Neurocognitive profiles in the prodrome to psychosis in NAPLS-1

Eva Velthorst, Eric C. Meyer, Anthony J. Giuliano, Jean Addington, Kristin S. Cadenhead, Tyrone D. Cannon, Barbara A. Cornblatt, Thomas H. McGlashan, Diana O. Perkins, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods, Carrie E. Bearden, Larry J. Seidman

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Background: Most studies of neurocognitive functioning in Clinical High Risk (CHR) cohorts have examined group averages, likely concealing heterogeneous subgroups. We aimed to identify neurocognitive subgroups and to explore associated outcomes. Methods: Data were acquired from 324 participants (mean age 18.4) in the first phase of the North American Prodrome Longitudinal Study (NAPLS-1), a multi-site consortium following individuals for up to 2 1/2 years. We applied Ward's method for hierarchical clustering data to 8 baseline neurocognitive measures, in 166 CHR individuals, 49 non-CHR youth with a family history of psychosis, and 109 healthy controls. We tested whether cluster membership was associated with conversion to psychosis, social and role functioning, and follow-up diagnosis. Analyses were repeated after data were clustered based on independently developed clinical decision rules. Results: Four neurocognitive clusters were identified: Significantly Impaired (n = 33); Mildly Impaired (n = 82); Normal (n = 145) and High (n = 64). The Significantly Impaired subgroup demonstrated the largest deviations on processing speed and memory tasks and had a conversion rate of 58%, a 40% chance of developing a schizophrenia spectrum diagnosis (compared to 24.4% in the Mildly Impaired, and 10.3% in the other two groups combined), and significantly worse functioning at baseline and 12-months. Data clustered using clinical decision rules yielded similar results, pointing to high convergent validity. Conclusion: Neurocognitive profiles vary substantially in their severity and are associated with diagnostic and functional outcome, underscoring neurocognition as a predictor of illness outcomes. These findings, if replicated, are a first step toward personalized treatment for individuals at-risk for psychosis.

Original languageEnglish
Pages (from-to)311-319
Number of pages9
JournalSchizophrenia Research
Volume204
DOIs
StatePublished - Feb 2019

Keywords

  • Clinical High Risk
  • Cluster analysis
  • Functional outcome
  • Heterogeneity
  • Neuropsychology

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