Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis

Anemüller, Jörn; Duann, Jeng Ren; Sejnowski, Terrence J.; Makeig, Scott

doi:10.1016/j.neucom.2005.12.029

Cited by 21 publications

(8 citation statements)

References 17 publications

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“…The complex-valued computations have been present from the "birth" of ICA [36,2] and show nice potentials in the analysis of biomedical signals (EEG, fMRI), see e.g., [37,38,39].…”

Section: Post Nonlinear Modelsmentioning

confidence: 99%

Separation theorem for independent subspace analysis and its consequences

Szabó

Póczos

Lrincz

2012

Pattern Recognition

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Independent component analysis (ICA)-the theory of mixed, independent, non-Gaussian sources-has a central role in signal processing, computer vision and pattern recognition. One of the most fundamental conjectures of this research field is that independent subspace analysis (ISA)-the extension of the ICA problem, where groups of sources are independent-can be solved by a simple ICA followed by grouping the ICA components. The conjecture, called ISA separation principle, (i) has been rigorously proven for some distribution types recently, (ii) forms the basis of the state-of-theart ISA solvers, (iii) enables one to estimate the unknown number and the dimensions of the sources efficiently, and (iv) can be extended to generalizations of the ISA task, such as different linear-, controlled-, post nonlinear-, complex valued-, partially observed problems, as well as to problems dealing with nonparametric source dynamics. Here, we shall review the advances on this field.

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“…The complex-valued computations have been present from the "birth" of ICA [36,2] and show nice potentials in the analysis of biomedical signals (EEG, fMRI), see e.g., [37,38,39].…”

Section: Post Nonlinear Modelsmentioning

confidence: 99%

Separation theorem for independent subspace analysis and its consequences

Szabó

Póczos

Lrincz

2012

Pattern Recognition

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“…For instance, in the case of EEG (electroencephalogram) data, we have both spatial and temporal structure in the signal. That said, few algorithms take full advantage of this when performing independent component analysis [50]. The pyramidal kernel of [51] is one possible choice for dependent random variables.…”

Section: Independence Measuresmentioning

confidence: 99%

A Hilbert Space Embedding for Distributions

Smola¹,

Gretton²,

Song³

et al.

Lecture Notes in Computer Science

339

491

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Abstract. We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.Kernel methods are widely used in supervised learning [1,2,3,4], however they are much less established in the areas of testing, estimation, and analysis of probability distributions, where information theoretic approaches [5,6] have long been dominant. Recent examples include [7] in the context of construction of graphical models, [8] in the context of feature extraction, and [9] in the context of independent component analysis. These methods have by and large a common issue: to compute quantities such as the mutual information, entropy, or Kullback-Leibler divergence, we require sophisticated space partitioning and/or bias correction strategies [10,9].In this paper we give an overview of methods which are able to compute distances between distributions without the need for intermediate density estimation. Moreover, these techniques allow algorithm designers to specify which properties of a distribution are most relevant to their problems. We are optimistic that our embedding approach to distribution representation and analysis will lead to the development of algorithms which are simpler and more effective than entropy-based methods in a broad range of applications.We begin our presentation in Section 1 with an overview of reproducing kernel Hilbert spaces (RKHSs), and a description of how probability distributions can be represented as elements in an RKHS. In Section 2, we show how these representations may be used to address a variety of problems, including homogeneity testing (Section 2.1), covariate shift correction (Section 2.2), independence measurement (Section 2.3), feature extraction (Section 2.4), and density estimation (Section 2.5).

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“…This process reduced data size by more than 78%. ICA was then applied to BOLD-signal time series of within-brain voxels to separate the data into a sum of activity in maximally independent brain maps and find the associated BOLD-signal time courses (Anemüller, 2006). …”

Section: Fmri Data Analysismentioning

confidence: 99%

Analysis of visual cortex-event-related fMRI data using ICA decomposition

Ahmed

Alam

Abdullah‐Al‐Mamun

et al. 2011

IJBET

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Independent Component Analysis (ICA) of Functional Magnetic Resonance Imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation. In this work, to detect and visualise variations in single-trial Hemodynamic Responses (HR) in event-related fMRI data, Infomax ICA algorithm has been used. Six subjects participated in four fMRI sessions. ICA decomposition of the resulting Blood Oxygenation Level-Dependent (BOLD) data from each session produced an independent component active in primary visual cortex. The BOLD-image plots demonstrated that component HRs varied substantially and often systematically across trials as well as across sessions, subjects, and brain areas.

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Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis

Cited by 21 publications

References 17 publications

Separation theorem for independent subspace analysis and its consequences

Separation theorem for independent subspace analysis and its consequences

A Hilbert Space Embedding for Distributions

Analysis of visual cortex-event-related fMRI data using ICA decomposition

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