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Predicting psychosis risk using a specific measure of cognitive control: a 12-month longitudinal study

Published online by Cambridge University Press:  11 September 2019

Joyce Y. Guo Open the ORCID record for Joyce Y. Guo [Opens in a new window] ,
Tara A. Niendam ,
Andrea M. Auther ,
Ricardo E. Carrión ,
Barbara A. Cornblatt ,
J. Daniel Ragland ,
Steven Adelsheim ,
Roderick Calkins ,
Tamara G. Sale  and
Stephan F. Taylor
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Joyce Y. Guo*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Imaging Research Center, the University of California at Davis, Sacramento, CA, USA Department of Psychology, Center for Neuroscience, the University of California at Davis, Davis, CA, USA
Tara A. Niendam
Affiliation:
Department of Psychiatry and Behavioral Sciences, Imaging Research Center, the University of California at Davis, Sacramento, CA, USA
Andrea M. Auther
Affiliation:
Division of Psychiatry Research, The Zucker Hillside Hospital, North Shore – Long Island Jewish Health System (NS-LIJHS), Glen Oaks, NY, USA
Ricardo E. Carrión
Affiliation:
Division of Psychiatry Research, The Zucker Hillside Hospital, North Shore – Long Island Jewish Health System (NS-LIJHS), Glen Oaks, NY, USA
Barbara A. Cornblatt
Affiliation:
Division of Psychiatry Research, The Zucker Hillside Hospital, North Shore – Long Island Jewish Health System (NS-LIJHS), Glen Oaks, NY, USA
J. Daniel Ragland
Affiliation:
Department of Psychiatry and Behavioral Sciences, Imaging Research Center, the University of California at Davis, Sacramento, CA, USA
Steven Adelsheim
Affiliation:
Stanford University, Boston, USA
Roderick Calkins
Affiliation:
Mid-Valley Behavioral Care Network, Marion County Health Department, Salem, Oregon, USA
Tamara G. Sale
Affiliation:
Regional Research Institute for Human Services, Portland State University, Oregon, USA
Stephan F. Taylor
Affiliation:
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
William R. McFarlane
Affiliation:
Regional Research Institute for Human Services, Portland State University, Oregon, USA Tufts University School of Medicine, Boston, MA, USA
Cameron S. Carter
Affiliation:
Department of Psychiatry and Behavioral Sciences, Imaging Research Center, the University of California at Davis, Sacramento, CA, USA Department of Psychology, Center for Neuroscience, the University of California at Davis, Davis, CA, USA
*
Author for correspondence: Joyce Y. Guo, E-mail: [email protected]
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Abstract

Background

Identifying risk factors of individuals in a clinical-high-risk state for psychosis are vital to prevention and early intervention efforts. Among prodromal abnormalities, cognitive functioning has shown intermediate levels of impairment in CHR relative to first-episode psychosis and healthy controls, highlighting a potential role as a risk factor for transition to psychosis and other negative clinical outcomes. The current study used the AX-CPT, a brief 15-min computerized task, to determine whether cognitive control impairments in CHR at baseline could predict clinical status at 12-month follow-up.

Methods

Baseline AX-CPT data were obtained from 117 CHR individuals participating in two studies, the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP) and the Understanding Early Psychosis Programs (EP) and used to predict clinical status at 12-month follow-up. At 12 months, 19 individuals converted to a first episode of psychosis (CHR-C), 52 remitted (CHR-R), and 46 had persistent sub-threshold symptoms (CHR-P). Binary logistic regression and multinomial logistic regression were used to test prediction models.

Results

Baseline AX-CPT performance (d-prime context) was less impaired in CHR-R compared to CHR-P and CHR-C patient groups. AX-CPT predictive validity was robust (0.723) for discriminating converters v. non-converters, and even greater (0.771) when predicting CHR three subgroups.

Conclusions

These longitudinal outcome data indicate that cognitive control deficits as measured by AX-CPT d-prime context are a strong predictor of clinical outcome in CHR individuals. The AX-CPT is brief, easily implemented and cost-effective measure that may be valuable for large-scale prediction efforts.

Keywords

AX-CPTclinical-high-risk for psychosiscognitive impairments
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 13 , October 2020 , pp. 2230 - 2239
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Copyright
Copyright © Cambridge University Press 2019

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Footnotes

*

Co-first Joyce Y. Guo & Tara A. Niendam provided equal contributions to this manuscript.

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