierre Comon,et al.  Comparative Speed Analysis of FastICA , 2007, ICA.

[17]  Jing Zhao,et al.  Document Clustering Based on Nonnegative Sparse Matrix Factorization , 2005, ICNC.

[18]  Terrence J. Sejnowski,et al.  A Non-linear Information Maximisation Algorithm that Performs Blind Separation , 1994, NIPS.

[19]  Karl J. Friston,et al.  Impaired Frontal-Basal Ganglia Connectivity in Adolescents with Internet Addiction , 2014, Scientific Reports.

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