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A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments
Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onse...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5518772/ https://www.ncbi.nlm.nih.gov/pubmed/28476662 http://dx.doi.org/10.1016/j.neuroimage.2017.05.001 |
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author | Pinti, Paola Merla, Arcangelo Aichelburg, Clarisse Lind, Frida Power, Sarah Swingler, Elizabeth Hamilton, Antonia Gilbert, Sam Burgess, Paul W. Tachtsidis, Ilias |
author_facet | Pinti, Paola Merla, Arcangelo Aichelburg, Clarisse Lind, Frida Power, Sarah Swingler, Elizabeth Hamilton, Antonia Gilbert, Sam Burgess, Paul W. Tachtsidis, Ilias |
author_sort | Pinti, Paola |
collection | PubMed |
description | Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that “brain-first” rather than “behaviour-first” analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging. |
format | Online Article Text |
id | pubmed-5518772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-55187722017-07-31 A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments Pinti, Paola Merla, Arcangelo Aichelburg, Clarisse Lind, Frida Power, Sarah Swingler, Elizabeth Hamilton, Antonia Gilbert, Sam Burgess, Paul W. Tachtsidis, Ilias Neuroimage Article Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that “brain-first” rather than “behaviour-first” analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging. Academic Press 2017-07-15 /pmc/articles/PMC5518772/ /pubmed/28476662 http://dx.doi.org/10.1016/j.neuroimage.2017.05.001 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pinti, Paola Merla, Arcangelo Aichelburg, Clarisse Lind, Frida Power, Sarah Swingler, Elizabeth Hamilton, Antonia Gilbert, Sam Burgess, Paul W. Tachtsidis, Ilias A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments |
title | A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments |
title_full | A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments |
title_fullStr | A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments |
title_full_unstemmed | A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments |
title_short | A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments |
title_sort | novel glm-based method for the automatic identification of functional events (aide) in fnirs data recorded in naturalistic environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5518772/ https://www.ncbi.nlm.nih.gov/pubmed/28476662 http://dx.doi.org/10.1016/j.neuroimage.2017.05.001 |
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