Dynamic causal modeling with genetic algorithms
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| Title: | Dynamic causal modeling with genetic algorithms |
|---|---|
| Authors: | Pyka, M.1 martin.pyka@gmx.de, Heider, D.2, Hauke, S.3, Kircher, T.4, Jansen, A.1 |
| Source: | Journal of Neuroscience Methods. Jan2011, Vol. 194 Issue 2, p402-406. 5p. |
| Subjects: | Genetic algorithms, Brain imaging, Genetic mutation, Probability theory, Mathematical optimization, Matrices (Mathematics) |
| Abstract: | Abstract: In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 57076287 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic causal modeling with genetic algorithms – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pyka%2C+M%2E%22">Pyka, M.</searchLink><relatesTo>1</relatesTo><i> martin.pyka@gmx.de</i><br /><searchLink fieldCode="AR" term="%22Heider%2C+D%2E%22">Heider, D.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hauke%2C+S%2E%22">Hauke, S.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Kircher%2C+T%2E%22">Kircher, T.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Jansen%2C+A%2E%22">Jansen, A.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Jan2011, Vol. 194 Issue 2, p402-406. 5p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+imaging%22">Brain imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+mutation%22">Genetic mutation</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Matrices+%28Mathematics%29%22">Matrices (Mathematics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jneumeth.2010.11.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 402 Subjects: – SubjectFull: Genetic algorithms Type: general – SubjectFull: Brain imaging Type: general – SubjectFull: Genetic mutation Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Matrices (Mathematics) Type: general Titles: – TitleFull: Dynamic causal modeling with genetic algorithms Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pyka, M. – PersonEntity: Name: NameFull: Heider, D. – PersonEntity: Name: NameFull: Hauke, S. – PersonEntity: Name: NameFull: Kircher, T. – PersonEntity: Name: NameFull: Jansen, A. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: Jan2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 01650270 Numbering: – Type: volume Value: 194 – Type: issue Value: 2 Titles: – TitleFull: Journal of Neuroscience Methods Type: main |
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