A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface
Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibil...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-07-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2018.00477/full |
| _version_ | 1819236308711636992 |
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| author | Carlos Amaral Susana Mouga Susana Mouga Marco Simões Marco Simões Helena C. Pereira Inês Bernardino Hugo Quental Rebecca Playle Rachel McNamara Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Miguel Castelo-Branco Miguel Castelo-Branco Miguel Castelo-Branco |
| author_facet | Carlos Amaral Susana Mouga Susana Mouga Marco Simões Marco Simões Helena C. Pereira Inês Bernardino Hugo Quental Rebecca Playle Rachel McNamara Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Miguel Castelo-Branco Miguel Castelo-Branco Miguel Castelo-Branco |
| author_sort | Carlos Amaral |
| collection | DOAJ |
| description | Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic “Joint-attention task.” We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial.(clinical-trial ID: NCT02445625—clinicaltrials.gov). |
| first_indexed | 2024-12-23T13:02:23Z |
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| last_indexed | 2024-12-23T13:02:23Z |
| publishDate | 2018-07-01 |
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| spelling | doaj.art-8d8260a36e154d51bd10e5be0cd85ea42022-12-21T17:46:00ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-07-011210.3389/fnins.2018.00477357512A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer InterfaceCarlos Amaral0Susana Mouga1Susana Mouga2Marco Simões3Marco Simões4Helena C. Pereira5Inês Bernardino6Hugo Quental7Rebecca Playle8Rachel McNamara9Guiomar Oliveira10Guiomar Oliveira11Guiomar Oliveira12Guiomar Oliveira13Miguel Castelo-Branco14Miguel Castelo-Branco15Miguel Castelo-Branco16CNC.IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, PortugalCNC.IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, PortugalUnidade de Neurodesenvolvimento e Autismo do Serviço do Centro de Desenvolvimento da Criança, Pediatric Hospital, Centro Hospitalar e Universitário de Coimbra, Coimbra, PortugalCNC.IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, PortugalCenter for Informatics and Systems, University of Coimbra, Coimbra, PortugalCNC.IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, PortugalCNC.IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, PortugalCNC.IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, PortugalCentre for Trials Research, Cardiff University, Cardiff, WalesCentre for Trials Research, Cardiff University, Cardiff, WalesUnidade de Neurodesenvolvimento e Autismo do Serviço do Centro de Desenvolvimento da Criança, Pediatric Hospital, Centro Hospitalar e Universitário de Coimbra, Coimbra, PortugalUniversity Clinic of Pediatrics, Faculty of Medicine, University of Coimbra, Coimbra, PortugalCentro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalFaculty of Medicine, University of Coimbra, Coimbra, PortugalCIBIT, Coimbra Institute for Biomedical Imaging and Translational Research, ICNAS - Institute of Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, PortugalICNAS—Produção Unipessoal, Coimbra, PortugalDeficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic “Joint-attention task.” We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial.(clinical-trial ID: NCT02445625—clinicaltrials.gov).https://www.frontiersin.org/article/10.3389/fnins.2018.00477/fullautismclinical trialbrain-computer interfaceEEGvirtual realitysocial attention |
| spellingShingle | Carlos Amaral Susana Mouga Susana Mouga Marco Simões Marco Simões Helena C. Pereira Inês Bernardino Hugo Quental Rebecca Playle Rachel McNamara Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Guiomar Oliveira Miguel Castelo-Branco Miguel Castelo-Branco Miguel Castelo-Branco A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface Frontiers in Neuroscience autism clinical trial brain-computer interface EEG virtual reality social attention |
| title | A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface |
| title_full | A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface |
| title_fullStr | A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface |
| title_full_unstemmed | A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface |
| title_short | A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface |
| title_sort | feasibility clinical trial to improve social attention in autistic spectrum disorder asd using a brain computer interface |
| topic | autism clinical trial brain-computer interface EEG virtual reality social attention |
| url | https://www.frontiersin.org/article/10.3389/fnins.2018.00477/full |
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