期刊
SCIENTIFIC REPORTS
卷 6, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/srep35333
关键词
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资金
- National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England at Cambridgeshire and Peterborough NHS Foundation Trust
- Innovative Medicines Initiative Joint Undertaking [115300]
- European Union
- EFPIA companies
- Autism Speaks
- UK Medical Research Council (MRC) [G0600977]
- Wellcome Trust [091774/Z/10/Z]
- Autism Research Trust (ART)
- William Binks Autism Neuroscience Fellowship at the University of Cambridge
- O'Brien Scholars Program within the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto
- Medical Research Council [G9817803, G9817803B, G1000183, G0600977, G1000183B, MR/N026063/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0513-10051, NF-SI-0515-10097, RP-PG-0606-1045] Funding Source: researchfish
- MRC [G1000183, G9817803, MR/N026063/1, G0600977] Funding Source: UKRI
- Wellcome Trust [091774/Z/10/Z] Funding Source: Wellcome Trust
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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