Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors
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Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals—including known population brain networks and subject-specific signals—are represented as latent variables. For estimation, we derive an expectation–maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75–250% higher intra-subject reliability. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.Keywords:
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In this article, I investigate two classes of noninformative priors: the reference priors of Berger and Bernardo and the reverse reference priors attributed to J.K. Ghosh. Datta and Ghosh (1995) gave a simple condition under which reference priors agree with reverse reference priors. They also gave several examples showing agreement or disagreement between the two priors. I derive the reference priors and reverse reference priors for several reparametrization of one of these examples, examine the invariance properties of the different parametrizations, and observe an interesting relationship between priors and invariance properties.
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Independent component analysis (ICA) has been widely applied to identify brain functional networks from multiple-subject fMRI. However, the best approach to handle artifacts is not yet clear. In this work, we study and compare two ICA approaches for artifact removal using simulations and real fMRI data. The first approach, recommended by the human connectome project, performs ICA on individual data to remove artifacts, and then applies group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA artifact Removal Plus Group ICA (TRPG). A second approach, Group Information Guided ICA (GIG-ICA), performs ICA on group data, and then removes the artifact group independent components (ICs), followed by individual subject ICA using the remaining group ICs as spatial references. Experiments demonstrate that GIG-ICA is more accurate in estimation of sources and time courses, more robust to data quality and quantity, and more reliable for identifying networks than IRPG.
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Abstract Popular models for decision making under ambiguity assume that people use not one but multiple priors. This paper is a first attempt to experimentally elicit multiple priors. In an ambiguous scenario with two underlying states we measure a subject’s single prior, her other potential priors (multiple priors), her confidence in these priors valuation of an ambiguous asset with the same underlying states. We also investigate subjects' updating of (multiple) priors after receiving signals about the true states. We find that single priors are best understood as a confidence-weighted average of multiple priors. Single priors also predict the valuation of ambiguous assets best, while both the minimum and maximum of subjects' multiple priors add explanatory power. This provides some but no exclusive support for the maxmin (Gilboa and Schmeidler, 1989) and the alpha maxmin model (Ghirardato et al., 2004). With regard to updating of priors, we do not observe strong deviations from Bayesian learning, although subjects overadjust/underadjust their priors and their confidence in multiple priors after a contradictory/confirming signal. Subjects also react to neutral information with more confidence in their priors. This holds under ambiguity, but not in a comparison treatment under risk.
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Blind separation of mixture images which mutually independent has been solved efficiently by some independent component analysis(ICA) methods. But these methods often failed in case of the source images are statistically non-independent. A novel fixed-point FastICA algorithm based on complexity pursuit is presented in this paper and with the algorithm the mixed images which not mutually independent can be separated successfully. Experimental results demonstrate the efficiency of our proposed method.
FastICA
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Independent Component Analysis (ICA) is a statistical signal processing technique having emerging new practical application areas, such as blind signal separation such as mixed voices or images, analysis of several types of data or feature extraction. Fast independent component analysis (Fast ICA ) is one of the most efficient ICA technique. Fast ICA algorithm separates the independent sources from their mixtures by measuring non-gaussianity.In this paper we present a method that can separate the signals as individual channels from other channels and also remove the noise using fast ica algorithm. The method is to decompose a multi channel signal into statistically independent components.
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Abstract We outline what we believe could be an improvement in future discussions of the brain acting as a Bayesian‐Laplacian system. We do so by distinguishing between two broad classes of priors on which the brain's inferential systems operate: in one category are biological priors (β priors ) and in the other artefactual ones (α priors ). We argue that β priors, of which colour categories and faces are good examples, are inherited or acquired very rapidly after birth, are highly or relatively resistant to change through experience, and are common to all humans. The consequence is that the probability of posteriors generated from β priors having universal assent and agreement is high. By contrast, α priors, of which man‐made objects are examples, are acquired post‐natally and modified at various stages throughout post‐natal life; they are much more accommodating of, and hospitable to, new experiences. Consequently, posteriors generated from them are less likely to find universal assent. Taken together, in addition to the more limited capacity of experiment and experience to alter the β priors compared with α priors, another cardinal distinction between the two is that the probability of posteriors generated from β priors having universal agreement is greater than that for α priors . The two categories are distinct at the extremes; there is, however, a middle range where they merge into one another to varying extents, resulting in posteriors that draw upon both categories.
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Independent component analysis (ICA) is a new technique to statistically extract independent components from the observed multidimensional mixture of data. Many successful examples of ICA application in the filed of signal processing are reported recently. Independent component analysis (ICA) was originally developed to deal with problems that are closely related to cocktail- party problems.ICA is a powerful and useful statistical tool for extracting independent source given only observed data that are mixtures of the unknown sources.
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Abstract We outline what we believe could be an improvement in future discussions of the brain acting as a Bayesian-Laplacian system. We do so by distinguishing between two broad classes of priors on which the brain’s inferential systems operate: in one category are biological priors ( β priors ) and in the other artifactual ones ( α priors ). We argue that β priors , of which colour categories and faces are good examples, are inherited or acquired very rapidly after birth, are highly or relatively resistant to change through experience, and are common to all humans. The consequence is that the probability of posteriors generated from β priors having universal assent and agreement is high. By contrast, α priors , of which man-made objects are examples, are acquired post-natally and modified at various stages throughout post-natal life; they are much more accommodating of, and hospitable to, new experiences. Consequently, posteriors generated from them are less likely to find universal assent. Taken together, in addition to the more limited capacity of experiment and experience to alter the β priors compared to α priors , another cardinal distinction between the two is that the probability of posteriors generated from β priors having universal agreement is greater than that for α priors . The two categories are not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.
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Blind separation of mixture images which mutually independent has been solved efficiently by some independent component analysis(ICA) methods. But these methods often failed in case of the source images are statistically non-independent. A novel fixed-point FastICA algorithm based on complexity pursuit is presented in this paper and with the algorithm the mixed images which not mutually independent can be separated successfully. Experimental results demonstrate the efficiency of our proposed method.
FastICA
Component (thermodynamics)
Separation (statistics)
Component analysis
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