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Identifying overlapping and distinctive traits of autism and schizophrenia using machine learning classification.

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Title: Identifying overlapping and distinctive traits of autism and schizophrenia using machine learning classification.
Authors: Pablo, Jenna N. (AUTHOR), Shires, Jorja (AUTHOR), Torrens, Wendy A. (AUTHOR), Kemmelmeier, Lena L. (AUTHOR), Haigh, Sarah M. (AUTHOR), Berryhill, Marian E. (AUTHOR)
Source: Cognitive Neuropsychiatry. Mar2025, Vol. 30 Issue 2, p69-91. 23p.
Subjects: Autism spectrum disorders, Schizophrenia, Personality questionnaires, Neuropsychological tests, Machine learning
Abstract: Introduction: Autism spectrum disorder (ASD) and schizophrenia spectrum disorder (SSD) share some symptoms. We conducted machine learning classification to determine if common screeners used for research in non-clinical and subclinical populations, the Autism-Spectrum Quotient (AQ) and Schizotypal Personality Questionnaire – Brief Revised (SPQ-BR), could identify non-overlapping symptoms. Methods: 1,397 undergraduates completed the SPQ-BR and AQ. Random forest classification modelled whether SPQ-BR item scores predicted AQ scores and factors, and vice versa. The models first used all item scores and then the least/most important features. Results: Robust trait overlap allows for the prediction of AQ from SPQ-BR and vice versa. Results showed that AQ item scores predicted 2 of 3 SPQ-BR factors (disorganised, interpersonal), and SPQ-BR item scores successfully predicted 2 of 5 AQ factors (communication, social skills). Importantly, classification model failures showed that AQ item scores could not predict the SPQ-BR cognitive-perceptual factor, and the SPQ-BR item scores could not predict 3 AQ factors (imagination, attention to detail, attention switching). Conclusions: Overall, the SPQ-BR and AQ measure overlapping symptoms that can be isolated to some factors. Importantly, where we observe model failures, we capture distinctive factors. We provide guidance for leveraging existing screeners to avert misdiagnosis and advancing specific/selective biomarker identification. [ABSTRACT FROM AUTHOR]
Copyright of Cognitive Neuropsychiatry is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Identifying overlapping and distinctive traits of autism and schizophrenia using machine learning classification.
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  Data: <searchLink fieldCode="AR" term="%22Pablo%2C+Jenna+N%2E%22">Pablo, Jenna N.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shires%2C+Jorja%22">Shires, Jorja</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Torrens%2C+Wendy+A%2E%22">Torrens, Wendy A.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kemmelmeier%2C+Lena+L%2E%22">Kemmelmeier, Lena L.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haigh%2C+Sarah+M%2E%22">Haigh, Sarah M.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Berryhill%2C+Marian+E%2E%22">Berryhill, Marian E.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Cognitive+Neuropsychiatry%22">Cognitive Neuropsychiatry</searchLink>. Mar2025, Vol. 30 Issue 2, p69-91. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Autism+spectrum+disorders%22">Autism spectrum disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Schizophrenia%22">Schizophrenia</searchLink><br /><searchLink fieldCode="DE" term="%22Personality+questionnaires%22">Personality questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Neuropsychological+tests%22">Neuropsychological tests</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Introduction: Autism spectrum disorder (ASD) and schizophrenia spectrum disorder (SSD) share some symptoms. We conducted machine learning classification to determine if common screeners used for research in non-clinical and subclinical populations, the Autism-Spectrum Quotient (AQ) and Schizotypal Personality Questionnaire – Brief Revised (SPQ-BR), could identify non-overlapping symptoms. Methods: 1,397 undergraduates completed the SPQ-BR and AQ. Random forest classification modelled whether SPQ-BR item scores predicted AQ scores and factors, and vice versa. The models first used all item scores and then the least/most important features. Results: Robust trait overlap allows for the prediction of AQ from SPQ-BR and vice versa. Results showed that AQ item scores predicted 2 of 3 SPQ-BR factors (disorganised, interpersonal), and SPQ-BR item scores successfully predicted 2 of 5 AQ factors (communication, social skills). Importantly, classification model failures showed that AQ item scores could not predict the SPQ-BR cognitive-perceptual factor, and the SPQ-BR item scores could not predict 3 AQ factors (imagination, attention to detail, attention switching). Conclusions: Overall, the SPQ-BR and AQ measure overlapping symptoms that can be isolated to some factors. Importantly, where we observe model failures, we capture distinctive factors. We provide guidance for leveraging existing screeners to avert misdiagnosis and advancing specific/selective biomarker identification. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Cognitive Neuropsychiatry is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2025
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