Unsupervised data-driven stratification of mentalizing heterogeneity in autism

Michael V.  Lombardo; Meng-Chuan Lai; Bonnie Auyeung; Rosemary J. Holt; Carrie Allison; Paula Smith; Bhismadev Chakrabarti; Amber N. V. Ruigrok; John Suckling; Edward T. Bullmore; Christine Ecker; Michael Craig; Declan G M Murphy; Francesca Happé; Simon Baron-Cohen

doi:10.1038/srep35333

Unsupervised data-driven stratification of mentalizing heterogeneity in autism

Michael V. Lombardo, Meng-Chuan Lai, Bonnie Auyeung, Rosemary J. Holt, Carrie Allison, Paula Smith, Bhismadev Chakrabarti, Amber N. V. Ruigrok, John Suckling, Edward T. Bullmore, Christine Ecker, Michael Craig, Declan G M Murphy, Francesca Happé, Simon Baron-Cohen

Research output: Contribution to journal › Article › peer-review

Abstract

Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n=694; n=249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen’s d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals.

Original language	English
Article number	35333
Journal	Scientific Reports
Volume	6
DOIs	https://doi.org/10.1038/srep35333
Publication status	Published - 18 Oct 2016

Keywords / Materials (for Non-textual outputs)

autism
clustering
precision medicine
mentalizing
emotion recognition
heterogeneity

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Lombardo-etal-2016-SR-Unsupervised-data-driven-stratificationFinal published version, 2.24 MBLicence: Creative Commons: Attribution (CC-BY)

Bonnie Auyeung
- bonnie.auyeunged.acuk
- School of Philosophy, Psychology and Language Sciences - Personal Chair of Child Health and Developmental Science
- Edinburgh Neuroscience
Person: Academic: Research Active

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Lombardo, M. V., Lai, M.-C., Auyeung, B., Holt, R. J., Allison, C., Smith, P., Chakrabarti, B., Ruigrok, A. N. V., Suckling, J., Bullmore, E. T., Ecker, C., Craig, M., Murphy, D. G. M., Happé, F., & Baron-Cohen, S. (2016). Unsupervised data-driven stratification of mentalizing heterogeneity in autism. Scientific Reports, 6, Article 35333. https://doi.org/10.1038/srep35333

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abstract = "Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n=694; n=249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62\% of ASC adults show evidence for large impairments (Cohen{\textquoteright}s d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals. ",

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AU - Lai, Meng-Chuan

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AU - Allison, Carrie

AU - Smith, Paula

AU - Chakrabarti, Bhismadev

AU - Ruigrok, Amber N. V.

AU - Suckling, John

AU - Bullmore, Edward T.

AU - Ecker, Christine

AU - Craig, Michael

AU - Murphy, Declan G M

AU - Happé, Francesca

AU - Baron-Cohen, Simon

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AB - Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n=694; n=249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen’s d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals.

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KW - clustering

KW - precision medicine

KW - mentalizing

KW - emotion recognition

KW - heterogeneity

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JO - Scientific Reports

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Unsupervised data-driven stratification of mentalizing heterogeneity in autism

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Keywords / Materials (for Non-textual outputs)

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