Eva Velthorst1, Eric C Meyer2, Anthony J Giuliano3, Jean Addington4, Kristin S Cadenhead5, Tyrone D Cannon6, Barbara A Cornblatt7, Thomas H McGlashan8, Diana O Perkins9, Ming T Tsuang5, Elaine F Walker10, Scott W Woods8, Carrie E Bearden11, Larry J Seidman12. 1. Department of Psychiatry and Seaver Autism Center for Treatment and Research, Icahn School of Medicine at Mount Sinai, New York, USA. Electronic address: eva.velthorst@mssm.edu. 2. Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, College of Medicine, College Station, TX, USA; VA VISN 17 Center of Excellence for Research on Returning War Veterans at Central Texas Veterans Healthcare System, Waco, TX, USA; Warriors Research Institute, Baylor Scott & White Health, Waco, TX, USA. 3. Department of Psychology, Worcester Recovery Center and Hospital, Worcester, MA, USA. 4. Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada. 5. Department of Psychiatry, University of California, San Diego, CA, USA. 6. Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA. 7. Department of Psychiatry, Zucker Hillside Hospital, Long Island, NY, USA. 8. Department of Psychiatry, Yale University, New Haven, CT, USA. 9. Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA. 10. Departments of Psychology and Psychiatry, Emory University, Atlanta, GA, USA. 11. Departments of Psychiatry and Biobehavioral Sciences and Psychology, UCLA, Los Angeles, CA, USA. 12. Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School at Massachusetts General Hospital, Boston, MA, USA.
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.
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.
Authors: Barbara A Cornblatt; Ricardo E Carrión; Jean Addington; Larry Seidman; Elaine F Walker; Tyronne D Cannon; Kristin S Cadenhead; Thomas H McGlashan; Diana O Perkins; Ming T Tsuang; Scott W Woods; Robert Heinssen; Todd Lencz Journal: Schizophr Bull Date: 2011-11-10 Impact factor: 9.306
Authors: Abraham Reichenberg; Philip D Harvey; Christopher R Bowie; Ramin Mojtabai; Jonathan Rabinowitz; Robert K Heaton; Evelyn Bromet Journal: Schizophr Bull Date: 2008-05-20 Impact factor: 9.306
Authors: Scott W Woods; Barbara C Walsh; Jean Addington; Kristin S Cadenhead; Tyrone D Cannon; Barbara A Cornblatt; Robert Heinssen; Diana O Perkins; Larry J Seidman; Sarah I Tarbox; Ming T Tsuang; Elaine F Walker; Thomas H McGlashan Journal: Schizophr Res Date: 2014-07-08 Impact factor: 4.939
Authors: Anthony O Ahmed; Gregory P Strauss; Robert W Buchanan; Brian Kirkpatrick; William T Carpenter Journal: J Psychiatr Res Date: 2017-11-12 Impact factor: 4.791
Authors: Barbara A Cornblatt; Andrea M Auther; Tara Niendam; Christopher W Smith; Jamie Zinberg; Carrie E Bearden; Tyrone D Cannon Journal: Schizophr Bull Date: 2007-04-17 Impact factor: 9.306
Authors: Tyrone D Cannon; Kristin Cadenhead; Barbara Cornblatt; Scott W Woods; Jean Addington; Elaine Walker; Larry J Seidman; Diana Perkins; Ming Tsuang; Thomas McGlashan; Robert Heinssen Journal: Arch Gen Psychiatry Date: 2008-01
Authors: Caroline Ranem Mohn-Haugen; Christine Mohn; Frank Larøi; Charlotte M Teigset; Merete Glenne Øie; Bjørn Rishovd Rund Journal: Schizophr Res Cogn Date: 2022-03-01
Authors: Kate Haining; Ruchika Gajwani; Joachim Gross; Andrew I Gumley; Robin A A Ince; Stephen M Lawrie; Frauke Schultze-Lutter; Matthias Schwannauer; Peter J Uhlhaas Journal: Eur Arch Psychiatry Clin Neurosci Date: 2021-08-16 Impact factor: 5.270
Authors: Sylvia Romanowska; Michael W Best; Christopher R Bowie; Colin A Depp; Thomas L Patterson; David L Penn; Amy E Pinkham; Philip D Harvey Journal: Schizophr Res Cogn Date: 2022-04-26
Authors: Seenae Eum; S Kristian Hill; Ney Alliey-Rodriguez; James M Stevenson; Leah H Rubin; Adam M Lee; Lauren J Mills; James L Reilly; Rebekka Lencer; Sarah K Keedy; Elena Ivleva; Richard S E Keefe; Godfrey D Pearlson; Brett A Clementz; Carol A Tamminga; Matcheri S Keshavan; Elliot S Gershon; John A Sweeney; Jeffrey R Bishop Journal: Neuropsychopharmacology Date: 2021-06-18 Impact factor: 8.294