Filippetti, Maria Laura and Andreu-Perez, Javier and De Klerk, Carina and Rigato, Silvia (2023) Are advanced methods necessary to improve infant fNIRS data analysis? An assessment of baseline-corrected averaging, general linear model (GLM) and multivariate pattern analysis (MVPA) based approaches. NeuroImage, 265. p. 119756. DOI https://doi.org/10.1016/j.neuroimage.2022.119756
Filippetti, Maria Laura and Andreu-Perez, Javier and De Klerk, Carina and Rigato, Silvia (2023) Are advanced methods necessary to improve infant fNIRS data analysis? An assessment of baseline-corrected averaging, general linear model (GLM) and multivariate pattern analysis (MVPA) based approaches. NeuroImage, 265. p. 119756. DOI https://doi.org/10.1016/j.neuroimage.2022.119756
Filippetti, Maria Laura and Andreu-Perez, Javier and De Klerk, Carina and Rigato, Silvia (2023) Are advanced methods necessary to improve infant fNIRS data analysis? An assessment of baseline-corrected averaging, general linear model (GLM) and multivariate pattern analysis (MVPA) based approaches. NeuroImage, 265. p. 119756. DOI https://doi.org/10.1016/j.neuroimage.2022.119756
Abstract
In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying more advanced statistical analyses to infant fNIRS data and compared the most commonly used baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches, to show how the conclusions one would draw based on these different analysis approaches converge or differ. The different analysis methods were tested using a face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured in thirty 4-to-6-month-old infants. By including more standard approaches together with recent machine learning techniques, we aim to inform the fNIRS community on alternative ways to analyse infant fNIRS datasets.
Item Type: | Article |
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Uncontrolled Keywords: | Developmental & Child Psychology |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of Faculty of Science and Health > Psychology, Department of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 23 Dec 2022 13:43 |
Last Modified: | 16 May 2024 19:59 |
URI: | http://repository.essex.ac.uk/id/eprint/34174 |
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