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data-driven clustering approach  (Neuropharm Ltd)
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data-driven forms  (OpenMRS LLC)
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process-guided deep learning and data-driven approach  (ProDa Biotech LLC)
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a data-driven approach for predicting printability in metal additive manufacturing processes  (Mycroft Bioanalytics)
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ica of the fmri toolbox gift v1.3b  (SourceForge net)
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SourceForge net ica of the fmri toolbox gift v1.3b
An illustration of the synthesis and analysis models of <t>the</t> <t>IVA</t> method applied to the group fMRI data. Similar to the <t>ICA</t> method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).
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nvivo for mac v. 11.4.3  (qsr international)
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An illustration of the synthesis and analysis models of <t>the</t> <t>IVA</t> method applied to the group fMRI data. Similar to the <t>ICA</t> method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).
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data-driven approaches  (EcoDynamics Inc)
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EcoDynamics Inc data-driven approaches
An illustration of the synthesis and analysis models of <t>the</t> <t>IVA</t> method applied to the group fMRI data. Similar to the <t>ICA</t> method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).
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memory-based learning (mbl)  (MBL Life science)
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MBL Life science memory-based learning (mbl)
An illustration of the synthesis and analysis models of <t>the</t> <t>IVA</t> method applied to the group fMRI data. Similar to the <t>ICA</t> method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).
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data-driven clusterizing approaches  (Stegmann Systems GmbH)
90
Stegmann Systems GmbH data-driven clusterizing approaches
An illustration of the synthesis and analysis models of <t>the</t> <t>IVA</t> method applied to the group fMRI data. Similar to the <t>ICA</t> method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).
Data Driven Clusterizing Approaches, supplied by Stegmann Systems GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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nonlinear dynamics sindy  (Nonlinear Dynamics)
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Nonlinear Dynamics nonlinear dynamics sindy
An illustration of the synthesis and analysis models of <t>the</t> <t>IVA</t> method applied to the group fMRI data. Similar to the <t>ICA</t> method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).
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Image Search Results


An illustration of the synthesis and analysis models of the IVA method applied to the group fMRI data. Similar to the ICA method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).

Journal:

Article Title: Independent Vector Analysis (IVA) for Group fMRI Processing of Subcortical Area

doi: 10.1002/ima.20141

Figure Lengend Snippet: An illustration of the synthesis and analysis models of the IVA method applied to the group fMRI data. Similar to the ICA method, the measured BOLD time series is assumed to be a linear combination of the unknown IC maps through a mixing matrix (time courses; unknown) in the synthesis model. In the concurrent analysis model, the assumed IC maps can be estimated by learning an inverse of the mixing matrix (i.e. unmixing matrix; inverse of the time courses). Note that, however, each component of the IVA model (represented as rectangular boxes) is a vector as opposed to a scalar value as in the ICA model. Additionally, the constraint of dependence among the elements of each vector component is applied to (automatically) group similar activation patterns (among the subjects) as a single output vector component. And thus, each vector component represents a group trend without complication of the random permutation of similar components across the subjects (please refer to the Methods Section for a short description of the model; the detailed description on the model and learning rule can be found in Lee et al., 2008).

Article Snippet: For comparison to the IVA approach, GLM (SPM2; univariate and model-based approach) and a group ICA of the fMRI toolbox (GIFT, v1.3b; icatb.sourceforge.net; multivariate and data-driven approach) were also applied to the same simulated data sets.

Techniques: Plasmid Preparation, Activation Assay