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Title: A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder
Authors: Simões, Marco 
Monteiro, Raquel 
Andrade, João 
Mouga, Susana 
França, Felipe
Oliveira, Guiomar 
Carvalho, Paulo 
Castelo-Branco, Miguel 
Keywords: emotional facial expression; mental imagery; EEG biomarker; machine learning; autism spectrum disorder; dynamic expressions
Issue Date: 2018
Publisher: Frontiers Media S.A.
Project: This work was supported by FCT – Portuguese national funding agency for science, research and technology [Grants PAC MEDPERSYST, POCI-01-0145-FEDER-016428, BIGDATIMAGE, CENTRO-01-0145-FEDER-000016 financed by Centro 2020 FEDER, COMPETE, FCT-UID/4539/2013 – COMPETE, POCI-01-0145-FEDER-007440, POCI-01-0145- FEDER-30852, Fellowships SFRH/BD/77044/2011 and SFRH/BD/102779/2014, and the BRAINTRAIN Project – Taking imaging into the therapeutic domain: Self-regulation of brain systems for mental disorders - FP7 HEALTH 2013 INNOVATION 1 602186 20, 2013, FLAD Life Sciences 2016. 
Serial title, monograph or event: Frontiers in Neuroscience
Volume: 12
Issue: NOV
Abstract: Imagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are able to classify abnormal imagery in ASD. To provide a handle between perception and action imagery cycles it is important to use visual stimuli exploring the dynamical nature of emotion representation. We conducted a case-control study providing a link between both visualization and mental imagery of dynamic facial expressions and investigated source responses to pure face-expression contrasts. We were able to replicate the same highly group discriminative neural signatures during action observation (dynamical face expressions) and imagery, in the precuneus. Larger activation in regions involved in imagery for the ASD group suggests that this effect is compensatory. We conducted a machine learning procedure to automatically identify these group differences, based on the EEG activity during mental imagery of facial expressions. We compared two classifiers and achieved an accuracy of 81% using 15 features (both linear and non-linear) of the signal from theta, high-beta and gamma bands extracted from right-parietal locations (matching the precuneus region), further confirming the findings regarding standard statistical analysis. This robust classification of signals resulting from imagery of dynamical expressions in ASD is surprising because it far and significantly exceeds the good classification already achieved with observation of neutral face expressions (74%). This novel neural correlate of emotional imagery in autism could potentially serve as a clinical interventional target for studies designed to improve facial expression recognition, or at least as an intervention biomarker.
ISSN: 1662-4548
DOI: 10.3389/fnins.2018.00791
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
FMUC Medicina - Artigos em Revistas Internacionais
I&D CIBIT - Artigos em Revistas Internacionais
I&D ICNAS - Artigos em Revistas Internacionais

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