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; MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happé, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng-Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G. M. Murphy, Greg Pasco, Amber N. V. Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams & C. Ellie Wilson; Christine Ecker; Michael C. Craig; Declan G M Murphy; Francesca Happe; Simon Baron-Cohen; Simon Baron-Cohen; Patrick F. Bolton

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, MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happé, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng-Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G. M. Murphy, Greg Pasco, Amber N. V. Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams & C. Ellie Wilson, Christine Ecker, Michael C. Craig, Declan G M Murphy, Francesca Happe, Simon Baron-Cohen, Simon Baron-Cohen, Patrick F. Bolton

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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'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
Early online date	18 Oct 2016
DOIs	https://doi.org/10.1038/srep35333
Publication status	E-pub ahead of print - 18 Oct 2016

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Unsupervised data-driven_LOMBARDO_Accepted12Sep2016_GOLD VoRFinal published version, 2.24 MBLicence: CC BY

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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., MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happé, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng-Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G. M. Murphy, Greg Pasco, Amber N. V. Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams & C. Ellie Wilson, Ecker, C., Craig, M. C., Murphy, D. G. M., Happe, F., Baron-Cohen, S., Baron-Cohen, S., & Bolton, P. F. (2016). Unsupervised data-driven stratification of mentalizing heterogeneity in autism. Scientific Reports, 6, Article 35333 . Advance online publication. https://doi.org/10.1038/srep35333

@article{3c703a54e4e646dc9ac3908062480905,

title = "Unsupervised data-driven stratification of mentalizing heterogeneity in autism",

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.",

author = "Lombardo, {Michael V.} and Lai, {Meng Chuan} and Bonnie Auyeung and Holt, {Rosemary J.} and Carrie Allison and Paula Smith and Bhismadev Chakrabarti and Ruigrok, {Amber N V} and John Suckling and Bullmore, {Edward T.} and {MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happ{\'e}, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng-Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G. M. Murphy, Greg Pasco, Amber N. V. Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams & C. Ellie Wilson} and Christine Ecker and Craig, {Michael C.} and Murphy, {Declan G M} and Francesca Happe and Simon Baron-Cohen and Simon Baron-Cohen and Bolton, {Patrick F.}",

year = "2016",

month = oct,

day = "18",

doi = "10.1038/srep35333",

language = "English",

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journal = "Scientific Reports",

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Lombardo, MV, Lai, MC, Auyeung, B, Holt, RJ, Allison, C, Smith, P, Chakrabarti, B, Ruigrok, ANV, Suckling, J, Bullmore, ET, MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happé, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng-Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G. M. Murphy, Greg Pasco, Amber N. V. Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams & C. Ellie Wilson, Ecker, C , Craig, MC , Murphy, DGM , Happe, F, Baron-Cohen, S, Baron-Cohen, S & Bolton, PF 2016, 'Unsupervised data-driven stratification of mentalizing heterogeneity in autism', Scientific Reports, vol. 6, 35333 . https://doi.org/10.1038/srep35333

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AU - Lombardo, Michael V.

AU - Lai, Meng Chuan

AU - Auyeung, Bonnie

AU - Holt, Rosemary J.

AU - Allison, Carrie

AU - Smith, Paula

AU - Chakrabarti, Bhismadev

AU - Ruigrok, Amber N V

AU - Suckling, John

AU - Bullmore, Edward T.

AU - MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happé, Julian Henty, Pe

AU - Ecker, Christine

AU - Craig, Michael C.

AU - Murphy, Declan G M

AU - Happe, Francesca

AU - Baron-Cohen, Simon

AU - Bolton, Patrick F.

PY - 2016/10/18

Y1 - 2016/10/18

N2 - 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.

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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U2 - 10.1038/srep35333

DO - 10.1038/srep35333

M3 - Article

SN - 2045-2322

VL - 6

JO - Scientific Reports

JF - Scientific Reports

M1 - 35333

ER -

Unsupervised data-driven stratification of mentalizing heterogeneity in autism

Abstract

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