[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.
Neuropsychological Profile by Diagnostic Group Adjusted for Age, Site, and Maternal Education
Neuropsychological Profile by Diagnostic Group Adjusted for Age, Site, and Maternal Education

Marginal means from mixed models were standardized with control means (SDs) to convert to z score. Error bars denote SEMs within groups. CHR + Cs indicates clinical high-risk converters to psychosis; CHR − NCs, clinical high-risk nonconverters; and HCs, healthy controls.

Figure 2.
Effect Sizes (Cohen d) for Individual Tests Adjusted for Age, Site, and Maternal Education for Clinical High-Risk Converters to Psychosis (CHR + Cs), Clinical High-Risk Nonconverters (CHR − NCs), and Healthy Controls (HCs)
Effect Sizes (Cohen d) for Individual Tests Adjusted for Age, Site, and Maternal Education for Clinical High-Risk Converters to Psychosis (CHR + Cs), Clinical High-Risk Nonconverters (CHR − NCs), and Healthy Controls (HCs)

Effect sizes are rank ordered from largest to smallest. ACPT indicates Auditory Continuous Performance Test (QA are simply the letters; Q3A, the number of letters between an “A” and a “Q”); BACS, Brief Assessment of Cognition in Schizophrenia symbol coding; BVMT-R, Brief Visuospatial Memory Test–Revised; CPT-IP, Continuous Performance Test–Identical Pairs; HVLT-R, Hopkins Verbal Learning Test–Revised; LNS, Letter Number Span; NAB, Neuropsychological Assessment Battery mazes; PAM, Paired Associate Memory; UPSIT, University of Pennsylvania Smell Identification Test; WASI, Wechsler Abbreviated Scale of Intelligence; WMS-3, Wechsler Memory Scale–Third Edition spatial span; and WRAT-4, Wide Range Achievement Test 4.

Table 1.  
Demographic and Clinical Characteristics in Clinical High-Risk (CHR) Groups and Healthy Controls (HCs)a
Demographic and Clinical Characteristics in Clinical High-Risk (CHR) Groups and Healthy Controls (HCs)a
Table 2.  
Additional Demographic and Clinical Characteristics in Clinical High-Risk (CHR) Groups and Healthy Controls (HCs)a
Additional Demographic and Clinical Characteristics in Clinical High-Risk (CHR) Groups and Healthy Controls (HCs)a
Table 3.  
Factor Analysis of 17 Test Scoresa
Factor Analysis of 17 Test Scoresa
Table 4.  
Neuropsychological Performance in Clinical High-Risk (CHR) Groups and Healthy Controls (HCs)
Neuropsychological Performance in Clinical High-Risk (CHR) Groups and Healthy Controls (HCs)
1.
Mirsky  AF.  Neuropsychological bases of schizophrenia.  Annu Rev Psychol. 1969;20:321-348.PubMedGoogle ScholarCrossref
2.
Seidman  LJ.  Schizophrenia and brain dysfunction: an integration of recent neurodiagnostic findings.  Psychol Bull. 1983;94(2):195-238.PubMedGoogle ScholarCrossref
3.
Green  MF.  What are the functional consequences of neurocognitive deficits in schizophrenia?  Am J Psychiatry. 1996;153(3):321-330.PubMedGoogle ScholarCrossref
4.
Heinrichs  RW.  The primacy of cognition in schizophrenia.  Am Psychol. 2005;60(3):229-242.PubMedGoogle ScholarCrossref
5.
Kahn  RS, Keefe  RS.  Schizophrenia is a cognitive illness: time for a change in focus.  JAMA Psychiatry. 2013;70(10):1107-1112.PubMedGoogle ScholarCrossref
6.
Lewandowski  KE, Cohen  BM, Ongur  D.  Evolution of neuropsychological dysfunction during the course of schizophrenia and bipolar disorder.  Psychol Med. 2011;41(2):225-241.PubMedGoogle ScholarCrossref
7.
Heckers  S.  What is the core of schizophrenia?  JAMA Psychiatry. 2013;70(10):1009-1010.PubMedGoogle ScholarCrossref
8.
Kraepelin  E.  Dementia Praecox and Paraphrenia. Edinburgh, Scotland: E & S Livingston; 1919.
9.
Bleuler  E.  Dementia Praecox or the Group of Schizophrenias. New York, NY: International Universities Press; 1950.
10.
Woodberry  KA, Giuliano  AJ, Seidman  LJ.  Premorbid IQ in schizophrenia: a meta-analytic review.  Am J Psychiatry. 2008;165(5):579-587.PubMedGoogle ScholarCrossref
11.
Reichenberg  A, Caspi  A, Harrington  H,  et al.  Static and dynamic cognitive deficits in childhood preceding adult schizophrenia: a 30-year study.  Am J Psychiatry. 2010;167(2):160-169.PubMedGoogle ScholarCrossref
12.
Cannon  M, Caspi  A, Moffitt  TE,  et al.  Evidence for early-childhood, pan-developmental impairment specific to schizophreniform disorder: results from a longitudinal birth cohort.  Arch Gen Psychiatry. 2002;59(5):449-456.PubMedGoogle ScholarCrossref
13.
Fusar-Poli  P, Deste  G, Smieskova  R,  et al.  Cognitive functioning in prodromal psychosis: a meta-analysis.  Arch Gen Psychiatry. 2012;69(6):562-571.PubMedGoogle ScholarCrossref
14.
Giuliano  AJ, Li  H, Mesholam-Gately  RI, Sorenson  SM, Woodberry  KA, Seidman  LJ.  Neurocognition in the psychosis risk syndrome: a quantitative and qualitative review.  Curr Pharm Des. 2012;18(4):399-415.PubMedGoogle ScholarCrossref
15.
Bora  E, Murray  RM.  Meta-analysis of cognitive deficits in ultra-high risk to psychosis and first-episode psychosis: do the cognitive deficits progress over, or after, the onset of psychosis?  Schizophr Bull. 2014;40(4):744-755.PubMedGoogle ScholarCrossref
16.
Mesholam-Gately  RI, Giuliano  AJ, Goff  KP, Faraone  SV, Seidman  LJ.  Neurocognition in first-episode schizophrenia: a meta-analytic review.  Neuropsychology. 2009;23(3):315-336.PubMedGoogle ScholarCrossref
17.
Heinrichs  RW, Zakzanis  KK.  Neurocognitive deficit in schizophrenia: a quantitative review of the evidence.  Neuropsychology. 1998;12(3):426-445.PubMedGoogle ScholarCrossref
18.
MacCabe  JH, Wicks  S, Löfving  S,  et al.  Decline in cognitive performance between ages 13 and 18 years and the risk for psychosis in adulthood: a Swedish longitudinal cohort study in males.  JAMA Psychiatry. 2013;70(3):261-270.PubMedGoogle ScholarCrossref
19.
Liu  CH, Keshavan  MS, Tronick  E, Seidman  LJ.  Perinatal risks and childhood premorbid indicators of later psychosis: next steps for early psychosocial interventions.  Schizophr Bull. 2015;41(4):801-816.PubMedGoogle ScholarCrossref
20.
McGlashan  TH, Walsh  BC, Woods  SW.  The Psychosis-Risk Syndrome: Handbook for Diagnosis and Follow-up. New York, NY: Oxford University Press Inc; 2010.
21.
Lencz  T, Smith  CW, McLaughlin  D,  et al.  Generalized and specific neurocognitive deficits in prodromal schizophrenia.  Biol Psychiatry. 2006;59(9):863-871.PubMedGoogle ScholarCrossref
22.
Seidman  LJ, Giuliano  AJ, Meyer  EC,  et al; North American Prodrome Longitudinal Study (NAPLS) Group.  Neuropsychology of the prodrome to psychosis in the NAPLS consortium: relationship to family history and conversion to psychosis.  Arch Gen Psychiatry. 2010;67(6):578-588.PubMedGoogle ScholarCrossref
23.
Frommann  I, Pukrop  R, Brinkmeyer  J,  et al.  Neuropsychological profiles in different at-risk states of psychosis: executive control impairment in the early—and additional memory dysfunction in the late—prodromal state.  Schizophr Bull. 2011;37(4):861-873.PubMedGoogle ScholarCrossref
24.
Michel  C, Ruhrmann  S, Schimmelmann  BG, Klosterkötter  J, Schultze-Lutter  F.  A stratified model for psychosis prediction in clinical practice.  Schizophr Bull. 2014;40(6):1533-1542.PubMedGoogle ScholarCrossref
25.
Nuechterlein  KH, Dawson  ME.  Information processing and attentional functioning in the developmental course of schizophrenic disorders.  Schizophr Bull. 1984;10(2):160-203.PubMedGoogle ScholarCrossref
26.
Cornblatt  BA, Keilp  JG.  Impaired attention, genetics, and the pathophysiology of schizophrenia.  Schizophr Bull. 1994;20(1):31-46.PubMedGoogle ScholarCrossref
27.
Park  S, Holzman  PS.  Schizophrenics show spatial working memory deficits.  Arch Gen Psychiatry. 1992;49(12):975-982.PubMedGoogle ScholarCrossref
28.
Park  S, Gooding  DC.  Working memory impairment as an endophenotypic marker of a schizophrenia diathesis.  Schizophr Res Cogn. 2014;1(3):127-136.PubMedGoogle ScholarCrossref
29.
Tamminga  CA, Stan  AD, Wagner  AD.  The hippocampal formation in schizophrenia.  Am J Psychiatry. 2010;167(10):1178-1193.PubMedGoogle ScholarCrossref
30.
Saykin  AJ, Shtasel  DL, Gur  RE,  et al.  Neuropsychological deficits in neuroleptic naive patients with first-episode schizophrenia.  Arch Gen Psychiatry. 1994;51(2):124-131.PubMedGoogle ScholarCrossref
31.
Aleman  A, Hijman  R, de Haan  EHF, Kahn  RS.  Memory impairment in schizophrenia: a meta-analysis.  Am J Psychiatry. 1999;156(9):1358-1366.PubMedGoogle Scholar
32.
Brewer  WJ, Wood  SJ, McGorry  PD,  et al.  Impairment of olfactory identification ability in individuals at ultra-high risk for psychosis who later develop schizophrenia.  Am J Psychiatry. 2003;160(10):1790-1794.PubMedGoogle ScholarCrossref
33.
Turetsky  BI, Kamath  V, Calkins  ME,  et al.  Olfaction and schizophrenia clinical risk status: just the facts.  Schizophr Res. 2012;139(1-3):260-261.PubMedGoogle ScholarCrossref
34.
Dickinson  D, Ramsey  ME, Gold  JM.  Overlooking the obvious: a meta-analytic comparison of digit symbol coding tasks and other cognitive measures in schizophrenia.  Arch Gen Psychiatry. 2007;64(5):532-542.PubMedGoogle ScholarCrossref
35.
Schaefer  J, Giangrande  E, Weinberger  DR, Dickinson  D.  The global cognitive impairment in schizophrenia: consistent over decades and around the world.  Schizophr Res. 2013;150(1):42-50.PubMedGoogle ScholarCrossref
36.
Woodberry  KA, Seidman  LJ, Giuliano  AJ, Verdi  MB, Cook  WL, McFarlane  WR.  Neuropsychological profiles in individuals at clinical high risk for psychosis: relationship to psychosis and intelligence.  Schizophr Res. 2010;123(2-3):188-198.PubMedGoogle ScholarCrossref
37.
Woods  SW, Addington  J, Bearden  CE,  et al.  Psychotropic medication use in youth at high risk for psychosis: comparison of baseline data from two research cohorts 1998-2005 and 2008-2011.  Schizophr Res. 2013;148(1-3):99-104.PubMedGoogle ScholarCrossref
38.
McGrath  LM, Braaten  EB, Doty  ND,  et al.  Extending the “cross-disorder” relevance of executive functions to dimensional neuropsychiatric traits in youth.  J Child Psychol Psychiatry. 2016;57(4):462-471. PubMedGoogle ScholarCrossref
39.
Hill  SK, Reilly  JL, Keefe  RSE,  et al.  Neuropsychological impairments in schizophrenia and psychotic bipolar disorder: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study.  Am J Psychiatry. 2013;170(11):1275-1284.PubMedGoogle ScholarCrossref
40.
Keefe  RS, Perkins  DO, Gu  H, Zipursky  RB, Christensen  BK, Lieberman  JA.  A longitudinal study of neurocognitive function in individuals at-risk for psychosis.  Schizophr Res. 2006;88(1-3):26-35.PubMedGoogle ScholarCrossref
41.
Riecher-Rössler  A, Pflueger  MO, Aston  J,  et al.  Efficacy of using cognitive status in predicting psychosis: a 7-year follow-up.  Biol Psychiatry. 2009;66(11):1023-1030.PubMedGoogle ScholarCrossref
42.
Addington  J, Cadenhead  KS, Cornblatt  BA,  et al.  North American Prodrome Longitudinal Study (NAPLS 2): overview and recruitment.  Schizophr Res. 2012;142(1-3):77-82.PubMedGoogle ScholarCrossref
43.
Addington  J, Liu  L, Buchy  L,  et al.  North American Prodrome Longitudinal Study (NAPLS 2): the prodromal symptoms.  J Nerv Ment Dis. 2015;203(5):328-335.PubMedGoogle ScholarCrossref
44.
Spitzer  RL, Williams  JB, Gibbon  M.  Instruction Manual for the Structured Clinical Interview for DSM-IV. New York: Biometrics Research Dept, New York State Psychiatric Institute; 1994.
45.
Cannon  TD, Yu  C, Addington  J,  et al.  An individualized risk calculator for research in prodromal psychosis [published online July 1, 2016].  Am J Psychiatry. PubMedGoogle Scholar
46.
Drake  RE, Mueser  K, McHugo  G. Clinical rating scales. In: Sederer  L, Dickey  B, eds.  Outcomes Assessment in Clinical Practice. Baltimore, MD: Williams & Wilkins; 1996:113-116.
47.
Addington  D, Addington  J, Maticka-Tyndale  E.  Assessing depression in schizophrenia: the Calgary Depression Scale.  Br J Psychiatry Suppl. 1993;163(22):39-44.PubMedGoogle Scholar
48.
Addington  J, Shah  H, Liu  L, Addington  D.  Reliability and validity of the Calgary Depression Scale for Schizophrenia (CDSS) in youth at clinical high risk for psychosis.  Schizophr Res. 2014;153(1-3):64-67.PubMedGoogle ScholarCrossref
49.
Nuechterlein  KH, Green  MF.  MATRICS Consensus Cognitive Battery. Los Angeles, CA: MATRICS Assessment Inc; 2006.
50.
Nuechterlein  KH, Green  MF, Kern  RS,  et al.  The MATRICS Consensus Cognitive Battery, part 1: test selection, reliability, and validity.  Am J Psychiatry. 2008;165(2):203-213.PubMedGoogle ScholarCrossref
51.
Kern  RS, Nuechterlein  KH, Green  MF,  et al.  The MATRICS Consensus Cognitive Battery, part 2: co-norming and standardization.  Am J Psychiatry. 2008;165(2):214-220.PubMedGoogle ScholarCrossref
52.
McCleery  A, Green  MF, Hellemann  GS,  et al.  Latent structure of cognition in schizophrenia: a confirmatory factor analysis of the MATRICS Consensus Cognitive Battery (MCCB).  Psychol Med. 2015;45(12):2657-2666.PubMedGoogle ScholarCrossref
53.
Wechsler  D.  WASI Manual. San Antonio, CA: Psychological Corp, Harcourt Brace & Co; 1999.
54.
Wilkinson  GS, Robertson  GJ.  The Wide Range Achievement Test Administration Manual. 4th ed. Wilmington, DE: Wide Range Inc; 2006.
55.
Hoffman  RE, Woods  SW, Hawkins  KA,  et al.  Extracting spurious messages from noise and risk of schizophrenia-spectrum disorders in a prodromal population.  Br J Psychiatry. 2007;191(4):355-356.PubMedGoogle ScholarCrossref
56.
Doty  RL.  The Smell Identification Test TM Administration Manual. 3rd ed. Haddon Heights, NJ: Sensonics Inc; 1995.
57.
Jacobson  S, van Erp  TG, Karlsgodt  K, Torre  J, Bearden  CE, Cannon  TD. Reduced temporo-limbic engagement during encoding of word pairs in an associative memory task in the psychosis prodrome: baseline fMRI findings from the North American Prodrome Longitudinal Study (NAPLS). Paper presented at: 13th International Congress on Schizophrenia Research (IC0SR); April 2-6, 2011; Colorado Springs, CO.
58.
Seidman  LJ, Breiter  HC, Goodman  JM,  et al.  A functional magnetic resonance imaging study of auditory vigilance with low and high information processing demands.  Neuropsychology. 1998;12(4):505-518.PubMedGoogle ScholarCrossref
59.
Seidman  LJ, Meyer  EC, Giuliano  AJ,  et al.  Auditory working memory impairments in individuals at familial high risk for schizophrenia.  Neuropsychology. 2012;26(3):288-303.PubMedGoogle ScholarCrossref
60.
Huang  S, Seidman  LJ, Rossi  S, Ahveninen  J.  Distinct cortical networks activated by auditory attention and working memory load.  Neuroimage. 2013;83:1098-1108.PubMedGoogle ScholarCrossref
61.
Schafer  JL, Olsen  MK.  Multiple imputation for multivariate missing-data problems: a data analyst’s perspective.  Multivariate Behav Res. 1998;33(4):545-571.PubMedGoogle ScholarCrossref
62.
Rubin  DB.  Multiple imputation after 18+ years.  J Am Stat Assoc. 1996;91:473-489.Google ScholarCrossref
63.
SPSS [computer program]. Version 23.0. Armonk, NY: IBM Corp; 2014.
64.
Cannon  TD, Cadenhead  K, Cornblatt  B,  et al.  Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America.  Arch Gen Psychiatry. 2008;65(1):28-37.PubMedGoogle ScholarCrossref
65.
Seidman  LJ.  Neuropsychological functioning in people with ADHD across the lifespan.  Clin Psychol Rev. 2006;26(4):466-485.PubMedGoogle ScholarCrossref
66.
Cirillo  MA, Seidman  LJ.  Verbal declarative memory dysfunction in schizophrenia: from clinical assessment to genetics and brain mechanisms.  Neuropsychol Rev. 2003;13(2):43-77.PubMedGoogle ScholarCrossref
67.
Carrión  RE, Cornblatt  BA, Burton  CZ,  et al.  Personalized prediction of psychosis: external validation of the NAPLS-2 psychosis risk calculator with the EDIPPP project [published online July 1, 2016].  Am J Psychiatry. PubMedGoogle Scholar
68.
Gold  JM, Barch  DM, Carter  CS,  et al.  Clinical, functional, and intertask correlations of measures developed by the Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia Consortium.  Schizophr Bull. 2012;38(1):144-152.PubMedGoogle ScholarCrossref
69.
Keshavan  MS, Vinogradov  S, Rumsey  J, Sherrill  J, Wagner  A.  Cognitive training in mental disorders: update and future directions.  Am J Psychiatry. 2014;171(5):510-522.PubMedGoogle ScholarCrossref
70.
Rauchensteiner  S, Kawohl  W, Ozgurdal  S,  et al.  Test-performance after cognitive training in persons at risk mental state of schizophrenia and patients with schizophrenia.  Psychiatry Res. 2011;185(3):334-339.PubMedGoogle ScholarCrossref
71.
Holzer  L, Urben  S, Passini  CM,  et al.  A randomized controlled trial of the effectiveness of computer-assisted cognitive remediation (CACR) in adolescents with psychosis or at high risk of psychosis.  Behav Cogn Psychother. 2014;42(4):421-434.PubMedGoogle ScholarCrossref
72.
Hooker  CI, Carol  EE, Eisenstein  TJ,  et al.  A pilot study of cognitive training in clinical high risk for psychosis: initial evidence of cognitive benefit.  Schizophr Res. 2014;157(1-3):314-316.PubMedGoogle ScholarCrossref
73.
Piskulic  D, Barbato  M, Liu  L, Addington  J.  Pilot study of cognitive remediation therapy on cognition in young people at clinical high risk of psychosis.  Psychiatry Res. 2015;225(1-2):93-98.PubMedGoogle ScholarCrossref
Original Investigation
December 2016

Association of Neurocognition With Transition to PsychosisBaseline Functioning in the Second Phase of the North American Prodrome Longitudinal Study

Author Affiliations
  • 1Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts
  • 2Department of Psychiatry, Harvard Medical School at Massachusetts General Hospital, Boston
  • 3Department of Psychiatry, Zucker Hillside Hospital, Queens, New York
  • 4Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
  • 5Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
  • 6Department of Psychology, University of California, Los Angeles
  • 7Department of Psychiatry, University of California, San Diego
  • 8Department of Psychology, Yale University, New Haven, Connecticut
  • 9Department of Psychiatry, Yale University, New Haven, Connecticut
  • 10Department of Psychiatry, University of California, San Francisco
  • 11San Francisco Veterans Affairs Medical Center, San Francisco, California
  • 12Department of Psychiatry, The University of North Carolina at Chapel Hill
  • 13Department of Psychology, Emory University, Atlanta, Georgia
  • 14Department of Psychiatry, Emory University, Atlanta, Georgia
 

Copyright 2016 American Medical Association. All Rights Reserved.

JAMA Psychiatry. 2016;73(12):1239-1248. doi:10.1001/jamapsychiatry.2016.2479
Key Points

Questions  What are the core neurocognitive dysfunctions associated with the clinical high-risk state of psychosis, and which functions are associated with individuals who transition to full psychosis?

Findings  In this multisite, case-control study and standardized assessment across 8 sites, clinical high-risk individuals were significantly impaired in virtually all neurocognitive dimensions compared with controls, especially in those who later transitioned to psychosis. Verbal abilities and declarative memory abilities were associated with time to conversion to psychosis, in association with age, site, and unusual thought content and suspiciousness.

Meaning  Interventions targeting the enhancement of neurocognitive functioning are warranted in those at clinical high risk for psychosis.

Abstract

Importance  Neurocognition is a central characteristic of schizophrenia and other psychotic disorders. Identifying the pattern and severity of neurocognitive functioning during the “near-psychotic,” clinical high-risk (CHR) state of psychosis is necessary to develop accurate risk factors for psychosis and more effective and potentially preventive treatments.

Objectives  To identify core neurocognitive dysfunctions associated with the CHR phase, measure the ability of neurocognitive tests to predict transition to psychosis, and determine if neurocognitive deficits are robust or explained by potential confounders.

Design, Setting, and Participants  In this case-control study across 8 sites, baseline neurocognitive data were collected from January 2009 to April 2013 in the second phase of the North American Prodrome Longitudinal Study (NAPLS 2). The dates of analysis were August 2015 to August 2016. The setting was a consortium of 8 university-based, outpatient programs studying the psychosis prodrome in North America. Participants were 264 healthy controls (HCs) and 689 CHR individuals, aged 12 to 35 years.

Main Outcomes and Measures  Neurocognitive associations with transition to psychosis and effects of medication on neurocognition. Nineteen neuropsychological tests and 4 factors derived from factor analysis were used: executive and visuospatial abilities, verbal abilities, attention and working memory abilities, and declarative memory abilities.

Results  This study included 264 HCs (137 male and 127 female) and 689 CHR participants (398 male and 291 female). In the HCs, 145 (54.9%) were white and 119 (45.1%) were not, whereas 397 CHR participants (57.6%) were white and 291 (42.3%) were not. In the HCs, 45 (17%) were of Hispanic origin, whereas 127 CHR participants (18.4%) were of Hispanic origin. The CHR individuals were significantly impaired compared with HCs on attention and working memory abilities and declarative memory abilities. The CHR converters had large deficits in attention and working memory abilities and declarative memory abilities (Cohen d, approximately 0.80) compared with controls and performed significantly worse on these dimensions than nonconverters (Cohen d, 0.28 and 0.48, respectively). These results were not accounted for by general cognitive ability or medications. In Cox proportional hazards regression, time to conversion in those who transitioned to psychosis was significantly predicted by high verbal (premorbid) abilities (β = 0.40; hazard ratio [HR], 1.48; 95% CI, 1.08-2.04; P = .02), impaired declarative memory abilities (β = −0.87; HR, 0.42; 95% CI, 0.31-0.56; P < .001), age (β = −0.10; HR, 0.90; 95% CI, 0.84-0.97; P = .003), site, and a combined score of unusual thought content or delusional ideas and suspiciousness or persecutory ideas items (β = 0.44; HR, 1.56; 95% CI, 1.36-1.78; P < .001).

Conclusions and Relevance  Neurocognitive impairment, especially in attention and working memory abilities and declarative memory abilities, is a robust characteristic of CHR participants, especially those who later develop psychosis. Interventions targeting the enhancement of neurocognitive functioning are warranted in this population.

Introduction

Neurocognitive dysfunction is a hallmark feature of schizophrenia1-5 and, to a lesser extent, of other psychoses,6 a conceptualization originating approximately 100 years ago7 by Kraepelin8 and by Bleuler.9 There is ample evidence of significant but milder impairments during the premorbid phase,10-12 greater deficits during the prodromal or clinical high-risk (CHR) period of psychosis,13-15 culminating in severe deficits in the first-episode16 and chronic phases.17 This transition suggests an evolution of neurocognitive dysfunction in individuals developing psychosis, especially schizophrenia.10,14,18,19 The CHR20 period is of interest because it offers a temporal window into the changes occurring during the “near-psychotic” state, before confounders, such as chronicity and long-term medication use, obscure the core deficits.

A substantial body of neurocognitive research in CHR populations has been summarized in several meta-analyses.13-15 Small to medium effect size (ES) impairments across most neurocognitive domains studied (Cohen d range, −0.26 to −0.67) and small to large ES values (Cohen d range, −0.35 to −0.84) in those who convert to psychosis (CHR + Cs) have been reported.14 Verbal memory and processing speed have emerged as strong predictors of psychosis.13,14,21-24 However, small sample sizes, differing measures, and variable reporting of sample characteristics limit the reliability of these findings. In this second phase of the North American Prodrome Longitudinal Study (NAPLS 2), we assessed, with what is to our knowledge, the largest CHR sample to date.

First, we sought to identify the key neurocognitive functions impaired in the CHR stage, especially in those who later convert to psychosis. Descriptions of schizophrenia place considerable emphasis on the centrality of dysfunctions in attention1,2,25,26 and working memory.27,28 Evidence of severe deficits in declarative memory29 has more recently emerged in first-episode16,30,31 and CHR14,21,22 samples. Olfactory identification deficits also have been touted as a possible risk factor,32,33 and processing speed34 and general cognitive ability have been shown to be robustly impaired in persons who later develop schizophrenia.10,14 We chose to provide extensive coverage of neurocognitive dimensions thought a priori to mark the evolution into frank psychosis.

Second, we investigated if the neurocognitive profiles were characterized by a general deficit syndrome or specific impairments.35 This finding is of particular relevance for those individuals who transition to psychotic disorders because it provides critical information about the nature of neurocognition in the earliest phase of psychosis.36 We hypothesized that the CHR + C group herein would be characterized by especially salient deficits against a background of general impairments.

Third, we examined differences between medicated and unmedicated CHR individuals. Many of these young people take a range of medications, including antipsychotics.37 Such medications could improve or impair cognition idiosyncratically. Prior CHR neurocognitive studies have not systematically addressed medication status. The large sample in the NAPLS 2 enabled an investigation of a sizable subgroup of CHR + Cs who have never been medicated, thus helping identify neurocognitive function.

Fourth, we explored the potential usefulness of neurocognition for its contribution to transition to psychosis. While it is unlikely that neurocognitive measures will be predictive by themselves of conversion to psychosis, in part because they are impaired in many neuropsychiatric disorders,38,39 knowledge of their relative sensitivities in combination with clinical features may help in the real-world prediction of psychosis or disability.24,40,41

Methods
Participants

In this case-control study across 8 sites, baseline neurocognitive functioning data were collected from January 2009 to April 2013 in the NAPLS 2. The dates of the analysis were August 2015 to August 2016. The NAPLS 2 is a consortium of 8 programs studying the psychosis prodrome in North America, as in the NAPLS 1. The methods and clinical features of the NAPLS 2 are detailed elsewhere.42,43 From a sample of 279 healthy controls (HCs) and 764 CHR individuals ranging in age from 12 to 35 years, 264 HCs and 689 CHR individuals provided baseline neurocognitive data. The study protocols were approved by the ethical review boards or human studies committees of all sites, including Beth Israel Deaconess Medical Center, Boston, Massachusetts; Emory University, Atlanta, Georgia; University of Calgary, Alberta, Canada; University of California, Los Angeles; University of California, San Diego; The University of North Carolina at Chapel Hill; Yale University, New Haven, Connecticut; and Zucker Hillside Hospital, Queens, New York. All procedures comply with the ethical standards of the relevant committees on human experimentation and with the Declaration of Helsinki, as revised in 2008. All participants provided written informed consent.

Inclusion and Exclusion Criteria

The CHR sample met the Criteria of Prodromal Syndromes (COPS),20 based on the Structured Interview for Prodromal Syndromes (SIPS),20 or if younger than 19 years, criteria for schizotypal personality disorder (n = 21) or COPS. Individuals were excluded if they had a lifetime Axis I psychotic disorder, estimated IQs less than 70 on both measures of IQ, a central nervous system disorder, or DSM-IV substance dependence in the past 6 months. Other nonpsychotic DSM-IV disorders were not exclusionary (eg, substance abuse disorder and major depression) unless they clearly caused or better accounted for prodromal symptoms. Antipsychotic medication use was allowed, provided there was clear evidence that psychotic symptoms were not present when the medication was started. The HCs could not meet criteria for any prodromal syndrome, report current or past psychotic or cluster A personality disorder, or have first-degree relatives with a history of psychotic disorder or psychotic symptoms.

Measures

The Structured Clinical Interview for DSM was used to rule out psychosis and to identify DSM-IV Axis I or cluster A personality disorders.44 For some analyses, we used a rescaled sum of the unusual thought content or delusional ideas and suspiciousness or persecutory ideas items from the SIPS-positive symptoms criteria.45 Transition to psychosis was determined by meeting the SIPS Presence of Psychotic Symptoms criteria.20 Assessments were at baseline, 12 months, and 24 months. Current alcohol and cannabis use was assessed with the Alcohol and Drug Use Scale.46 The Calgary Depression Scale for Schizophrenia47,48 was used to assess depression.

The neuropsychological battery was designed to cover a range of functions using well-established clinical neuropsychological tests, as well as experimental measures of sensory, perceptual, or cognitive functions hypothesized to be important indicators of CHR status or conversion to psychosis. These tests included the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB),49-52 the Wechsler Abbreviated Scale of Intelligence (WASI) for general intellectual ability,53 and the Wide Range Achievement Test 4 (WRAT-4) reading task to estimate premorbid ability.54 Experimental measures included the Babble test (for auditory perception55), the University of Pennsylvania Smell Identification Test56 for olfactory identification, a visual and verbal Paired Associate Memory (PAM57) test, and 3 Auditory Continuous Performance Tests (ACPTs58-60). One summary measure from each test was chosen a priori as the best estimate of the function of that test. We factor analyzed the test battery to reduce the number of variables. The eText and eTable 1 in the Supplement provide extensive detail on the battery.

Statistical Analysis

We examined missing data before implementing multiple imputation.61,62 From a sample of 1043 individuals, 953 (91.4%) received baseline neurocognitive testing. Of the CHR sample of 93 individuals who transitioned to psychosis during the 2-year follow-up, 89 (95.7%) received testing. Among the tested sample of 953 individuals, 634 scores (3.5%) from 18 107 total neuropsychological data points were missing. After multiple imputation, we conducted a factor analysis of the 19 neuropsychological variables (eText in the Supplement). All analyses were performed with statistical software (SPSS, version 23.0; IBM Corporation).63

Study groups were HCs, nonconverters (CHR − NCs), and CHR + Cs. We used t tests, Kolmogorov-Smirnov z tests, and χ2 tests to assess demographic comparability. Because of differences in age and maternal educational level, we controlled for both using multivariate analysis of variance (MANOVA) and controlled for site as a random-effects factor with a linear mixed model. We covaried for estimated current and premorbid IQs to test the role of general intellectual ability in cognitive dysfunctions. We compared medicated vs unmedicated groups of HCs vs CHR + Cs and of CHR + Cs vs CHR − NCs by conducting MANOVA with planned comparisons using residualized factor scores generated from the linear mixed models.

To examine group cognitive profiles, we residualized out age and maternal educational level from all neurocognitive indexes (4 factors derived from factor analysis). Area under the curve was calculated by the receiver operating characteristic curve program in SPSS. Prediction of conversion to psychosis and time to conversion was assessed by logistic and Cox proportional hazards regressions. Covariates were selected based on similar prediction analyses conducted in the first phase of the NAPLS64 and the second phase45 and entered into the model if they were associated with survival time and predicted conversion status in logistic regression. Survival time was time to the last SIPS interview or conversion, whichever occurred first. Candidate covariates were added to the model as a block and then subjected to backward selection with a criterion P = .10. Candidates who survived at P ≤ .05 within domains were entered into an omnibus model. Effect sizes were calculated with Cohen d. Bonferroni-corrected significance for the mean comparisons was set for individual tests at P < .003 (0.05 divided by 19) and for factors at P < .013 (0.05 divided by 4).

Results
Demographics

In this naturalistic, observational study, there were 137 male and 127 female HCs and 398 male and 291 female CHR individuals (Table 1 and Table 2). The HCs were significantly older and had significantly more education, and HC mothers also had significantly more education. The groups did not differ in sex or racial distribution, paternal education, or ethnicity. There were no significant differences on any demographic characteristics between CHR − NCs and CHR + Cs.

Clinical Characteristics

The groups did not significantly differ in frequency of alcohol or cannabis abuse or current depression (Tables 1 and 2). There were no significant correlations between these clinical characteristics and neurocognitive factors. The CHR + C group had a significantly shorter follow-up period than the CHR − NC group, reflecting time to conversion and attrition. The CHR − NCs and CHC + Cs were taking a variety of medications, including antipsychotics, antidepressants, stimulants, and others, but there were no significant differences in rates between the 2 CHR groups.

Factor Analysis

The eText in the Supplement explains factor selection. The 4 factors examined were executive and visuospatial abilities, verbal abilities, attention and working memory abilities, and declarative memory abilities (Table 3). Two tests laden with sensory-perceptual processes (olfaction and audition) had very low (University of Pennsylvania Smell Identification Test) or negligible (Babble test) loadings initially and were dropped from factor analysis. They were analyzed with the other individual tests. The bivariate correlations among tests are summarized in eTable 2 in the Supplement.

Neurocognition Group Comparisons
HCs vs CHR Individuals

The CHR group performed significantly worse than HCs on all 19 neuropsychological tests combined (MANOVA F19,933 = 6.71, P < .001), on the 4 factors combined (F4,948 = 24.18, P < .001), and, after controlling for age, site, and maternal educational level, on 2 of 4 factors (attention and working memory abilities F1,948 = 56.52, P < .001 and declarative memory abilities F1,948 = 22.83, P < .001) and on 14 of 19 individual neuropsychological tests. The largest ES (attention and working memory abilities) was of moderate magnitude (Table 4). The mean ES across the 19 neuropsychological tests was small (Cohen d = 0.30). Model-corrected profiles are shown in Figure 1. Controlling separately for WASI IQ score and WRAT-4 reading, the attention and working memory abilities and the declarative memory abilities factors remained significantly different between groups. The differences between HCs and CHR individuals remained significant on 12 of 14 individual tests after covarying WRAT-4 reading. Covarying WASI IQ score yielded fewer significant differences.

HCs vs CHR + Cs

The CHR + Cs performed significantly worse than the HCs (F19,333 = 5.95, P < .001) using all tests. The 4-factor MANOVA (F4,348 = 22.82, P < .001) showed significant differences. In models controlling for age, site, and maternal educational level, CHR + Cs performed significantly worse on 3 of 4 factors, namely, executive and visuospatial abilities (Cohen d = 0.36), attention and working memory abilities (Cohen d = 0.80), and declarative memory abilities (Cohen d = 0.77). The mean ES across the 19 neuropsychological tests was Cohen d = 0.47. Effect sizes that are model adjusted are shown in Figure 2. The CHR + Cs performed significantly worse on verbal abilities, attention and working memory abilities, and declarative memory abilities and on 12 of 14 individual tests after controlling for WRAT-4 reading. They showed fewer significant test differences after covarying WASI IQ score.

Impairments were comparable between 252 currently unmedicated HCs and 51 unmedicated CHR + Cs, with 38 currently medicated CHR + Cs, and between 236 never-medicated HCs and 29 never-medicated CHR + Cs. The smaller group of CHR + Cs taking antipsychotic medications was significantly impaired on attention and working memory abilities and on declarative memory abilities compared with HCs. Moreover, there were no significant cognitive differences between currently unmedicated CHR + Cs vs medicated CHR + Cs, between never-medicated CHR + Cs vs medicated CHR + Cs, or between CHR + Cs taking vs not taking antipsychotic medications (eText, eTable 3, and eFigure in the Supplement).

CHR + Cs vs CHR − NCs

The CHR + C group performed significantly worse than the CHR − NC group (MANOVA F19,669 = 1.90, P = .01 and 4-factor MANOVA F4,684 = 6.51, P < .001), specifically on attention and working memory abilities (Cohen d = 0.28) and on declarative memory abilities (Cohen d = 0.48) after controlling for age, site, and maternal educational level (Table 4). The CHR + Cs performed significantly worse in mixed linear model contrasts only on the Brief Visuospatial Memory Test–Revised (BVMT-R) (Cohen d = 0.40) and Paired Associate Memory (PAM) test (Cohen d = 0.39). The mean ES across the 19 neuropsychological tests was Cohen d = 0.20. After WASI IQ score and WRAT-4 reading were covaried, CHR + Cs performed significantly better on verbal abilities and worse on declarative memory abilities and worse on the BVMT-R and PAM test tasks.

Prediction Analyses

After exploring a range of possible predictors, age (β = −0.10; hazard ratio [HR], 0.90; 95% CI, 0.84-0.97; P = .003), unusual thought content or delusional ideas and suspiciousness or persecutory ideas items symptoms (β = 0.44; HR, 1.56; 95% CI, 1.36-1.78; P < .001), and dummy codes for 3 sites were retained (eText in the Supplement). The verbal abilities (β = 0.40; HR, 1.48; 95% CI, 1.08-2.04; P = .02) and declarative memory abilities (β = −0.87; HR, 0.42; 95% CI, 0.31-0.56; P < .001) factors were retained. Similar results were observed in logistic regression analyses predicting conversion. Cox proportional hazards regression was then run with the strongest-loading individual component tests (BVMT-R, Hopkins Verbal Learning Test–Revised [HVLT-R], and PAM test for declarative memory abilities and WRAT-4 reading and WASI vocabulary for verbal abilities). All covariates were retained, as were the BVMT-R (β = 0.05; HR, 0.95; 95% CI, 0.91-0.99; P = .009), HVLT-R (β = −0.05; HR, 0.95; 95% CI, 0.91-1.00; P = .04), PAM test (β = −1.83; HR, 0.16; 95% CI, 0.05-0.54; P = .003), and WRAT-4 reading (β = 0.05; HR, 1.05; 95% CI,1.01-1.10, P = .009). Declarative memory abilities had the highest area under the curve at 0.624, followed by attention and working memory abilities with an area under the curve of 0.568. The highest areas under the curve for declarative memory abilities tests were the PAM test at 0.607, BVMT-R at 0.604, and HVLT-R at 0.576, and the highest areas under the curve for attention and working memory abilities were Brief Assessment of Cognition in Schizophrenia (BACS) symbol coding at 0.584, Trail Making A at 0.582, ACPT working memory at 0.579, and ACPT vigilance at 0.568 (eTable 4 in the Supplement).

Discussion

In the largest and most detailed study of CHR prodromal cases to date, using a multisite, case-control design and standardized assessments, we demonstrated that CHR individuals were significantly impaired in virtually all neurocognitive dimensions compared with HCs, and this finding could not be accounted for by premorbid or current general cognitive ability, current depression, medication use, or alcohol or cannabis abuse. Effect sizes compared with HCs for attention and working memory abilities and for declarative memory abilities were large (Cohen d, approximately 0.80) for CHR + Cs. Compared with CHR − NCs, CHR + Cs were significantly impaired in attention and working memory abilities and in declarative memory abilities, with the latter significantly predicting conversion to psychosis and time to event in concert with a rescaled sum of the unusual thought content or delusional ideas and suspiciousness or persecutory ideas items from the SIPS. Comparable impairments were observed in never-medicated and currently unmedicated CHR − NCs and CHR + Cs. These data demonstrate the sensitivity of neurocognitive function as a component risk marker for psychosis.

Our findings support theoretical models hypothesizing attention and working memory abilities impairments and, even more strongly, impaired declarative memory abilities as central to the CHR stage.21,22 The results are consistent with the NAPLS 1, in which declarative memory abilities had the largest ES decrement and approximately the same magnitude in CHR + Cs.22 The distinct profile of performance across domains, especially in CHR + Cs, suggests that, at the incipient psychotic phase, specific forms of neurocognition are affected and are predictive of later psychosis.

Among CHR participants, there was considerable variability in neurocognitive performance. Impairments of CHR − NCs (mean Cohen d, 0.30) were on the order of other psychiatric disorders in young people, such as attention-deficit/hyperactivity disorder.65 Impairments in the CHR + Cs (mean Cohen d, 0.47) were approximately 56.7% larger, although smaller than those observed in first-episode schizophrenia16 (eTable 5 in the Supplement). Investigations of individual variability and longitudinal analyses are needed to identify how profile and severity differ according to comorbid disorders, final diagnoses (eg, schizophrenia vs bipolar psychosis), and preconversion and postconversion.

A key question was how neurocognitive deficits are associated with medication use status. Psychotropic-naive and unmedicated subgroups had significant impairments that were of comparable magnitude to those observed in the overall CHR subgroups. Treated groups, including those taking antipsychotic medications, were largely comparable to those without treatment, except they had somewhat greater attention and working memory abilities impairment. These observations emphasize the essential nature of neurocognitive impairment in the CHR stage and de-emphasize the role of medication use as confounders in our results. Our study design precludes conclusions about causality, and future work should study the effect of medication use on neurocognition in CHR populations in a prospective design.

There were several other potentially important observations. The unexpectedly higher verbal score (reflecting WRAT-4 reading) that was retained in logistic and Cox proportional hazards regressions in concert with impaired declarative memory abilities was not a significant predictor in univariate comparisons. This pattern of high verbal premorbid ability and impaired memory, coupled with the unusual thought content or delusional ideas and suspiciousness or persecutory ideas items composite, appears to be a pernicious combination predicting conversion and needs replication. Also important, the BVMT-R (a visual-memory test) showed comparably large impairments as the 2 verbal memory tasks, highlighting that deficits of declarative memory abilities in CHR are not solely verbal and that declarative memory abilities impairments are key neurocognitive risk markers.66

Neurocognitive tests used in concert with other clinical and psychobiological measures may enhance prediction of psychosis or functional outcome. For example, in analyses limited to 2 tests selected from a literature review14 before these neuropsychological analyses, the NAPLS 2 investigators found that the HVLT-R and the Brief Assessment of Cognition in Schizophrenia symbol coding tests added modest but significant independent predictive power above the clinical measures in a risk calculator algorithm for psychosis conversion,45 and this finding was replicated in an independent non-NAPLS 2 sample.67 Similar results have been observed in other studies.24,40,41 In the present study, we showed that other tests, including the BVMT-R, PAM test, and ACPT vigilance test, added significant independent variance beyond the unusual thought content or delusional ideas and suspiciousness or persecutory ideas items symptoms, augmenting the importance of neurocognitive markers.

Our study has strengths and limitations. Because of its large sample from diverse geographical areas, extensive neurocognitive coverage, complete neurocognitive data set, and large never-medicated sample, the NAPLS 2 allowed for a strong confirmation of neurocognitive hypotheses. The NAPLS 2 built on and improved the NAPLS 1 assessment, confirming and expanding prior results (eTable 5 in the Supplement). This broad range of measures increased the scope of what is known about CHR neurocognition.

Limitations

Limitations include the fact that most of these tests and factors are complex. Therefore, while declarative memory abilities are clearly affected, the tasks tapping this domain cannot parse the specific mechanisms underlying the deficits. Further research with more molecular measures of cognition, such as those developed by the Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia (CENTRACS) Consortium,68 may allow specification of the cognitive processes underlying the deficits. We did not randomize or counterbalance the order of tests, so we cannot rule out order effects. However, the most impaired tasks were spread out across the battery from the sixth to the last tests in the battery, so there is no obvious fatigue effect.

Conclusions

Neurocognitive impairment is common in CHR individuals and of clinically meaningful magnitude, especially in those who later develop psychosis. Attention and working memory abilities and declarative memory abilities are important targets for early cognitive, enhancing interventions in this population.69-73

Back to top
Article Information

Corresponding Author: Larry J. Seidman, PhD, Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, 75 Fenwood Rd, Room 542, Boston, MA 02115 (lseidman@bidmc.harvard.edu).

Accepted for Publication: August 15, 2016.

Published Online: November 2, 2016. doi:10.1001/jamapsychiatry.2016.2479

Author Contributions: Drs Seidman and Shapiro had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Seidman, Addington, Cadenhead, Cannon, McGlashan, Perkins, Tsuang, Walker, Woods.

Acquisition, analysis, or interpretation of data: Seidman, Shapiro, Stone, Woodberry, Ronzio, Cornblatt, Addington, Bearden, Cadenhead, Cannon, Mathalon, Perkins, Walker, Woods.

Drafting of the manuscript: Seidman, Shapiro, Ronzio, McGlashan.

Critical revision of the manuscript for important intellectual content: Seidman, Shapiro, Stone, Woodberry, Cornblatt, Addington, Bearden, Cadenhead, Cannon, Mathalon, Perkins, Tsuang, Walker, Woods.

Statistical analysis: Seidman, Shapiro, Woodberry, Ronzio, Mathalon.

Obtained funding: Seidman, Cornblatt, Addington, Bearden, Cadenhead, Cannon, McGlashan, Perkins, Walker, Woods.

Administrative, technical, or material support: Seidman, Shapiro, Stone, Cornblatt, Bearden, McGlashan, Perkins, Walker, Woods.

Study supervision: Seidman, Shapiro, Stone, Woodberry, Cornblatt, Addington, Bearden, Cadenhead, Cannon, McGlashan, Perkins, Tsuang, Woods.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by grants U01 MH081928, P50 MH080272, and R01 MH096027 from the National Institute of Mental Health and by grant SCDMH82101008006 from the Commonwealth of Massachusetts (Dr Seidman), as well as by the following grants from the National Institute of Mental Health: grant U01 MH081857 (Dr Cornblatt); grant U01 MH081984 (Dr Addington); grant P50 MH066286 (Prodromal Core) (Dr Bearden); grants R01 MH60720, U01 MH082022, and K24 MH76191 (Dr Cadenhead); grant U01 MH081902 (Dr Cannon); grant U01 MH082004 (Dr Perkins); grant U01 MH081988 (Dr Walker); and grant U01 MH082022 (Dr Woods). This study was also supported by Clinical Translational Science Award UL1RR025758 and General Clinical Research Center Grant M01RR01032 from the National Center for ResearchResources to Harvard University at Beth Israel Deaconess Medical Center and by grant P41RR14075 from the National Center for Research Resources.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health.

Additional Contributions: Robert Heinssen, PhD, of the National Institute of Mental Health, provided study support. Statistical consultation was provided by Stephen V. Faraone, PhD (State University of New York Upstate Medical University), Michael F. Green, PhD (Geffen School of Medicine at University of California, Los Angeles), and Gerhard Hellemann, PhD (Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles) on the factor analyses, as well as on statistical approaches by Dr Faraone. Caitlin Bryant, BS, Michelle Friedman-Yakoobian, PhD, Anthony Giuliano, PhD, Andrea Gnong-Granato, MSW, Victoria Choate Hasler, MA, Matcheri S. Keshavan, MD, Robert W. McCarley, MD, Raquelle Mesholam-Gately, PhD, Jayne-Marie Nova, BA, Corin Pilo-Comtois, MA, Janine Rodenhiser-Hill, PhD, Rachael Serur, BA, Lynda Tucker, and Joanne Wojcik, PhD, provided assistance with the study at the Beth Israel Deaconess Medical Center site. Ralph E. Hoffman, MD, of Yale University, developed the Babble test and died during the writing of the manuscript. No compensation was given to any of them specifically for their work.

References
1.
Mirsky  AF.  Neuropsychological bases of schizophrenia.  Annu Rev Psychol. 1969;20:321-348.PubMedGoogle ScholarCrossref
2.
Seidman  LJ.  Schizophrenia and brain dysfunction: an integration of recent neurodiagnostic findings.  Psychol Bull. 1983;94(2):195-238.PubMedGoogle ScholarCrossref
3.
Green  MF.  What are the functional consequences of neurocognitive deficits in schizophrenia?  Am J Psychiatry. 1996;153(3):321-330.PubMedGoogle ScholarCrossref
4.
Heinrichs  RW.  The primacy of cognition in schizophrenia.  Am Psychol. 2005;60(3):229-242.PubMedGoogle ScholarCrossref
5.
Kahn  RS, Keefe  RS.  Schizophrenia is a cognitive illness: time for a change in focus.  JAMA Psychiatry. 2013;70(10):1107-1112.PubMedGoogle ScholarCrossref
6.
Lewandowski  KE, Cohen  BM, Ongur  D.  Evolution of neuropsychological dysfunction during the course of schizophrenia and bipolar disorder.  Psychol Med. 2011;41(2):225-241.PubMedGoogle ScholarCrossref
7.
Heckers  S.  What is the core of schizophrenia?  JAMA Psychiatry. 2013;70(10):1009-1010.PubMedGoogle ScholarCrossref
8.
Kraepelin  E.  Dementia Praecox and Paraphrenia. Edinburgh, Scotland: E & S Livingston; 1919.
9.
Bleuler  E.  Dementia Praecox or the Group of Schizophrenias. New York, NY: International Universities Press; 1950.
10.
Woodberry  KA, Giuliano  AJ, Seidman  LJ.  Premorbid IQ in schizophrenia: a meta-analytic review.  Am J Psychiatry. 2008;165(5):579-587.PubMedGoogle ScholarCrossref
11.
Reichenberg  A, Caspi  A, Harrington  H,  et al.  Static and dynamic cognitive deficits in childhood preceding adult schizophrenia: a 30-year study.  Am J Psychiatry. 2010;167(2):160-169.PubMedGoogle ScholarCrossref
12.
Cannon  M, Caspi  A, Moffitt  TE,  et al.  Evidence for early-childhood, pan-developmental impairment specific to schizophreniform disorder: results from a longitudinal birth cohort.  Arch Gen Psychiatry. 2002;59(5):449-456.PubMedGoogle ScholarCrossref
13.
Fusar-Poli  P, Deste  G, Smieskova  R,  et al.  Cognitive functioning in prodromal psychosis: a meta-analysis.  Arch Gen Psychiatry. 2012;69(6):562-571.PubMedGoogle ScholarCrossref
14.
Giuliano  AJ, Li  H, Mesholam-Gately  RI, Sorenson  SM, Woodberry  KA, Seidman  LJ.  Neurocognition in the psychosis risk syndrome: a quantitative and qualitative review.  Curr Pharm Des. 2012;18(4):399-415.PubMedGoogle ScholarCrossref
15.
Bora  E, Murray  RM.  Meta-analysis of cognitive deficits in ultra-high risk to psychosis and first-episode psychosis: do the cognitive deficits progress over, or after, the onset of psychosis?  Schizophr Bull. 2014;40(4):744-755.PubMedGoogle ScholarCrossref
16.
Mesholam-Gately  RI, Giuliano  AJ, Goff  KP, Faraone  SV, Seidman  LJ.  Neurocognition in first-episode schizophrenia: a meta-analytic review.  Neuropsychology. 2009;23(3):315-336.PubMedGoogle ScholarCrossref
17.
Heinrichs  RW, Zakzanis  KK.  Neurocognitive deficit in schizophrenia: a quantitative review of the evidence.  Neuropsychology. 1998;12(3):426-445.PubMedGoogle ScholarCrossref
18.
MacCabe  JH, Wicks  S, Löfving  S,  et al.  Decline in cognitive performance between ages 13 and 18 years and the risk for psychosis in adulthood: a Swedish longitudinal cohort study in males.  JAMA Psychiatry. 2013;70(3):261-270.PubMedGoogle ScholarCrossref
19.
Liu  CH, Keshavan  MS, Tronick  E, Seidman  LJ.  Perinatal risks and childhood premorbid indicators of later psychosis: next steps for early psychosocial interventions.  Schizophr Bull. 2015;41(4):801-816.PubMedGoogle ScholarCrossref
20.
McGlashan  TH, Walsh  BC, Woods  SW.  The Psychosis-Risk Syndrome: Handbook for Diagnosis and Follow-up. New York, NY: Oxford University Press Inc; 2010.
21.
Lencz  T, Smith  CW, McLaughlin  D,  et al.  Generalized and specific neurocognitive deficits in prodromal schizophrenia.  Biol Psychiatry. 2006;59(9):863-871.PubMedGoogle ScholarCrossref
22.
Seidman  LJ, Giuliano  AJ, Meyer  EC,  et al; North American Prodrome Longitudinal Study (NAPLS) Group.  Neuropsychology of the prodrome to psychosis in the NAPLS consortium: relationship to family history and conversion to psychosis.  Arch Gen Psychiatry. 2010;67(6):578-588.PubMedGoogle ScholarCrossref
23.
Frommann  I, Pukrop  R, Brinkmeyer  J,  et al.  Neuropsychological profiles in different at-risk states of psychosis: executive control impairment in the early—and additional memory dysfunction in the late—prodromal state.  Schizophr Bull. 2011;37(4):861-873.PubMedGoogle ScholarCrossref
24.
Michel  C, Ruhrmann  S, Schimmelmann  BG, Klosterkötter  J, Schultze-Lutter  F.  A stratified model for psychosis prediction in clinical practice.  Schizophr Bull. 2014;40(6):1533-1542.PubMedGoogle ScholarCrossref
25.
Nuechterlein  KH, Dawson  ME.  Information processing and attentional functioning in the developmental course of schizophrenic disorders.  Schizophr Bull. 1984;10(2):160-203.PubMedGoogle ScholarCrossref
26.
Cornblatt  BA, Keilp  JG.  Impaired attention, genetics, and the pathophysiology of schizophrenia.  Schizophr Bull. 1994;20(1):31-46.PubMedGoogle ScholarCrossref
27.
Park  S, Holzman  PS.  Schizophrenics show spatial working memory deficits.  Arch Gen Psychiatry. 1992;49(12):975-982.PubMedGoogle ScholarCrossref
28.
Park  S, Gooding  DC.  Working memory impairment as an endophenotypic marker of a schizophrenia diathesis.  Schizophr Res Cogn. 2014;1(3):127-136.PubMedGoogle ScholarCrossref
29.
Tamminga  CA, Stan  AD, Wagner  AD.  The hippocampal formation in schizophrenia.  Am J Psychiatry. 2010;167(10):1178-1193.PubMedGoogle ScholarCrossref
30.
Saykin  AJ, Shtasel  DL, Gur  RE,  et al.  Neuropsychological deficits in neuroleptic naive patients with first-episode schizophrenia.  Arch Gen Psychiatry. 1994;51(2):124-131.PubMedGoogle ScholarCrossref
31.
Aleman  A, Hijman  R, de Haan  EHF, Kahn  RS.  Memory impairment in schizophrenia: a meta-analysis.  Am J Psychiatry. 1999;156(9):1358-1366.PubMedGoogle Scholar
32.
Brewer  WJ, Wood  SJ, McGorry  PD,  et al.  Impairment of olfactory identification ability in individuals at ultra-high risk for psychosis who later develop schizophrenia.  Am J Psychiatry. 2003;160(10):1790-1794.PubMedGoogle ScholarCrossref
33.
Turetsky  BI, Kamath  V, Calkins  ME,  et al.  Olfaction and schizophrenia clinical risk status: just the facts.  Schizophr Res. 2012;139(1-3):260-261.PubMedGoogle ScholarCrossref
34.
Dickinson  D, Ramsey  ME, Gold  JM.  Overlooking the obvious: a meta-analytic comparison of digit symbol coding tasks and other cognitive measures in schizophrenia.  Arch Gen Psychiatry. 2007;64(5):532-542.PubMedGoogle ScholarCrossref
35.
Schaefer  J, Giangrande  E, Weinberger  DR, Dickinson  D.  The global cognitive impairment in schizophrenia: consistent over decades and around the world.  Schizophr Res. 2013;150(1):42-50.PubMedGoogle ScholarCrossref
36.
Woodberry  KA, Seidman  LJ, Giuliano  AJ, Verdi  MB, Cook  WL, McFarlane  WR.  Neuropsychological profiles in individuals at clinical high risk for psychosis: relationship to psychosis and intelligence.  Schizophr Res. 2010;123(2-3):188-198.PubMedGoogle ScholarCrossref
37.
Woods  SW, Addington  J, Bearden  CE,  et al.  Psychotropic medication use in youth at high risk for psychosis: comparison of baseline data from two research cohorts 1998-2005 and 2008-2011.  Schizophr Res. 2013;148(1-3):99-104.PubMedGoogle ScholarCrossref
38.
McGrath  LM, Braaten  EB, Doty  ND,  et al.  Extending the “cross-disorder” relevance of executive functions to dimensional neuropsychiatric traits in youth.  J Child Psychol Psychiatry. 2016;57(4):462-471. PubMedGoogle ScholarCrossref
39.
Hill  SK, Reilly  JL, Keefe  RSE,  et al.  Neuropsychological impairments in schizophrenia and psychotic bipolar disorder: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study.  Am J Psychiatry. 2013;170(11):1275-1284.PubMedGoogle ScholarCrossref
40.
Keefe  RS, Perkins  DO, Gu  H, Zipursky  RB, Christensen  BK, Lieberman  JA.  A longitudinal study of neurocognitive function in individuals at-risk for psychosis.  Schizophr Res. 2006;88(1-3):26-35.PubMedGoogle ScholarCrossref
41.
Riecher-Rössler  A, Pflueger  MO, Aston  J,  et al.  Efficacy of using cognitive status in predicting psychosis: a 7-year follow-up.  Biol Psychiatry. 2009;66(11):1023-1030.PubMedGoogle ScholarCrossref
42.
Addington  J, Cadenhead  KS, Cornblatt  BA,  et al.  North American Prodrome Longitudinal Study (NAPLS 2): overview and recruitment.  Schizophr Res. 2012;142(1-3):77-82.PubMedGoogle ScholarCrossref
43.
Addington  J, Liu  L, Buchy  L,  et al.  North American Prodrome Longitudinal Study (NAPLS 2): the prodromal symptoms.  J Nerv Ment Dis. 2015;203(5):328-335.PubMedGoogle ScholarCrossref
44.
Spitzer  RL, Williams  JB, Gibbon  M.  Instruction Manual for the Structured Clinical Interview for DSM-IV. New York: Biometrics Research Dept, New York State Psychiatric Institute; 1994.
45.
Cannon  TD, Yu  C, Addington  J,  et al.  An individualized risk calculator for research in prodromal psychosis [published online July 1, 2016].  Am J Psychiatry. PubMedGoogle Scholar
46.
Drake  RE, Mueser  K, McHugo  G. Clinical rating scales. In: Sederer  L, Dickey  B, eds.  Outcomes Assessment in Clinical Practice. Baltimore, MD: Williams & Wilkins; 1996:113-116.
47.
Addington  D, Addington  J, Maticka-Tyndale  E.  Assessing depression in schizophrenia: the Calgary Depression Scale.  Br J Psychiatry Suppl. 1993;163(22):39-44.PubMedGoogle Scholar
48.
Addington  J, Shah  H, Liu  L, Addington  D.  Reliability and validity of the Calgary Depression Scale for Schizophrenia (CDSS) in youth at clinical high risk for psychosis.  Schizophr Res. 2014;153(1-3):64-67.PubMedGoogle ScholarCrossref
49.
Nuechterlein  KH, Green  MF.  MATRICS Consensus Cognitive Battery. Los Angeles, CA: MATRICS Assessment Inc; 2006.
50.
Nuechterlein  KH, Green  MF, Kern  RS,  et al.  The MATRICS Consensus Cognitive Battery, part 1: test selection, reliability, and validity.  Am J Psychiatry. 2008;165(2):203-213.PubMedGoogle ScholarCrossref
51.
Kern  RS, Nuechterlein  KH, Green  MF,  et al.  The MATRICS Consensus Cognitive Battery, part 2: co-norming and standardization.  Am J Psychiatry. 2008;165(2):214-220.PubMedGoogle ScholarCrossref
52.
McCleery  A, Green  MF, Hellemann  GS,  et al.  Latent structure of cognition in schizophrenia: a confirmatory factor analysis of the MATRICS Consensus Cognitive Battery (MCCB).  Psychol Med. 2015;45(12):2657-2666.PubMedGoogle ScholarCrossref
53.
Wechsler  D.  WASI Manual. San Antonio, CA: Psychological Corp, Harcourt Brace & Co; 1999.
54.
Wilkinson  GS, Robertson  GJ.  The Wide Range Achievement Test Administration Manual. 4th ed. Wilmington, DE: Wide Range Inc; 2006.
55.
Hoffman  RE, Woods  SW, Hawkins  KA,  et al.  Extracting spurious messages from noise and risk of schizophrenia-spectrum disorders in a prodromal population.  Br J Psychiatry. 2007;191(4):355-356.PubMedGoogle ScholarCrossref
56.
Doty  RL.  The Smell Identification Test TM Administration Manual. 3rd ed. Haddon Heights, NJ: Sensonics Inc; 1995.
57.
Jacobson  S, van Erp  TG, Karlsgodt  K, Torre  J, Bearden  CE, Cannon  TD. Reduced temporo-limbic engagement during encoding of word pairs in an associative memory task in the psychosis prodrome: baseline fMRI findings from the North American Prodrome Longitudinal Study (NAPLS). Paper presented at: 13th International Congress on Schizophrenia Research (IC0SR); April 2-6, 2011; Colorado Springs, CO.
58.
Seidman  LJ, Breiter  HC, Goodman  JM,  et al.  A functional magnetic resonance imaging study of auditory vigilance with low and high information processing demands.  Neuropsychology. 1998;12(4):505-518.PubMedGoogle ScholarCrossref
59.
Seidman  LJ, Meyer  EC, Giuliano  AJ,  et al.  Auditory working memory impairments in individuals at familial high risk for schizophrenia.  Neuropsychology. 2012;26(3):288-303.PubMedGoogle ScholarCrossref
60.
Huang  S, Seidman  LJ, Rossi  S, Ahveninen  J.  Distinct cortical networks activated by auditory attention and working memory load.  Neuroimage. 2013;83:1098-1108.PubMedGoogle ScholarCrossref
61.
Schafer  JL, Olsen  MK.  Multiple imputation for multivariate missing-data problems: a data analyst’s perspective.  Multivariate Behav Res. 1998;33(4):545-571.PubMedGoogle ScholarCrossref
62.
Rubin  DB.  Multiple imputation after 18+ years.  J Am Stat Assoc. 1996;91:473-489.Google ScholarCrossref
63.
SPSS [computer program]. Version 23.0. Armonk, NY: IBM Corp; 2014.
64.
Cannon  TD, Cadenhead  K, Cornblatt  B,  et al.  Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America.  Arch Gen Psychiatry. 2008;65(1):28-37.PubMedGoogle ScholarCrossref
65.
Seidman  LJ.  Neuropsychological functioning in people with ADHD across the lifespan.  Clin Psychol Rev. 2006;26(4):466-485.PubMedGoogle ScholarCrossref
66.
Cirillo  MA, Seidman  LJ.  Verbal declarative memory dysfunction in schizophrenia: from clinical assessment to genetics and brain mechanisms.  Neuropsychol Rev. 2003;13(2):43-77.PubMedGoogle ScholarCrossref
67.
Carrión  RE, Cornblatt  BA, Burton  CZ,  et al.  Personalized prediction of psychosis: external validation of the NAPLS-2 psychosis risk calculator with the EDIPPP project [published online July 1, 2016].  Am J Psychiatry. PubMedGoogle Scholar
68.
Gold  JM, Barch  DM, Carter  CS,  et al.  Clinical, functional, and intertask correlations of measures developed by the Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia Consortium.  Schizophr Bull. 2012;38(1):144-152.PubMedGoogle ScholarCrossref
69.
Keshavan  MS, Vinogradov  S, Rumsey  J, Sherrill  J, Wagner  A.  Cognitive training in mental disorders: update and future directions.  Am J Psychiatry. 2014;171(5):510-522.PubMedGoogle ScholarCrossref
70.
Rauchensteiner  S, Kawohl  W, Ozgurdal  S,  et al.  Test-performance after cognitive training in persons at risk mental state of schizophrenia and patients with schizophrenia.  Psychiatry Res. 2011;185(3):334-339.PubMedGoogle ScholarCrossref
71.
Holzer  L, Urben  S, Passini  CM,  et al.  A randomized controlled trial of the effectiveness of computer-assisted cognitive remediation (CACR) in adolescents with psychosis or at high risk of psychosis.  Behav Cogn Psychother. 2014;42(4):421-434.PubMedGoogle ScholarCrossref
72.
Hooker  CI, Carol  EE, Eisenstein  TJ,  et al.  A pilot study of cognitive training in clinical high risk for psychosis: initial evidence of cognitive benefit.  Schizophr Res. 2014;157(1-3):314-316.PubMedGoogle ScholarCrossref
73.
Piskulic  D, Barbato  M, Liu  L, Addington  J.  Pilot study of cognitive remediation therapy on cognition in young people at clinical high risk of psychosis.  Psychiatry Res. 2015;225(1-2):93-98.PubMedGoogle ScholarCrossref
×