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Covariate-Adjusted Hybrid Principal Components Analysis
Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. The high-dimensional data capture underlying neural dynamics and it is of clinical interest to model differences in neurodevelopmental trajectories betwe...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274738/ http://dx.doi.org/10.1007/978-3-030-50153-2_30 |
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author | Scheffler, Aaron Wolfe Dickinson, Abigail DiStefano, Charlotte Jeste, Shafali Şentürk, Damla |
author_facet | Scheffler, Aaron Wolfe Dickinson, Abigail DiStefano, Charlotte Jeste, Shafali Şentürk, Damla |
author_sort | Scheffler, Aaron Wolfe |
collection | PubMed |
description | Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. The high-dimensional data capture underlying neural dynamics and it is of clinical interest to model differences in neurodevelopmental trajectories between diagnostic groups, for example typically developing (TD) children and children with autism spectrum disorder (ASD). In such cases, valid group-level inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, resting state EEG is collected on both TD and ASD children aged two to twelve years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development for both the TD and ASD diagnostic groups. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for region-referenced functional EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group-level inference. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children. |
format | Online Article Text |
id | pubmed-7274738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72747382020-06-08 Covariate-Adjusted Hybrid Principal Components Analysis Scheffler, Aaron Wolfe Dickinson, Abigail DiStefano, Charlotte Jeste, Shafali Şentürk, Damla Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. The high-dimensional data capture underlying neural dynamics and it is of clinical interest to model differences in neurodevelopmental trajectories between diagnostic groups, for example typically developing (TD) children and children with autism spectrum disorder (ASD). In such cases, valid group-level inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, resting state EEG is collected on both TD and ASD children aged two to twelve years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development for both the TD and ASD diagnostic groups. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for region-referenced functional EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group-level inference. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children. 2020-05-16 /pmc/articles/PMC7274738/ http://dx.doi.org/10.1007/978-3-030-50153-2_30 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Scheffler, Aaron Wolfe Dickinson, Abigail DiStefano, Charlotte Jeste, Shafali Şentürk, Damla Covariate-Adjusted Hybrid Principal Components Analysis |
title | Covariate-Adjusted Hybrid Principal Components Analysis |
title_full | Covariate-Adjusted Hybrid Principal Components Analysis |
title_fullStr | Covariate-Adjusted Hybrid Principal Components Analysis |
title_full_unstemmed | Covariate-Adjusted Hybrid Principal Components Analysis |
title_short | Covariate-Adjusted Hybrid Principal Components Analysis |
title_sort | covariate-adjusted hybrid principal components analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274738/ http://dx.doi.org/10.1007/978-3-030-50153-2_30 |
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