IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0113879.html
   My bibliography  Save this article

Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism

Author

Listed:
  • Marcel Adam Just
  • Vladimir L Cherkassky
  • Augusto Buchweitz
  • Timothy A Keller
  • Tom M Mitchell

Abstract

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought’s underlying brain activation patterns.

Suggested Citation

  • Marcel Adam Just & Vladimir L Cherkassky & Augusto Buchweitz & Timothy A Keller & Tom M Mitchell, 2014. "Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.
    • Handle: RePEc:plo:pone00:0113879
    DOI: 10.1371/journal.pone.0113879
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113879
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0113879&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0113879?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Svetlana V Shinkareva & Robert A Mason & Vicente L Malave & Wei Wang & Tom M Mitchell & Marcel Adam Just, 2008. "Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-9, January.
    2. Karim S Kassam & Amanda R Markey & Vladimir L Cherkassky & George Loewenstein & Marcel Adam Just, 2013. "Identifying Emotions on the Basis of Neural Activation," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    3. Marcel Adam Just & Vladimir L Cherkassky & Sandesh Aryal & Tom M Mitchell, 2010. "A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-15, January.
    4. Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bin Liu & Ramesh Govindan & Brian Uzzi, 2016. "Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-11, January.
    2. Umut Güçlü & Marcel A J van Gerven, 2014. "Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-12, August.
    3. Guillermo A Cecchi & Lejian Huang & Javeria Ali Hashmi & Marwan Baliki & María V Centeno & Irina Rish & A Vania Apkarian, 2012. "Predictive Dynamics of Human Pain Perception," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
    4. Zvi N. Roth & Kendrick Kay & Elisha P. Merriam, 2022. "Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Hamed Nili & Cai Wingfield & Alexander Walther & Li Su & William Marslen-Wilson & Nikolaus Kriegeskorte, 2014. "A Toolbox for Representational Similarity Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
    6. Hamed Nili & Alexander Walther & Arjen Alink & Nikolaus Kriegeskorte, 2020. "Inferring exemplar discriminability in brain representations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    7. Jacob M. Paul & Martijn Ackooij & Tuomas C. Cate & Ben M. Harvey, 2022. "Numerosity tuning in human association cortices and local image contrast representations in early visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Agustin Lage-Castellanos & Giancarlo Valente & Elia Formisano & Federico De Martino, 2019. "Methods for computing the maximum performance of computational models of fMRI responses," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-25, March.
    9. Kay H Brodersen & Thomas M Schofield & Alexander P Leff & Cheng Soon Ong & Ekaterina I Lomakina & Joachim M Buhmann & Klaas E Stephan, 2011. "Generative Embedding for Model-Based Classification of fMRI Data," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-19, June.
    10. Shinsuke Koyama & Uri Eden & Emery Brown & Robert Kass, 2010. "Bayesian decoding of neural spike trains," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 37-59, February.
    11. Kiyohito Iigaya & Sanghyun Yi & Iman A. Wahle & Sandy Tanwisuth & Logan Cross & John P. O’Doherty, 2023. "Neural mechanisms underlying the hierarchical construction of perceived aesthetic value," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    12. Dinarte Diaz,Lelys Ileana, 2020. "Peer Effects on Violence : Experimental Evidence from El Salvador," Policy Research Working Paper Series 9187, The World Bank.
    13. Ming Bo Cai & Nicolas W Schuck & Jonathan W Pillow & Yael Niv, 2019. "Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-30, May.
    14. Ghislain St-Yves & Emily J. Allen & Yihan Wu & Kendrick Kay & Thomas Naselaris, 2023. "Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    15. Hanzhong Liu & Bin Yu, 2017. "Comments on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 740-750, December.
    16. Bono, Filippa & Giacomarra, Marcella, 2016. "The photovoltaic growth in the European Union requires stronger RES support," Journal of Policy Modeling, Elsevier, vol. 38(2), pages 324-339.
    17. Marcel Adam Just & Vladimir L Cherkassky & Sandesh Aryal & Tom M Mitchell, 2010. "A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-15, January.
    18. Raheel Zafar & Sarat C Dass & Aamir Saeed Malik, 2017. "Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    19. Tarana Nigam & Caspar M. Schwiedrzik, 2024. "Predictions enable top-down pattern separation in the macaque face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    20. Samy A. Abdel-Ghaffar & Alexander G. Huth & Mark D. Lescroart & Dustin Stansbury & Jack L. Gallant & Sonia J. Bishop, 2024. "Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0113879. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.