Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition.
J Neurosci Methods
; 325: 108359, 2019 09 01.
Article
em En
| MEDLINE
| ID: mdl-31306718
ABSTRACT
BACKGROUND:
Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. NEWMETHOD:
The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone.RESULTS:
Hierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables. Comparison with ExistingMethod:
The Kullback-Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20.CONCLUSION:
The current study sheds new light on the importance of temporal course variability in ICA of fMRI data.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Imageamento por Ressonância Magnética
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Córtex Cerebral
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Neuroimagem Funcional
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Rede Nervosa
Tipo de estudo:
Guideline
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Male
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article