Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.

Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or "brain-read", unattend...

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Autores principales:	Spiro P Pantazatos, Ardesheer Talati, Paul Pavlidis, Joy Hirsch
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6a1da2ad9d214a5c8fe84cc5d14a626c
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spelling	oai:doaj.org-article:6a1da2ad9d214a5c8fe84cc5d14a626c2021-11-18T05:51:28ZDecoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.1553-734X1553-735810.1371/journal.pcbi.1002441https://doaj.org/article/6a1da2ad9d214a5c8fe84cc5d14a626c2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22479172/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or "brain-read", unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90-100%) were achieved with 15-40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction.Spiro P PantazatosArdesheer TalatiPaul PavlidisJoy HirschPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 3, p e1002441 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Spiro P Pantazatos Ardesheer Talati Paul Pavlidis Joy Hirsch Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
description	Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or "brain-read", unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90-100%) were achieved with 15-40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction.
format	article
author	Spiro P Pantazatos Ardesheer Talati Paul Pavlidis Joy Hirsch
author_facet	Spiro P Pantazatos Ardesheer Talati Paul Pavlidis Joy Hirsch
author_sort	Spiro P Pantazatos
title	Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
title_short	Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
title_full	Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
title_fullStr	Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
title_full_unstemmed	Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
title_sort	decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/6a1da2ad9d214a5c8fe84cc5d14a626c
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_version_	1718424732273999872

Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity.

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