Identifying endophenotypes of autism: A multivariate approach
The existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC that are also present when comparing controls and the unaffected siblings of ASC individuals. In t...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-06-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00060/full |
| _version_ | 1811279633689083904 |
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| author | Fermín eSegovia Fermín eSegovia Rosemary eHolt Michael eSpencer Juan Manuel Górriz Javier eRamírez Carlos G. Puntonet Christophe ePhillips Lindsay eChura Simon eBaron-Cohen John eSuckling |
| author_facet | Fermín eSegovia Fermín eSegovia Rosemary eHolt Michael eSpencer Juan Manuel Górriz Javier eRamírez Carlos G. Puntonet Christophe ePhillips Lindsay eChura Simon eBaron-Cohen John eSuckling |
| author_sort | Fermín eSegovia |
| collection | DOAJ |
| description | The existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC that are also present when comparing controls and the unaffected siblings of ASC individuals. In this work, we used a multivariate methodology applied on magnetic resonance images to look for such differences. The proposed procedure consists of combining a searchlight approach and a support vector machine classifier to identify the differences between three groups of participants in pairwise comparisons: controls, people with ASC and their unaffected siblings. Then we compared those differences selecting spatially collocated as candidate endophenotypes of ASC. |
| first_indexed | 2024-04-13T00:59:13Z |
| format | Article |
| id | doaj.art-e2cd38d71c574fea9064f171c3b36e20 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-13T00:59:13Z |
| publishDate | 2014-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-e2cd38d71c574fea9064f171c3b36e202022-12-22T03:09:33ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882014-06-01810.3389/fncom.2014.0006086657Identifying endophenotypes of autism: A multivariate approachFermín eSegovia0Fermín eSegovia1Rosemary eHolt2Michael eSpencer3Juan Manuel Górriz4Javier eRamírez5Carlos G. Puntonet6Christophe ePhillips7Lindsay eChura8Simon eBaron-Cohen9John eSuckling10University of LiègeUniversity of CambridgeUniversity of CambridgeUniversity of CambridgeUniversity of GranadaUniversity of GranadaUniversity of GranadaUniversity of LiègeUniversity of CambridgeUniversity of CambridgeUniversity of CambridgeThe existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC that are also present when comparing controls and the unaffected siblings of ASC individuals. In this work, we used a multivariate methodology applied on magnetic resonance images to look for such differences. The proposed procedure consists of combining a searchlight approach and a support vector machine classifier to identify the differences between three groups of participants in pairwise comparisons: controls, people with ASC and their unaffected siblings. Then we compared those differences selecting spatially collocated as candidate endophenotypes of ASC.http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00060/fullMRIAutism Spectrum DisorderendophenotypeSupport vector machinesearchlight |
| spellingShingle | Fermín eSegovia Fermín eSegovia Rosemary eHolt Michael eSpencer Juan Manuel Górriz Javier eRamírez Carlos G. Puntonet Christophe ePhillips Lindsay eChura Simon eBaron-Cohen John eSuckling Identifying endophenotypes of autism: A multivariate approach Frontiers in Computational Neuroscience MRI Autism Spectrum Disorder endophenotype Support vector machine searchlight |
| title | Identifying endophenotypes of autism: A multivariate approach |
| title_full | Identifying endophenotypes of autism: A multivariate approach |
| title_fullStr | Identifying endophenotypes of autism: A multivariate approach |
| title_full_unstemmed | Identifying endophenotypes of autism: A multivariate approach |
| title_short | Identifying endophenotypes of autism: A multivariate approach |
| title_sort | identifying endophenotypes of autism a multivariate approach |
| topic | MRI Autism Spectrum Disorder endophenotype Support vector machine searchlight |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00060/full |
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