[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.
Cognitive Trajectories of Individual Tests Over 24-Month Follow-up
Cognitive Trajectories of Individual Tests Over 24-Month Follow-up

A, Healthy controls. B, Remitters. C, Nonconverters. D, Nonremitters. E, Converters. Each test is color-coded. Individual lines reflect estimated cognitive scores for each test computed on the basis of linear mixed model outputs for each test. Babble indicates Babble Task; BACS, Brief Assessment of Cognition for Schizophrenia; BDIT, Binocular Depth Inversion Task; CPT, Continuous Performance Task: 2d, 2Digit, 3d, 3Digit, 4d, 4 Digit subtasks; HISOC, High Risk Social Challenge; SG, Snakes in the Grass test; Acc, Accuracy; rt, reaction time; and WMSIIIss, Wechsler Memory Scale – III Spatial Span.

Figure 2.
Component Loading Plots for Baseline and 24-Month Follow-up by Healthy Controls, Remitters, and Nonremitters
Component Loading Plots for Baseline and 24-Month Follow-up by Healthy Controls, Remitters, and Nonremitters

A-C, Component loading plots for baseline. D-F, Component loading plots for 24-month follow-up. BACS indicates Brief Assessment of Cognition in Schizophrenia (ds, digit sequencing; sc, symbol coding; sfa, verbal fluency—animals; sff, verbal fluency—fruits; sfv, verbal fluency—vegetables; tmt, token motor task; tol, Tower of London; vf, verbal fluency; vm, verbal memory); Continuous Performance Task: 2d, 2Digit, 3d, 3Digit, 4d, 4 Digit subtasks; GCF, general cognitive function; HISOC, High-Risk Social Challenge; SG, Snakes in the Grass (Distractrt, distractor reaction time; Targetrt, target reaction time); WMSIIIss, Wechsler Memory Scale-III Spatial Span.

Figure 3.
Cognitive Component Profiles by Group and Longitudinal Time by Group Models
Cognitive Component Profiles by Group and Longitudinal Time by Group Models

A, Cognitive component profiles by group. B-F, Longitudinal time by group. BL indicates baseline; EM, expectation maximization; FU, follow-up; GCF, general cognitive function; NWT, nonweighted; WT, weighted; z, standardized score. Bonferroni-corrected model significance is indicated by superscript notation.

aHealthy controls vs remitters.

bHealthy controls vs nonremitters.

cRemitters vs nonremitters.

dRemitters baseline vs remitters follow-up.

Figure 4.
Social and Occupational Functioning and Cognitive Component Changes
Social and Occupational Functioning and Cognitive Component Changes

A, Functioning profiles by time point and group (healthy controls, remitters, and nonremitters). B, Repeated-measures schematics for time-by-group and time-by-cognitive component. Differential rate of change between baseline and follow-up: SOFAS: F = 36.85; P < .001; η2 = 0.130. Attention component: F = 5.65; P = .02; η2 = 0.013. GCF component: F = 7.18; P = .01; η2 = 0.014. C, Post hoc repeated measures. Differential rate of change between baseline and follow-up: SOFAS: F = 90.86; P < .001; η2 = 0.170. Attention component: F = 5.65; P = .02; η2 = 0.013. D, Post hoc repeated measures. Differential rate of change between baseline and follow-up: SOFAS: F = 8.67; P = .003; η2 = 0.020. Attention component: F = 15.83; P < .001; η2 = 0.036. E, Post hoc repeated measures. Differential rate of change between baseline and follow-up: SOFAS: F = 3.24; P = .07; η2 = 0.002. GCF component: F = 7.11; P < .009; η2 = 0.058. % indicates percentage difference between best and worst functioning during assessment time point; ∆, difference between baseline and follow-up; GCF, general cognitive function; and SOFAS, Social and Occupational Functioning Assessment Scale.

Table.  
Baseline Demographics Across Groups
Baseline Demographics Across Groups
Supplement.

eAppendix 1. Statistical Approaches for Testing Principal Component Loadings: Data Analysis to Test Cognitive Structure Differences Across Groups Prospectively

eAppendix 2. Concept of Differentiation and De-Differentiation of Cognitive Factors Tested via Changes in Factor Loadings: Kolmogorov-Smirnov Test for Approximating De-Differentiation

eAppendix 3. Data Analysis Workflow: Discussion of Data-Analytic Strategies Carried Out for the Current Report

eTable 1. Ordinal Regression and Post-Hoc Tests for Baseline Cognitive Prediction of Group Membership

eTable 2. Linear Mixed Models Elements for Cognitive Tests

eTable 3. Linear Mixed Models Elements for Maturational Stage Investigation

eTable 4. Linear Mixed Model Elements for UHR Ascertainment Age and Age-Related Trajectories

eTable 5. Post-Hoc Linear Mixed Model for UHR Ascertainment Age and Age-Related Trajectories

eTable 6. Marginal Homogeneity Test for Effect Distribution at Baseline and Follow-Up

eTable 7. Kolmogorov-Smirnov Tests for Component Loading Comparisons

eTable 8. Univariate One-Way ANOVA for Cognitive Components, by Group Comparisons 

eTable 9. Repeated Measures ANOVA

eFigure 1. Distribution of Factor Loadings Across Groups at Baseline and Follow-Up

eFigure 2. Baseline Cognitive Profiles by Group

eFigure 3. BACS Digit Sequencing: Linear Mixed Model SPSS Path Plots by Healthy Controls, Non-Converters, Converters, Maturational Stage and Time

eFigure 4. BACS Semantic Fluency (F): Linear Mixed Model SPSS Path Plots by Healthy Controls, Non-converters, Converters, Maturational Stage and Time

eFigure 5. BACS Symbol Coding: Linear Mixed Model SPSS Path Plots by Healthy Controls, Non-Converters, Converters, Maturational Stage and Time

eFigure 6. BACS Verbal Memory: Linear Mixed Model SPSS Path Plots by Healthy Controls, Remitters, Non-Remitters, Maturational Stage and Time

eFigure 7. BACS Semantic Fluency (F): Linear Mixed Model SPSS Path Plots by Healthy Controls, Remitters, Non-Remitters, Maturational Stage and Time

eFigure 8. Snakes in Grass (Reaction Time): Linear Mixed Model SPSS Path Plots of Predicted Score for Age Dependent Trajectories Across Groups

eFigure 9. Snakes in Grass (Accuracy): Linear Mixed Model SPSS Path Plots of Predicted Score for Age Dependent Trajectories Across Groups

1.
Hawkins  KA, McGlashan  TH, Quinlan  D,  et al.  Factorial structure of the scale of prodromal symptoms.  Schizophr Res. 2004;68(2-3):339-347. doi:10.1016/S0920-9964(03)00053-7PubMedGoogle ScholarCrossref
2.
McGlashan  TH, Walsh  BC, Woods  SW.  The Psychosis-Risk Syndrome: Handbook for Diagnosis and Follow-Up. New York, NY: Oxford University Press; 2010.
3.
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. doi:10.1001/archgenpsychiatry.2010.66PubMedGoogle ScholarCrossref
4.
Addington  J, Cadenhead  KS, Cannon  TD,  et al; North American Prodrome Longitudinal Study.  North American Prodrome Longitudinal Study: a collaborative multisite approach to prodromal schizophrenia research.  Schizophr Bull. 2007;33(3):665-672. doi:10.1093/schbul/sbl075PubMedGoogle ScholarCrossref
5.
Cannon  TD, Cornblatt  B, McGorry  P.  The empirical status of the ultra high-risk (prodromal) research paradigm.  Schizophr Bull. 2007;33(3):661-664. doi:10.1093/schbul/sbm031PubMedGoogle ScholarCrossref
6.
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. doi:10.1001/archgenpsychiatry.2007.3PubMedGoogle ScholarCrossref
7.
Cannon  TD, van Erp  TG, Bearden  CE,  et al.  Early and late neurodevelopmental influences in the prodrome to schizophrenia: contributions of genes, environment, and their interactions.  Schizophr Bull. 2003;29(4):653-669. doi:10.1093/oxfordjournals.schbul.a007037PubMedGoogle ScholarCrossref
8.
McGlashan  TH.  A selective review of recent North American long-term followup studies of schizophrenia.  Schizophr Bull. 1988;14(4):515-542. doi:10.1093/schbul/14.4.515PubMedGoogle ScholarCrossref
9.
Franke  P, Maier  W, Hardt  J, Hain  C.  Cognitive functioning and anhedonia in subjects at risk for schizophrenia.  Schizophr Res. 1993;10(1):77-84. doi:10.1016/0920-9964(93)90079-XPubMedGoogle ScholarCrossref
10.
Sarfati  Y, Hardy-Baylé  M-C.  Could cognitive vulnerability identify high-risk subjects for schizophrenia?  Am J Med Genet. 2002;114(8):893-897. doi:10.1002/ajmg.b.10251PubMedGoogle ScholarCrossref
11.
Hawkins  KA, Addington  J, Keefe  RS,  et al.  Neuropsychological status of subjects at high risk for a first episode of psychosis.  Schizophr Res. 2004;67(2-3):115-122. doi:10.1016/j.schres.2003.08.007PubMedGoogle ScholarCrossref
12.
Yung  AR, Yuen  HP, McGorry  PD,  et al.  Mapping the onset of psychosis: the Comprehensive Assessment of At-Risk Mental States.  Aust N Z J Psychiatry. 2005;39(11-12):964-971. doi:10.1080/j.1440-1614.2005.01714.xPubMedGoogle ScholarCrossref
13.
Brewer  WJ, Wood  SJ, Phillips  LJ,  et al.  Generalized and specific cognitive performance in clinical high-risk cohorts: a review highlighting potential vulnerability markers for psychosis.  Schizophr Bull. 2006;32(3):538-555. doi:10.1093/schbul/sbj077PubMedGoogle ScholarCrossref
14.
Keefe  RSE, 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. doi:10.1016/j.schres.2006.06.041PubMedGoogle ScholarCrossref
15.
Lencz  T, Smith  CW, McLaughlin  D,  et al.  Generalized and specific neurocognitive deficits in prodromal schizophrenia.  Biol Psychiatry. 2006;59(9):863-871. doi:10.1016/j.biopsych.2005.09.005PubMedGoogle ScholarCrossref
16.
Niendam  TA, Bearden  CE, Johnson  JK,  et al.  Neurocognitive performance and functional disability in the psychosis prodrome.  Schizophr Res. 2006;84(1):100-111. doi:10.1016/j.schres.2006.02.005PubMedGoogle ScholarCrossref
17.
Seidman  LJ, Giuliano  AJ, Smith  CW,  et al.  Neuropsychological functioning in adolescents and young adults at genetic risk for schizophrenia and affective psychoses: results from the Harvard and Hillside Adolescent High Risk Studies.  Schizophr Bull. 2006;32(3):507-524. doi:10.1093/schbul/sbj078PubMedGoogle ScholarCrossref
18.
Eastvold  AD, Heaton  RK, Cadenhead  KS.  Neurocognitive deficits in the (putative) prodrome and first episode of psychosis.  Schizophr Res. 2007;93(1-3):266-277. doi:10.1016/j.schres.2007.03.013PubMedGoogle ScholarCrossref
19.
Pukrop  R, Ruhrmann  S, Schultze-Lutter  F, Bechdolf  A, Brockhaus-Dumke  A, Klosterkötter  J.  Neurocognitive indicators for a conversion to psychosis: comparison of patients in a potentially initial prodromal state who did or did not convert to a psychosis.  Schizophr Res. 2007;92(1-3):116-125. doi:10.1016/j.schres.2007.01.020PubMedGoogle ScholarCrossref
20.
Ozgürdal  S, Littmann  E, Hauser  M,  et al.  Neurocognitive performances in participants of at-risk mental state for schizophrenia and in first-episode patients.  J Clin Exp Neuropsychol. 2009;31(4):392-401. doi:10.1080/13803390802206406PubMedGoogle ScholarCrossref
21.
Walder  DJ, Mittal  V, Trotman  HD, McMillan  AL, Walker  EF.  Neurocognition and conversion to psychosis in adolescents at high-risk.  Schizophr Res. 2008;101(1-3):161-168. doi:10.1016/j.schres.2007.12.477PubMedGoogle ScholarCrossref
22.
Pukrop  R, Klosterkötter  J.  Neurocognitive indicators of clinical high-risk states for psychosis: a critical review of the evidence.  Neurotox Res. 2010;18(3-4):272-286. doi:10.1007/s12640-010-9191-1PubMedGoogle 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. doi:10.1093/schbul/sbp155PubMedGoogle ScholarCrossref
24.
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. doi:10.1001/archgenpsychiatry.2011.1592PubMedGoogle ScholarCrossref
25.
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. doi:10.2174/138161212799316019PubMedGoogle ScholarCrossref
26.
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. doi:10.1093/schbul/sbt085PubMedGoogle ScholarCrossref
27.
Seidman  LJ, Shapiro  DI, Stone  WS,  et al.  Association of neurocognition with transition to psychosis: baseline functioning in the second phase of the North American Prodrome Longitudinal Study.  JAMA Psychiatry. 2016;73(12):1239-1248. doi:10.1001/jamapsychiatry.2016.2479PubMedGoogle ScholarCrossref
28.
Heinrichs  RW.  Meta-analysis and the science of schizophrenia: variant evidence or evidence of variants?  Neurosci Biobehav Rev. 2004;28(4):379-394. doi:10.1016/j.neubiorev.2004.06.003PubMedGoogle ScholarCrossref
29.
Heinrichs  RW.  The primacy of cognition in schizophrenia.  Am Psychol. 2005;60(3):229-242. doi:10.1037/0003-066X.60.3.229PubMedGoogle ScholarCrossref
30.
Heinrichs  RW, Zakzanis  KK.  Neurocognitive deficit in schizophrenia: a quantitative review of the evidence.  Neuropsychology. 1998;12(3):426-445. doi:10.1037/0894-4105.12.3.426PubMedGoogle ScholarCrossref
31.
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. doi:10.1037/a0014708PubMedGoogle ScholarCrossref
32.
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. doi:10.1016/j.schres.2010.06.021PubMedGoogle ScholarCrossref
33.
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. doi:10.1176/appi.ajp.2009.09040574PubMedGoogle ScholarCrossref
34.
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. doi:10.1001/archpsyc.59.5.449PubMedGoogle ScholarCrossref
35.
Fuller  R, Nopoulos  P, Arndt  S, O’Leary  D, Ho  B-C, Andreasen  NC.  Longitudinal assessment of premorbid cognitive functioning in patients with schizophrenia through examination of standardized scholastic test performance.  Am J Psychiatry. 2002;159(7):1183-1189. doi:10.1176/appi.ajp.159.7.1183PubMedGoogle ScholarCrossref
36.
Keefe  RSE, Kahn  RS.  Cognitive decline and disrupted cognitive trajectory in schizophrenia.  JAMA Psychiatry. 2017;74(5):535-536. doi:10.1001/jamapsychiatry.2017.0312PubMedGoogle ScholarCrossref
37.
Niendam  TA, Bearden  CE, Zinberg  J, Johnson  JK, O’Brien  M, Cannon  TD.  The course of neurocognition and social functioning in individuals at ultra high risk for psychosis.  Schizophr Bull. 2007;33(3):772-781. doi:10.1093/schbul/sbm020PubMedGoogle ScholarCrossref
38.
Reichenberg  A, Weiser  M, Rapp  MA,  et al.  Elaboration on premorbid intellectual performance in schizophrenia: premorbid intellectual decline and risk for schizophrenia.  Arch Gen Psychiatry. 2005;62(12):1297-1304. doi:10.1001/archpsyc.62.12.1297PubMedGoogle ScholarCrossref
39.
Reichenberg  A, Harvey  PD.  Neuropsychological impairments in schizophrenia: integration of performance-based and brain imaging findings.  Psychol Bull. 2007;133(5):833-858. doi:10.1037/0033-2909.133.5.833PubMedGoogle ScholarCrossref
40.
Cannon  M, Moffitt  TE, Caspi  A, Murray  RM, Harrington  H, Poulton  R.  Neuropsychological performance at the age of 13 years and adult schizophreniform disorder: prospective birth cohort study.  Br J Psychiatry. 2006;189(5):463-464. doi:10.1192/bjp.bp.105.020552PubMedGoogle ScholarCrossref
41.
Niendam  TA, Bearden  CE, Rosso  IM,  et al.  A prospective study of childhood neurocognitive functioning in schizophrenic patients and their siblings.  Am J Psychiatry. 2003;160(11):2060-2062. doi:10.1176/appi.ajp.160.11.2060PubMedGoogle ScholarCrossref
42.
Groom  MJ, Jackson  GM, Calton  TG,  et al.  Cognitive deficits in early-onset schizophrenia spectrum patients and their non-psychotic siblings: a comparison with ADHD.  Schizophr Res. 2008;99(1-3):85-95. doi:10.1016/j.schres.2007.11.008PubMedGoogle ScholarCrossref
43.
Wilson  RS, Segawa  E, Hizel  LP, Boyle  PA, Bennett  DA.  Terminal dedifferentiation of cognitive abilities.  Neurology. 2012;78(15):1116-1122. doi:10.1212/WNL.0b013e31824f7ff2PubMedGoogle ScholarCrossref
44.
Hülür  G, Wilhelm  O, Robitzsch  A.  Intelligence differentiation in early childhood.  J Individ Differ. 2011;32(3):170-179. doi:10.1027/1614-0001/a000049Google ScholarCrossref
45.
Knowles  EEM, Weiser  M, David  AS,  et al.  Dedifferentiation and substitute strategy: deconstructing the processing-speed impairment in schizophrenia.  Schizophr Res. 2012;142(1-3):129-136. doi:10.1016/j.schres.2012.08.020PubMedGoogle ScholarCrossref
46.
Knowles  EEM, Weiser  M, David  AS, Glahn  DC, Davidson  M, Reichenberg  A.  The puzzle of processing speed, memory, and executive function impairments in schizophrenia: fitting the pieces together.  Biol Psychiatry. 2015;78(11):786-793. doi:10.1016/j.biopsych.2015.01.018PubMedGoogle ScholarCrossref
47.
Lam  M, Collinson  SL, Eng  GK,  et al.  Refining the latent structure of neuropsychological performance in schizophrenia.  Psychol Med. 2014;44(16):3557-3570. doi:10.1017/S0033291714001020PubMedGoogle ScholarCrossref
48.
Dickinson  D, Goldberg  TE, Gold  JM, Elvevåg  B, Weinberger  DR.  Cognitive factor structure and invariance in people with schizophrenia, their unaffected siblings, and controls.  Schizophr Bull. 2011;37(6):1157-1167. doi:10.1093/schbul/sbq018PubMedGoogle ScholarCrossref
49.
Lee  J, Rekhi  G, Mitter  N,  et al.  The Longitudinal Youth at Risk Study (LYRIKS)—an Asian UHR perspective.  Schizophr Res. 2013;151(1-3):279-283. doi:10.1016/j.schres.2013.09.025PubMedGoogle ScholarCrossref
50.
First  MB, Spitzer  RL, Gibbon  M, Williams  JBW.  Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. New York, NY: Biometrics Research, New York State Psychiatric Institute; 2002.
51.
Kay  SR, Fiszbein  A, Opler  LA.  The Positive and Negative Syndrome Scale (PANSS) for schizophrenia.  Schizophr Bull. 1987;13(2):261-276. doi:10.1093/schbul/13.2.261PubMedGoogle ScholarCrossref
52.
Beck  AT, Epstein  N, Brown  G, Steer  RA.  An inventory for measuring clinical anxiety: psychometric properties.  J Consult Clin Psychol. 1988;56(6):893-897. doi:10.1037/0022-006X.56.6.893PubMedGoogle ScholarCrossref
53.
Addington  D, Addington  J, Maticka-Tyndale  E.  Assessing depression in schizophrenia: the Calgary Depression Scale.  Br J Psychiatry Suppl. 1993;(22):39-44.PubMedGoogle Scholar
54.
Morosini  PL, Magliano  L, Brambilla  L, Ugolini  S, Pioli  R.  Development, reliability and acceptability of a new version of the DSM-IV Social and Occupational Functioning Assessment Scale (SOFAS) to assess routine social functioning.  Acta Psychiatr Scand. 2000;101(4):323-329. doi:10.1034/j.1600-0447.2000.101004323.xPubMedGoogle Scholar
55.
Chong  S-A, Campbell  A, Chee  M,  et al.  The Singapore flagship programme in translational and clinical research in psychosis.  Early Interv Psychiatry. 2011;5(4):290-300. doi:10.1111/j.1751-7893.2011.00304.xPubMedGoogle ScholarCrossref
56.
Wechsler  D.  Wechsler Memory Scale. San Antonio, TX: Psychological Corporation; 1997.
57.
Keefe  RSE, Harvey  PD, Goldberg  TE,  et al.  Norms and standardization of the Brief Assessment of Cognition in Schizophrenia (BACS).  Schizophr Res. 2008;102(1-3):108-115. doi:10.1016/j.schres.2008.03.024PubMedGoogle ScholarCrossref
58.
Emrich  HM.  A three-component-system hypothesis of psychosis: impairment of binocular depth inversion as an indicator of a functional dysequilibrium.  Br J Psychiatry Suppl. 1989;(5):37-39.PubMedGoogle Scholar
59.
Koethe  D, Kranaster  L, Hoyer  C,  et al.  Binocular depth inversion as a paradigm of reduced visual information processing in prodromal state, antipsychotic-naïve and treated schizophrenia.  Eur Arch Psychiatry Clin Neurosci. 2009;259(4):195-202. doi:10.1007/s00406-008-0851-6PubMedGoogle ScholarCrossref
60.
Cornblatt  BA, Risch  NJ, Faris  G, Friedman  D, Erlenmeyer-Kimling  L.  The Continuous Performance Test, Identical Pairs Version (CPT-IP): I. new findings about sustained attention in normal families.  Psychiatry Res. 1988;26(2):223-238. doi:10.1016/0165-1781(88)90076-5PubMedGoogle ScholarCrossref
61.
Gibson  CM, Penn  DL, Prinstein  MJ, Perkins  DO, Belger  A.  Social skill and social cognition in adolescents at genetic risk for psychosis.  Schizophr Res. 2010;122(1-3):179-184. doi:10.1016/j.schres.2010.04.018PubMedGoogle ScholarCrossref
62.
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. doi:10.1192/bjp.bp.106.031195PubMedGoogle ScholarCrossref
63.
Fox  E, Griggs  L, Mouchlianitis  E.  The detection of fear-relevant stimuli: are guns noticed as quickly as snakes?  Emotion. 2007;7(4):691-696. doi:10.1037/1528-3542.7.4.691PubMedGoogle ScholarCrossref
64.
Trampush  JW, Lencz  T, Knowles  E,  et al.  Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment.  Am J Med Genet B Neuropsychiatr Genet. 2015;168B(5):363-373. doi:10.1002/ajmg.b.32319PubMedGoogle ScholarCrossref
65.
Kaiser  HF.  The varimax criterion for analytic rotation in factor analysis.  Psychometrika. 1958;23(3):187-200.doi: doi:10.1007/BF02289233Google ScholarCrossref
66.
Reichenberg  A.  Cognitive impairment as a risk factor for psychosis.  Dialogues Clin Neurosci. 2005;7(1):31-38.PubMedGoogle Scholar
67.
Kendler  KS, Ohlsson  H, Mezuk  B, Sundquist  JO, Sundquist  K.  Observed cognitive performance and deviation from familial cognitive aptitude at age 16 years and ages 18 to 20 years and risk for schizophrenia and bipolar illness in a Swedish national sample.  JAMA Psychiatry. 2016;73(5):465-471. doi:10.1001/jamapsychiatry.2016.0053PubMedGoogle ScholarCrossref
68.
Hülür  G, Ram  N, Willis  SL, Schaie  KW, Gerstorf  D.  Cognitive dedifferentiation with increasing age and proximity of death: within-person evidence from the Seattle Longitudinal Study.  Psychol Aging. 2015;30(2):311-323. doi:10.1037/a0039260PubMedGoogle ScholarCrossref
69.
Kraus  M, Rapisarda  A, Lam  M,  et al.  Disrupted latent inhibition in individuals at ultra high-risk for developing psychosis.  Schizophr Res Cogn. 2016;6:1-8. doi:10.1016/j.scog.2016.07.003PubMedGoogle ScholarCrossref
70.
Öhman  A, Flykt  A, Esteves  F.  Emotion drives attention: detecting the snake in the grass.  J Exp Psychol Gen. 2001;130(3):466-478. doi:10.1037/0096-3445.130.3.466PubMedGoogle ScholarCrossref
71.
Kraus  MS, Keefe  RS, Krishnan  RK.  Memory-prediction errors and their consequences in schizophrenia.  Neuropsychol Rev. 2009;19(3):336-352. doi:10.1007/s11065-009-9106-1PubMedGoogle ScholarCrossref
72.
Carter  CS, Barch  DM, Buchanan  RW,  et al.  Identifying cognitive mechanisms targeted for treatment development in schizophrenia: an overview of the first meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia Initiative.  Biol Psychiatry. 2008;64(1):4-10. doi:10.1016/j.biopsych.2008.03.020PubMedGoogle ScholarCrossref
73.
Allen  DN, Strauss  GP, Donohue  B, van Kammen  DP.  Factor analytic support for social cognition as a separable cognitive domain in schizophrenia.  Schizophr Res. 2007;93(1-3):325-333. doi:10.1016/j.schres.2007.02.008PubMedGoogle ScholarCrossref
74.
Nuechterlein  KH, Barch  DM, Gold  JM, Goldberg  TE, Green  MF, Heaton  RK.  Identification of separable cognitive factors in schizophrenia.  Schizophr Res. 2004;72(1):29-39. doi:10.1016/j.schres.2004.09.007PubMedGoogle ScholarCrossref
75.
van Hooren  S, Versmissen  D, Janssen  I,  et al.  Social cognition and neurocognition as independent domains in psychosis.  Schizophr Res. 2008;103(1-3):257-265. doi:10.1016/j.schres.2008.02.022PubMedGoogle ScholarCrossref
76.
Green  MF, Horan  WP.  Social cognition in schizophrenia.  Curr Dir Psychol Sci. 2010;19(4):243-248. doi:10.1177/0963721410377600Google ScholarCrossref
77.
Green  MF, Bearden  CE, Cannon  TD,  et al.  Social cognition in schizophrenia, part 1: performance across phase of illness.  Schizophr Bull. 2012;38(4):854-864. doi:10.1093/schbul/sbq171PubMedGoogle ScholarCrossref
78.
Keefe  RSE, McClintock  SM, Roth  RM, Doraiswamy  PM, Tiger  S, Madhoo  M.  Cognitive effects of pharmacotherapy for major depressive disorder: a systematic review.  J Clin Psychiatry. 2014;75(8):864-876. doi:10.4088/JCP.13r08609PubMedGoogle ScholarCrossref
79.
Rosenblat  JD, Kakar  R, McIntyre  RS.  The cognitive effects of antidepressants in major depressive disorder: a systematic review and meta-analysis of randomized clinical trials.  Int J Neuropsychopharmacol. 2015;19(2):pyv082. doi:10.1093/ijnp/pyv082PubMedGoogle ScholarCrossref
Original Investigation
September 2018

Longitudinal Cognitive Changes in Young Individuals at Ultrahigh Risk for Psychosis

Author Affiliations
  • 1Research Division, Institute of Mental Health, Singapore, Singapore
  • 2Department of General Psychiatry 1, Institute of Mental Health, Singapore, Singapore
  • 3Neuroscience and Behavioural Disorders, Duke-NUS Medical School, Singapore, Singapore
  • 4Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina
JAMA Psychiatry. 2018;75(9):929-939. doi:10.1001/jamapsychiatry.2018.1668
Key Points

Question  Do baseline and longitudinal cognitive architecture discriminate healthy controls from subgroups of young individuals at risk for psychosis?

Findings  This multiple-group design study involving 384 healthy controls and 173 individuals at ultrahigh risk for psychosis found that baseline cognitive architecture differentiated healthy controls from converters and nonremitters. Remitters were found to recover their cognitive deficits over time, but nonremitters did not.

Meaning  Cognitive deficits appear to identify the individuals most likely to develop psychosis and appear to reflect an underlying deterioration of a person’s clinical condition over time.

Abstract

Importance  Cognitive deficits are a key feature of risk for psychosis. Longitudinal changes in cognitive architecture may be associated with the social and occupational functioning in young people.

Objectives  To examine longitudinal profiles of cognition in individuals at ultrahigh risk (UHR) for psychosis, compared with healthy controls, and to investigate the association of cognition with functioning.

Design, Setting, and Participants  This study has a multiple-group prospective design completed in 24 months and was conducted from January 1, 2009, to November 11, 2012, as part of the Longitudinal Youth at-Risk Study conducted in Singapore. Participants either were recruited from psychiatric outpatient clinics, educational institutions, and community mental health agencies or self-referred. Follow-up assessments were performed every 6 months for 2 years or until conversion to psychosis. Individuals with medical causes for psychosis, current illicit substance use, or color blindness were excluded. Data analysis was conducted from June 2014 to May 2018.

Main Outcomes and Measures  Neuropsychological, perceptual, and social cognitive tasks; semi-structured interviews, and the Structured Clinical Interview for DSM-IV Axis I disorders were administered every 6 months. The UHR status of nonconverters, converters, remitters, and nonremitters was monitored. Cognitive domain scores and functioning were investigated longitudinally.

Results  In total, 384 healthy controls and 173 UHR individuals between ages 14 and 29 years were evaluated prospectively. Of the 384 healthy controls, 153 (39.8%) were female and 231 (60.2%) were male with a mean (SD) age of 21.69 (3.26) years. Of the 173 individuals at UHR for psychosis, 56 (32.4%) were female and 117 (67.6%) were male with a mean (SD) age of 21.27 (3.52) years). After 24 months of follow-up, 383 healthy controls (99.7%) and 122 individuals at UHR for psychosis (70.5%) remained. Baseline cognitive deficits were associated with psychosis conversion later (mean odds ratio [OR], 1.66; combined 95% CI, 1.08-2.83; P = .04) and nonremission of UHR status (mean OR, 1.67; combined 95% CI, 1.09-2.95; P = .04). Five cognitive components—social cognition, attention, verbal fluency, general cognitive function, and perception—were obtained from principal components analysis. Longitudinal component structure change was observed in general cognitive function (maximum vertical deviation = 0.59; χ2 = 8.03; P = .01). Group-by-time interaction on general cognitive function (F = 12.23; η2 = 0.047; P < .001) and perception (F = 8.33; η2 = 0.032; P < .001) was present. Changes in attention (F = 5.65; η2 = 0.013; P = .02) and general cognitive function (F = 7.18; η2 = 0.014; P = .01) accounted for longitudinal changes in social and occupational functioning.

Conclusions and Relevance  Individuals in this study who met the UHR criteria appeared to demonstrate cognitive deficits, and those whose UHR status remitted were seen to recover cognitively. Cognition appeared as poor in nonremitters and appeared to be associated with poor functional outcome. This study suggests that cognitive dimensions are sensitive to the identification of young individuals at risk for psychosis and to the longitudinal course of those at highest risk.

Introduction

Individuals who are prodromal to schizophrenia have a higher risk for and transition rate to psychosis compared with the general population.1-7 Cognitive deficits are also a predictor associated with psychosis.3,7-27 Cognitive impairments are the core disabling factors in psychosis and schizophrenia.28-31 Meta-analytic evidence indicates that cognitive deficits are present in individuals at ultrahigh risk (UHR) for psychosis.24-26,32-34 There is a 35% likelihood that the presence of symptoms—functional or cognitive manifestations—in high-risk, care-seeking individuals predates psychosis.6 However, systematic evidence is scarce for longitudinal cognitive trajectories in individuals at UHR for psychosis. Recent reports confirm that cognitive deficits at baseline are associated with conversion to psychosis, but the reports have not addressed the longitudinal cognitive profiles of these individuals.27 Equivocal evidence ranges from modest improvements in cognition in converters and first episode psychosis26 to suggestions that cognitive decline may be a strong factor in eventual psychosis.33,35,36 Previous reports indicate that approximately 50% of individuals at UHR for psychosis improve spontaneously within a short follow-up time frame.37

Longitudinal schizophrenia cognitive studies may offer insights to UHR cognitive trajectories. Premorbid cognitive deficits were found to be associated with schizophrenia.33,38-40 Cognitive impairment can be observed also in nonpsychotic family members of psychotic patients.41,42 Progressive changes in cognition over a 30-year period were reported in children who later developed schizophrenia.33 Two aspects of cognitive trajectories may be investigated: (1) means-based change, where differential time-based cognitive changes may exist between healthy individuals and those at UHR for psychosis, and (2) covariance-based change. The latter involves changes in the cognitive component structure, as defined by cognitive tests, over time43,44 and is known as the cognitive dedifferentiation hypothesis. This dedifferentiation is associated with poorer cognitive function with increased covariation across cognitive tests, a phenomenon previously observed in aging research.43,44 Intriguingly, forms of cognitive dedifferentiation were also noted in schizophrenia,45,46 where a subtle increase in test covariation was previously reported.47,48

We studied the prospective cognitive trajectories of individuals at UHR for psychosis. We expected to observe the greatest decline in cognitive performance over time among individuals at UHR who converted to psychosis compared with nonconverters and healthy controls. In individuals whose UHR status did not remit during the follow-up period, we expected to observe declining cognitive performance compared with remitters and healthy controls. We hypothesized that increased test covariance would be present as a function of time for individuals whose UHR status did not remit over time. Finally, we examined how changes in cognition as a function of time affected the social and occupational functioning of individuals at UHR for psychosis.

Methods

Ethics approval for this study was provided by the Singapore National Healthcare Group's Domain Specific Review Board. Written informed consent was obtained from all participants, and consent from a legally acceptable representative was obtained for minors (younger than 21 years) as required by local regulations. This study was conducted from January 1, 2009, to November 11, 2012. Data analysis was conducted from June 2014 to May 2018.

Participants

This study, as part of the Longitudinal Youth at-Risk Study conducted in Singapore,49 included 384 healthy controls and 173 individuals who met the criteria for UHR for psychosis.12 After 24 months, 383 healthy controls (99.7%) and 122 individuals at UHR for psychosis (70.5%) had remained in the study. Participants either were recruited from psychiatric outpatient clinics, educational institutes, and community mental health agencies or were self-referred. Individuals with neurological causes for psychosis, current illicit substance use, or color blindness were excluded. All participants were between 14 and 29 years of age. Their UHR status was ascertained by the Comprehensive Assessment of At-Risk Mental States,12 and their psychiatric history was evaluated with the Structured Clinical Interview for DSM-IV Axis I Disorders.50 Healthy controls did not fulfill UHR criteria, had no psychiatric disorder, and had no family history of psychosis.

Follow-up assessments at 6-month intervals for 2 years or until conversion to psychosis included the Positive and Negative Syndrome Scale,51 Beck Anxiety Inventory,52 Calgary Depression Scale for Schizophrenia,53 and the Social and Occupational Functioning Assessment Scale.54 Remission status was assessed at the 12- and 24-month time points. Individuals at UHR for psychosis were categorized into converters or nonconverters and remitters or nonremitters. Individuals at UHR at baseline but who no longer fulfilled UHR criteria at the 24-month time point were categorized as remitters. Those who met UHR criteria at final assessment or had converted to psychosis were categorized as nonremitters. In subsequent analyses, 2 sets of analysis were carried out involving (1) healthy controls, converters, and nonconverters and (2) healthy controls, remitters, and nonremitters. Details of the sampling methodology and the demographic characteristics of the sample were reported elsewhere.49,55

Cognitive Measures

The Wechsler Memory Scale-III Spatial Span56; the Brief Assessment of Cognition in Schizophrenia,57 which consists of verbal memory, digit sequencing, token motor task, verbal fluency, symbol coding, and Tower of London tests; the Binocular Depth Inversion task58,59; the Continuous Performance Test—Identical Pairs60; the High-Risk Social Challenge skills interview61; the Babble task62; and the Snakes in the Grass test63 were administered. Cognitive tests were adjusted for age, sex, age × sex, age,2 and age2 × sex via linear regression modeling,64 and standardized residual scores were used for subsequent analysis. Cognitive scores were standardized against healthy control baseline measures. (See the Supplement for eAppendixes 1 and 2 [with eFigure 1], which deal with the concept of testing factor structure changes, and eAppendix 3 for data preprocessing details.)

Statistical Analysis

Ordinal logistic regression was conducted to examine between-groups baseline cognitive differences. Univariate models that were P < .05 were selected for subsequent analysis. Linear mixed models were carried out to examine cognitive changes, allowing the inclusion of all longitudinal data available for each participant and the examination of the association of maturational stage with age-related trajectory changes over time. Stuart-Maxwell Marginal Homogeneity test was used to examine the divergence of the estimated test score distributions between the baseline and the 24-month follow-up for each group; these distributions were Bonferroni corrected. A principal components analysis (PCA) was conducted on baseline and 24-month cognitive batteries to investigate the cognitive component structure changes. Component loading vectors were compared via the Kolmogorov-Smirnov test (see eAppendixes 1 and 2 in the Supplement), and the comparisons were Bonferroni corrected. Cognitive components scores were compared using 1-way and repeated measures analysis of variance to examine group-by-time interactions. Repeated-measures general linear models were used to investigate the association of cognitive component changes with the rate of functioning changes. Bonferroni corrections for all intergroup comparisons were completed; further details of the data analysis are reported in eAppendix 3 in the Supplement. Analyses were conducted using SPSS, version 22.0 (IBM), unless otherwise noted.

Results
Demographics

Prospectively we evaluated 384 healthy controls (of whom 153 [39.8%] were female and 231 [60.2%] were male with a mean [SD] age of 21.69 [3.26] years) and 173 individuals at UHR for psychosis (of whom 56 [32.4%] were female and 117 [67.6%] were male with a mean [SD] age of 21.27 [3.52] years) who were between 14 and 29 years of age. Individuals at UHR for psychosis were further studied according to their conversion status (17 converters, of whom 3 [17.6%] were female with a mean [SD] age of 20.41 [3.18] years; 156 nonconverters, of whom 53 [34.4%] were female with a mean [SD] age of 21.37 [3.55] years) and remission status (84 remitters, of whom 28 [33.3%] were female with a mean [SD] age of 21.15 [3.41] years; 89 nonremitters, of whom 28 [31.5%] were female with a mean [SD] age of 21.38 [3.64] years). Further demographic characteristics are reported in the Table.

No statistically significant differences in sex proportions were found across healthy controls, nonconverters, and converters (χ21 = 3.74; P = .05) as well as healthy controls, remitters, and nonremitters (χ21 = 3.74; P = .05). No statistically significant differences in age were observed among healthy controls, nonconverters, and converters (F = 1.53; P = .22) and healthy controls, remitters, and nonremitters (F = 1.02; P = .36). Statistically significant higher Comprehensive Assessment of At-Risk Mental State scores were observed in individuals at UHR for psychosis compared with healthy controls (F = 766.74; P < .001; η2 = 0.581). No notable differences were observed in the Positive and Negative Syndrome Scale, Calgary Depression Scale for Schizophrenia, and Beck Anxiety Inventory measures across groups (Table).

Baseline Group Differences: Ordinal Logistic Regression

Baseline cognitive profiles of all tests are reported in eFigure 2 in the Supplement. Statistically significant between-group differences were found in verbal memory, digit sequencing, token motor task, verbal fluency, symbol coding, and Tower of London tests; the Wechsler Memory Scale-III Spatial Span; the High-Risk Social Challenge skills interview; the Snakes in the Grass test, and the Continuous Performance Test—Identical Pairs across groups (eTable 1 in the Supplement). Post hoc independent, unpaired, 2-tailed t tests revealed differences among healthy controls, nonconverters; healthy controls, converters; and healthy controls, nonremitters (eTable 1 in the Supplement). Baseline cognitive deficits were associated with psychosis conversion (mean odds ratio [OR], 1.66; combined 95% CI, 1.08-2.83; P = .04) and nonremission of UHR status (mean OR, 1.67; combined 95% CI, 1.09-2.95; P = .04).

Cognitive Changes: Linear Mixed Models

Verbal memory, digit sequencing, token motor task, and symbol coding tests; the High-Risk Social Challenge skills interview; the Snakes in the Grass test; and the Continuous Performance Test—Identical Pairs showed longitudinal changes across all groups. Nonlinear changes in cognitive trajectories were also found (Figure 1; eTable 2 in the Supplement). Group-level differences were expected, but few group-by-time interactions were observed across cognitive tests (eTable 2 in the Supplement). The distributions of cognitive linear mixed models estimated scores were different at baseline and 24-month follow-up for all groups (eTable 6 in the Supplement). Increasing effect sizes across groups suggested that cognitive trajectories in nonremitters and converters were most divergent, pointing to subtle underlying perturbations of test covariation. Maturation stage (median split of age at baseline) and age-related trajectory changes were unremarkable. Statistically significant model changes were mostly due to the variability within the clinical groups rather than by observed group differentials (eFigures 3-9 and eTables 3-5 in the Supplement).

Psychometric Architecture of Cognitive Constructs: Principal Components Analysis

Twenty cognitive subtests with nominally significant (P < .05) baseline group differences were selected for PCA. Five orthogonal principal components were extracted using the Kaiser criterion65 (λ >1). Social cognition, attention, verbal fluency, general cognitive function (GCF), and perception were the 5 principal components that explained variances of 63.3% (healthy control), 74.1% (remitter), and 71.2% (nonremitter) at baseline and variances of 62.8% (healthy control), 75.7% (remitter), and 84.4% (nonremitter) at 24-month follow-up. The reliability of cognitive measures was comparable at baseline (overall α = .831; healthy control α = .792; remitter α = .845; nonremitter α = .809) and at 24-month follow-up (overall α = .848; healthy control α = .818; remitter α = .863; nonremitter α = .900). Component loadings by group and follow-up are represented in Figure 2.

Component load differences in GCF were noted among healthy controls, remitters, and nonremitters. Stark differences in component load between GCF baseline and 24-month follow-up in nonremitters were observed. Longitudinal changes for the component loadings for GCF in nonremitters were also observed (maximum vertical deviation = 0.59; χ2 = 8.03; P = .01). The observation is supported by results of the Kolmogorov-Smirnov tests that examined component load vectors across the PCA output (eTable 7 in the Supplement). Different load profiles were present among healthy controls, remitters, and nonremitters at baseline and at 24-month follow-up for the perception component, where subtler trends in social cognition load changes appeared in nonremitters but failed to survive the Bonferroni correction (eTable 7 in the Supplement).

Longitudinal Change in Cognitive Constructs

Weighted and nonweighted cognitive component scores were computed on the basis of the PCA results (eAppendix 3 in the Supplement). Repeated-measures analysis of variance was conducted on the cognitive component scores. Bonferroni-corrected α level of .025 was used to handle 2 test sets that evaluated the same hypothesis. Longitudinal changes were found for attention, GCF, and perception (Figure 3; eTable 8 in the Supplement). Post hoc Bonferroni-adjusted paired sample 2-tailed t tests showed improved performance in remitters, which accounted for the overall model effects. In nonweighted component scores, only GCF was found to be significant. Between-participant analysis of variance tests at both baseline and follow-up further confirmed that, although remitters appeared more similar to nonremitters at baseline for social cognition, attention, and GCF, their performance improved spontaneously with time, and remitters were more similar to healthy controls at 24-month follow-up (Figure 3; eTable 8 in the Supplement).

Relation to Functioning

There was a main association of time with the range of social and occupational functioning at baseline and 24-month follow-up (Figure 4; eTable 9 in the Supplement). A statistically significant group-by-time interaction was observed, suggesting differential rates of change of functioning among healthy controls, remitters, and nonremitters. Group-by-time interaction on GCF (F = 12.23; η2 = 0.047; P < .001) and perception (F = 8.33; η2 = 0.032; P < .001) was present. Change in attention and GCF components appeared to partially mediate change in functioning (eTable 9 in the Supplement). Post hoc models revealed that change in the attention component (F = 5.65; η2 = 0.013; P = .02) partially mediated the spontaneous improvements in functioning in remitters and nonremitters compared with healthy controls (Figure 3E and Figure 4C and D). Change in GCF (F = 7.18; η2 = 0.014; P = .01) fully accounted for a differential rate of change in functioning between remitters and nonremitters. All post hoc comparisons survived Bonferroni correction.

Discussion

To our knowledge, this study is the largest prospective single-site report of a case-control sample of individuals at UHR for psychosis. Comparisons between remitters and nonremitters suggested that above baseline cognition, trajectory and component-based analyses can identify psychosis and nonremission from illness. Worsening cognitive function over time may be a prime factor in eventual, if not incipient, psychosis.33,36,66,67

Baseline Differences and Prediction Models

The study results are consistent with literature that shows significant cognitive deficits in UHR samples. Participants at UHR for psychosis were differentiated from healthy controls, and converters were differentiated from nonconverters according to baseline cognitive performance. Cognitive modeling results demonstrated statistically significant differences not only among healthy controls, converters, and nonconverters but also between individuals who met UHR criteria at baseline and those whose UHR status remitted, compared with those who had no remission.

Prospective Trajectory Changes

Longitudinal modeling of cognitive performance revealed that most individuals improved with repeated testing every 6 months in the 24-month follow-up. Statistically significant group differences in trajectories were observed, suggesting that baseline variations in cognitive performance interact differently with time in the different groups. These results were consistent with earlier reports indicating that some individuals at UHR for psychosis display cognitive improvements with time.67 Practice effects, pharmacological effects, and diagnostic heterogeneity67 were alternative explanations for the phenomenon, but the more fine-grained follow-up neuropsychological test data reported here may offer further clarification of the cognitive trajectories of individuals at UHR for psychosis. Gradual increases in variability of test performance over time suggest the possibility that the underlying cognitive architecture may have devolved in converters and nonremitters during follow-up. Thus, measures of dedifferentiation of cognitive components may be 1 of the most powerful factors in later conversion and nonremission in individuals at risk for psychosis. Additional analysis of the maturational stage indicated that, between age 14 and 29 years, the most cognitive trajectory changes could be associated with clinical outcomes. Improvement of cognitive performance over time seems to be associated with age, but differential age-related cognitive trajectories do not appear to be present in groups at UHR for psychosis. Nevertheless, larger samples and wider age ranges might be required to further examine differential maturational profiles.

Cognitive Architecture and Shifts in Component Loadings on Test Performance

Instead of maximizing the separation of cognitive components, we extracted them orthogonally to make apparent the cross-loading of cognitive subtests. Comparing PCA loading vectors revealed a significant shift in loading patterns between baseline and follow-up in nonremitters, implying the subtle changes in cognitive architecture over time. To our knowledge, such architectural changes have not been reported in previous studies of individuals at UHR for psychosis. We postulate that examining the prospective differential contribution of cognitive components to test performance could reveal subtle cognitive changes in at-risk states that will help differentiate between remitters and nonremitters. Covariance strength across cognitive test performance has been shown to yield vital insights into brain function in aging research43,44,68 and to be a property of deficit cognition in schizophrenia.45,47,48 Decreasing differentiation of GCF, perception, and social cognition components over time among nonremitters and converters is apparent.

Investigating Cognitive Constructs

Cognitive components weighted by differential component loadings revealed more sensitivity with social and occupational functioning, particularly with the attention and GCF components. These findings indicate that incorporating cognitive architecture changes appears to be essential in uncovering subtle but important cognitive fluctuations that are relevant to functioning. Neither perception nor social cognition contributes to the variance in functional change beyond the traditional neuropsychological constructs. The trend related to the lack of clear group separation within the perception component could be attributed to the psychometrics of contributing tests. The Snakes in the Grass test, a visual search paradigm, may reflect the subtler changes in lower-level cognitive processes rather than the more robust separation in more traditional neuropsychological tasks. Nevertheless, the contribution of the perception component to test covariance supports the evidence that more refined cognitive mechanisms continue to be sensitive measures in identifying UHR for psychosis in general.69-72 Social cognition was the only construct that showed decrement over time in nonremitters beyond the baseline differences between healthy controls and remitters, although the component loading analysis suggested only trend-level dedifferentiation. It confirms that these findings replicate the notion that social cognition is separable from cognition even among individuals at UHR for psychosis.73-75 Longer follow-up periods might be necessary to determine whether an association between functioning and social cognition might emerge, similar to those in schizophrenia, as the downward trajectory in nonremitters ensues.76,77 Although speculative, mathematical models of cognitive architecture might be more sensitive than the standard neuropsychological tests to the changing neurobiology associated with emerging psychosis in young people at risk.

Cognition improved as a function of time, but the changes in remitters were dramatic. Remitters started at baseline with cognitive profiles that were similar to those of nonremitters, but their performance at follow-up was not different from that of healthy controls. The model that best exemplified this phenomenon included the measures comprising GCF. The correspondent longitudinal transition from dedifferentiation to differentiation of GCF that accounted for the functional recovery in remitters illuminates opportunities for follow-up work.

Overall, the results point to the possibility that UHR may not be a stable clinical or cognitive construct and that the deficits observed are transient. Results indicate that cognitive deficits in nonremitters tend to be stable and impaired in nearly all components. Longitudinal changes in cognitive architecture, particularly in remitters, have an association with the social and occupational functioning in young people. The converse could be true as the cognitive architecture continues to be increasingly dedifferentiated in nonremitters. These findings suggest that the prognosis for nonremitters is poor and will require the most clinical attention and remediation in the long term.

Limitations

This study has several limitations. First, the conversion rate is low. Of the 173 participants at UHR for psychosis followed-up during a 2-year period, 17 (9.8%) converted to psychosis, which is a lower rate than most other reported conversion rates. We speculate that the reason may be the strict drug laws in Singapore and the structured nature of its society. Low conversion rate precluded more sophisticated analysis on convertors. Second, medication use was not systematically adjusted for in the current analysis. The individuals at UHR for psychosis were not medicated with antipsychotics, although some were taking antidepressants. The association of antidepressants with cognition was found to be weak,78,79 but no differences in anxiety or depressive symptoms among UHR groups were observed. Subsequent studies of psychotropic medications and their various cognitive outcomes in at-risk mental states may be informative. Third, the subsampling between nonremitters and converters presented a challenge. Because of the limited sample sizes, we chose to use 2 analysis subsets, comparing healthy controls with remitters and nonremitters as well as healthy controls with nonconverters and converters. It would be ideal to classify samples as healthy controls, remitters, nonremitters, and converters, which is a necessary consideration for subsequent studies with larger sample sizes. Finally, following up participants at UHR for psychosis for only 24 months, although informative, limited the definition of remitters, nonremitters, and nonconverters. Cognitive dedifferentiation phenomena in nonremitters suggest the likelihood of long-lasting cognitive changes, but a longer prospective study would help clarify the degree to which these changes are detrimental to other aspects of clinical outcomes. Such a study would validate remission status (eg, if these cases slip back to being UHR for psychosis) and elucidate potential biological agent underpinnings responsible for the deficit.

Conclusions

To our knowledge, to date, this study had 1 of the largest single-site samples of individuals at UHR for psychosis. It replicates findings in the literature that cognition is impaired before the onset of psychosis. Baseline cognitive impairment differentiates nonremitters with more enduring symptomatology from healthy controls and individuals at UHR for psychosis whose UHR status later remits. Although predominantly a trait, cognitive architecture shows subtle changes over time in nonremitting individuals at UHR for psychosis. These cognitive architecture changes are associated with functional outcomes and may herald a conversion to psychosis and a cognitive architecture similar to schizophrenia.

Back to top
Article Information

Accepted for Publication: May 12, 2018.

Corresponding Author: Richard S. E. Keefe, PhD, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Box 3270, Durham, NC 27710 (richard.keefe@duke.edu).

Published Online: July 25, 2018. doi:10.1001/jamapsychiatry.2018.1668

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Lam M et al. JAMA Psychiatry.

Correction: This article was corrected on September 5, 2018, to fix an error in the first paragraph of the Results section.

Author Contributions: Drs Lee and Chong had full access to all of 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: J. Lee, Kraus, Subramaniam, Chong, Keefe.

Acquisition, analysis, or interpretation of data: Lam, J. Lee, Rapisarda, See, Yang, S. Lee, Abdul-Rashid, Kraus, Keefe.

Drafting of the manuscript: Lam, J. Lee, Keefe.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Lam, J. Lee, Yang.

Obtained funding: Subramaniam, Chong, Keefe.

Administrative, technical, or material support: J. Lee, Rapisarda, See, Yang, S. Lee, Kraus, Subramaniam, Chong, Keefe.

Study supervision: J. Lee, Subramaniam, Keefe.

Conflict of Interest Disclosures: Dr Keefe reported receiving honoraria or serving as a paid consultant, a speaker, or an advisory board member for Abbvie, Acadia, Aeglea, Akebia, Akili, Alkermes, ArmaGen, Astellas, Avanir, AviNeuro/ChemRar, Axovant, Biogen, Boehringer-Ingelehim, Cerecor, CoMentis, Critical Path Institute, FORUM, Global Medical Education, GW Pharmaceuticals, Intracellular Therapeutics, Janssen, Lundbeck, Lysogene, MedScape, Mentis Cura, Merck, Minerva Neurosciences Inc, Mitsubishi, Monteris, Moscow Research Institute of Psychiatry, Neuralstem, Neuronix, Novartis, New York State Office of Mental Health, Otsuka, Pfizer, Regenix Bio, Reviva, Roche, Sangamo, Sanofi, Sunovion, Takeda, Targacept, University of Moscow, University of Texas Southwest Medical Center, and WebMD. He also reported receiving royalties from versions of the Brief Assessment of Cognition testing battery, the MCCB Battery, and the Virtual Reality Functional Capacity Assessment Tool, as well as being a shareholder in NeuroCog Trials Inc and Sengenix. No other disclosures were reported.

Funding/Support: This study was funded by grant NMRC/TCR/003/2008 from the National Research Foundation Singapore of the National Medical Research Council Translational and Clinical Research Flagship Program, by grant NMRC/CG/004/2013 from the Singapore Ministry of Health’s National Medical Research Council of the Centre Grant Program, and by grant MH095: 003/008-1014 from the National Medical Research Council Research Training Fellowship (Dr Lam).

Role of the Funder/Sponsor: The National Research Foundation Singapore of the National Medical Research Council Translational and Clinical Research Flagship Program had a role in the design and conduct of the study and the collection, management, analysis, and interpretation of the data. The Singapore Ministry of Health’s National Medical Research Council of the Centre Grant Program had a role in the preparation, review, or approval of the manuscript. The funders had no role in the decision to submit the manuscript for publication.

Additional Contributions: We thank Shitij Kapur, MD, PhD, University of Melbourne; Merete Nordentoft, MD, PhD, University of Copenhagen; Ranga Krishnan, MD, Rush University; Diana Perkins, MD, PhD; Pat McGorry, MD, PhD; Simon Collinson, PhD, National University of Singapore; and Dale Purves, PhD, for their advisory roles in the design and conduct of the study. These individuals received no compensation for their contributions.

References
1.
Hawkins  KA, McGlashan  TH, Quinlan  D,  et al.  Factorial structure of the scale of prodromal symptoms.  Schizophr Res. 2004;68(2-3):339-347. doi:10.1016/S0920-9964(03)00053-7PubMedGoogle ScholarCrossref
2.
McGlashan  TH, Walsh  BC, Woods  SW.  The Psychosis-Risk Syndrome: Handbook for Diagnosis and Follow-Up. New York, NY: Oxford University Press; 2010.
3.
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. doi:10.1001/archgenpsychiatry.2010.66PubMedGoogle ScholarCrossref
4.
Addington  J, Cadenhead  KS, Cannon  TD,  et al; North American Prodrome Longitudinal Study.  North American Prodrome Longitudinal Study: a collaborative multisite approach to prodromal schizophrenia research.  Schizophr Bull. 2007;33(3):665-672. doi:10.1093/schbul/sbl075PubMedGoogle ScholarCrossref
5.
Cannon  TD, Cornblatt  B, McGorry  P.  The empirical status of the ultra high-risk (prodromal) research paradigm.  Schizophr Bull. 2007;33(3):661-664. doi:10.1093/schbul/sbm031PubMedGoogle ScholarCrossref
6.
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. doi:10.1001/archgenpsychiatry.2007.3PubMedGoogle ScholarCrossref
7.
Cannon  TD, van Erp  TG, Bearden  CE,  et al.  Early and late neurodevelopmental influences in the prodrome to schizophrenia: contributions of genes, environment, and their interactions.  Schizophr Bull. 2003;29(4):653-669. doi:10.1093/oxfordjournals.schbul.a007037PubMedGoogle ScholarCrossref
8.
McGlashan  TH.  A selective review of recent North American long-term followup studies of schizophrenia.  Schizophr Bull. 1988;14(4):515-542. doi:10.1093/schbul/14.4.515PubMedGoogle ScholarCrossref
9.
Franke  P, Maier  W, Hardt  J, Hain  C.  Cognitive functioning and anhedonia in subjects at risk for schizophrenia.  Schizophr Res. 1993;10(1):77-84. doi:10.1016/0920-9964(93)90079-XPubMedGoogle ScholarCrossref
10.
Sarfati  Y, Hardy-Baylé  M-C.  Could cognitive vulnerability identify high-risk subjects for schizophrenia?  Am J Med Genet. 2002;114(8):893-897. doi:10.1002/ajmg.b.10251PubMedGoogle ScholarCrossref
11.
Hawkins  KA, Addington  J, Keefe  RS,  et al.  Neuropsychological status of subjects at high risk for a first episode of psychosis.  Schizophr Res. 2004;67(2-3):115-122. doi:10.1016/j.schres.2003.08.007PubMedGoogle ScholarCrossref
12.
Yung  AR, Yuen  HP, McGorry  PD,  et al.  Mapping the onset of psychosis: the Comprehensive Assessment of At-Risk Mental States.  Aust N Z J Psychiatry. 2005;39(11-12):964-971. doi:10.1080/j.1440-1614.2005.01714.xPubMedGoogle ScholarCrossref
13.
Brewer  WJ, Wood  SJ, Phillips  LJ,  et al.  Generalized and specific cognitive performance in clinical high-risk cohorts: a review highlighting potential vulnerability markers for psychosis.  Schizophr Bull. 2006;32(3):538-555. doi:10.1093/schbul/sbj077PubMedGoogle ScholarCrossref
14.
Keefe  RSE, 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. doi:10.1016/j.schres.2006.06.041PubMedGoogle ScholarCrossref
15.
Lencz  T, Smith  CW, McLaughlin  D,  et al.  Generalized and specific neurocognitive deficits in prodromal schizophrenia.  Biol Psychiatry. 2006;59(9):863-871. doi:10.1016/j.biopsych.2005.09.005PubMedGoogle ScholarCrossref
16.
Niendam  TA, Bearden  CE, Johnson  JK,  et al.  Neurocognitive performance and functional disability in the psychosis prodrome.  Schizophr Res. 2006;84(1):100-111. doi:10.1016/j.schres.2006.02.005PubMedGoogle ScholarCrossref
17.
Seidman  LJ, Giuliano  AJ, Smith  CW,  et al.  Neuropsychological functioning in adolescents and young adults at genetic risk for schizophrenia and affective psychoses: results from the Harvard and Hillside Adolescent High Risk Studies.  Schizophr Bull. 2006;32(3):507-524. doi:10.1093/schbul/sbj078PubMedGoogle ScholarCrossref
18.
Eastvold  AD, Heaton  RK, Cadenhead  KS.  Neurocognitive deficits in the (putative) prodrome and first episode of psychosis.  Schizophr Res. 2007;93(1-3):266-277. doi:10.1016/j.schres.2007.03.013PubMedGoogle ScholarCrossref
19.
Pukrop  R, Ruhrmann  S, Schultze-Lutter  F, Bechdolf  A, Brockhaus-Dumke  A, Klosterkötter  J.  Neurocognitive indicators for a conversion to psychosis: comparison of patients in a potentially initial prodromal state who did or did not convert to a psychosis.  Schizophr Res. 2007;92(1-3):116-125. doi:10.1016/j.schres.2007.01.020PubMedGoogle ScholarCrossref
20.
Ozgürdal  S, Littmann  E, Hauser  M,  et al.  Neurocognitive performances in participants of at-risk mental state for schizophrenia and in first-episode patients.  J Clin Exp Neuropsychol. 2009;31(4):392-401. doi:10.1080/13803390802206406PubMedGoogle ScholarCrossref
21.
Walder  DJ, Mittal  V, Trotman  HD, McMillan  AL, Walker  EF.  Neurocognition and conversion to psychosis in adolescents at high-risk.  Schizophr Res. 2008;101(1-3):161-168. doi:10.1016/j.schres.2007.12.477PubMedGoogle ScholarCrossref
22.
Pukrop  R, Klosterkötter  J.  Neurocognitive indicators of clinical high-risk states for psychosis: a critical review of the evidence.  Neurotox Res. 2010;18(3-4):272-286. doi:10.1007/s12640-010-9191-1PubMedGoogle 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. doi:10.1093/schbul/sbp155PubMedGoogle ScholarCrossref
24.
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. doi:10.1001/archgenpsychiatry.2011.1592PubMedGoogle ScholarCrossref
25.
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. doi:10.2174/138161212799316019PubMedGoogle ScholarCrossref
26.
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. doi:10.1093/schbul/sbt085PubMedGoogle ScholarCrossref
27.
Seidman  LJ, Shapiro  DI, Stone  WS,  et al.  Association of neurocognition with transition to psychosis: baseline functioning in the second phase of the North American Prodrome Longitudinal Study.  JAMA Psychiatry. 2016;73(12):1239-1248. doi:10.1001/jamapsychiatry.2016.2479PubMedGoogle ScholarCrossref
28.
Heinrichs  RW.  Meta-analysis and the science of schizophrenia: variant evidence or evidence of variants?  Neurosci Biobehav Rev. 2004;28(4):379-394. doi:10.1016/j.neubiorev.2004.06.003PubMedGoogle ScholarCrossref
29.
Heinrichs  RW.  The primacy of cognition in schizophrenia.  Am Psychol. 2005;60(3):229-242. doi:10.1037/0003-066X.60.3.229PubMedGoogle ScholarCrossref
30.
Heinrichs  RW, Zakzanis  KK.  Neurocognitive deficit in schizophrenia: a quantitative review of the evidence.  Neuropsychology. 1998;12(3):426-445. doi:10.1037/0894-4105.12.3.426PubMedGoogle ScholarCrossref
31.
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. doi:10.1037/a0014708PubMedGoogle ScholarCrossref
32.
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. doi:10.1016/j.schres.2010.06.021PubMedGoogle ScholarCrossref
33.
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. doi:10.1176/appi.ajp.2009.09040574PubMedGoogle ScholarCrossref
34.
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. doi:10.1001/archpsyc.59.5.449PubMedGoogle ScholarCrossref
35.
Fuller  R, Nopoulos  P, Arndt  S, O’Leary  D, Ho  B-C, Andreasen  NC.  Longitudinal assessment of premorbid cognitive functioning in patients with schizophrenia through examination of standardized scholastic test performance.  Am J Psychiatry. 2002;159(7):1183-1189. doi:10.1176/appi.ajp.159.7.1183PubMedGoogle ScholarCrossref
36.
Keefe  RSE, Kahn  RS.  Cognitive decline and disrupted cognitive trajectory in schizophrenia.  JAMA Psychiatry. 2017;74(5):535-536. doi:10.1001/jamapsychiatry.2017.0312PubMedGoogle ScholarCrossref
37.
Niendam  TA, Bearden  CE, Zinberg  J, Johnson  JK, O’Brien  M, Cannon  TD.  The course of neurocognition and social functioning in individuals at ultra high risk for psychosis.  Schizophr Bull. 2007;33(3):772-781. doi:10.1093/schbul/sbm020PubMedGoogle ScholarCrossref
38.
Reichenberg  A, Weiser  M, Rapp  MA,  et al.  Elaboration on premorbid intellectual performance in schizophrenia: premorbid intellectual decline and risk for schizophrenia.  Arch Gen Psychiatry. 2005;62(12):1297-1304. doi:10.1001/archpsyc.62.12.1297PubMedGoogle ScholarCrossref
39.
Reichenberg  A, Harvey  PD.  Neuropsychological impairments in schizophrenia: integration of performance-based and brain imaging findings.  Psychol Bull. 2007;133(5):833-858. doi:10.1037/0033-2909.133.5.833PubMedGoogle ScholarCrossref
40.
Cannon  M, Moffitt  TE, Caspi  A, Murray  RM, Harrington  H, Poulton  R.  Neuropsychological performance at the age of 13 years and adult schizophreniform disorder: prospective birth cohort study.  Br J Psychiatry. 2006;189(5):463-464. doi:10.1192/bjp.bp.105.020552PubMedGoogle ScholarCrossref
41.
Niendam  TA, Bearden  CE, Rosso  IM,  et al.  A prospective study of childhood neurocognitive functioning in schizophrenic patients and their siblings.  Am J Psychiatry. 2003;160(11):2060-2062. doi:10.1176/appi.ajp.160.11.2060PubMedGoogle ScholarCrossref
42.
Groom  MJ, Jackson  GM, Calton  TG,  et al.  Cognitive deficits in early-onset schizophrenia spectrum patients and their non-psychotic siblings: a comparison with ADHD.  Schizophr Res. 2008;99(1-3):85-95. doi:10.1016/j.schres.2007.11.008PubMedGoogle ScholarCrossref
43.
Wilson  RS, Segawa  E, Hizel  LP, Boyle  PA, Bennett  DA.  Terminal dedifferentiation of cognitive abilities.  Neurology. 2012;78(15):1116-1122. doi:10.1212/WNL.0b013e31824f7ff2PubMedGoogle ScholarCrossref
44.
Hülür  G, Wilhelm  O, Robitzsch  A.  Intelligence differentiation in early childhood.  J Individ Differ. 2011;32(3):170-179. doi:10.1027/1614-0001/a000049Google ScholarCrossref
45.
Knowles  EEM, Weiser  M, David  AS,  et al.  Dedifferentiation and substitute strategy: deconstructing the processing-speed impairment in schizophrenia.  Schizophr Res. 2012;142(1-3):129-136. doi:10.1016/j.schres.2012.08.020PubMedGoogle ScholarCrossref
46.
Knowles  EEM, Weiser  M, David  AS, Glahn  DC, Davidson  M, Reichenberg  A.  The puzzle of processing speed, memory, and executive function impairments in schizophrenia: fitting the pieces together.  Biol Psychiatry. 2015;78(11):786-793. doi:10.1016/j.biopsych.2015.01.018PubMedGoogle ScholarCrossref
47.
Lam  M, Collinson  SL, Eng  GK,  et al.  Refining the latent structure of neuropsychological performance in schizophrenia.  Psychol Med. 2014;44(16):3557-3570. doi:10.1017/S0033291714001020PubMedGoogle ScholarCrossref
48.
Dickinson  D, Goldberg  TE, Gold  JM, Elvevåg  B, Weinberger  DR.  Cognitive factor structure and invariance in people with schizophrenia, their unaffected siblings, and controls.  Schizophr Bull. 2011;37(6):1157-1167. doi:10.1093/schbul/sbq018PubMedGoogle ScholarCrossref
49.
Lee  J, Rekhi  G, Mitter  N,  et al.  The Longitudinal Youth at Risk Study (LYRIKS)—an Asian UHR perspective.  Schizophr Res. 2013;151(1-3):279-283. doi:10.1016/j.schres.2013.09.025PubMedGoogle ScholarCrossref
50.
First  MB, Spitzer  RL, Gibbon  M, Williams  JBW.  Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. New York, NY: Biometrics Research, New York State Psychiatric Institute; 2002.
51.
Kay  SR, Fiszbein  A, Opler  LA.  The Positive and Negative Syndrome Scale (PANSS) for schizophrenia.  Schizophr Bull. 1987;13(2):261-276. doi:10.1093/schbul/13.2.261PubMedGoogle ScholarCrossref
52.
Beck  AT, Epstein  N, Brown  G, Steer  RA.  An inventory for measuring clinical anxiety: psychometric properties.  J Consult Clin Psychol. 1988;56(6):893-897. doi:10.1037/0022-006X.56.6.893PubMedGoogle ScholarCrossref
53.
Addington  D, Addington  J, Maticka-Tyndale  E.  Assessing depression in schizophrenia: the Calgary Depression Scale.  Br J Psychiatry Suppl. 1993;(22):39-44.PubMedGoogle Scholar
54.
Morosini  PL, Magliano  L, Brambilla  L, Ugolini  S, Pioli  R.  Development, reliability and acceptability of a new version of the DSM-IV Social and Occupational Functioning Assessment Scale (SOFAS) to assess routine social functioning.  Acta Psychiatr Scand. 2000;101(4):323-329. doi:10.1034/j.1600-0447.2000.101004323.xPubMedGoogle Scholar
55.
Chong  S-A, Campbell  A, Chee  M,  et al.  The Singapore flagship programme in translational and clinical research in psychosis.  Early Interv Psychiatry. 2011;5(4):290-300. doi:10.1111/j.1751-7893.2011.00304.xPubMedGoogle ScholarCrossref
56.
Wechsler  D.  Wechsler Memory Scale. San Antonio, TX: Psychological Corporation; 1997.
57.
Keefe  RSE, Harvey  PD, Goldberg  TE,  et al.  Norms and standardization of the Brief Assessment of Cognition in Schizophrenia (BACS).  Schizophr Res. 2008;102(1-3):108-115. doi:10.1016/j.schres.2008.03.024PubMedGoogle ScholarCrossref
58.
Emrich  HM.  A three-component-system hypothesis of psychosis: impairment of binocular depth inversion as an indicator of a functional dysequilibrium.  Br J Psychiatry Suppl. 1989;(5):37-39.PubMedGoogle Scholar
59.
Koethe  D, Kranaster  L, Hoyer  C,  et al.  Binocular depth inversion as a paradigm of reduced visual information processing in prodromal state, antipsychotic-naïve and treated schizophrenia.  Eur Arch Psychiatry Clin Neurosci. 2009;259(4):195-202. doi:10.1007/s00406-008-0851-6PubMedGoogle ScholarCrossref
60.
Cornblatt  BA, Risch  NJ, Faris  G, Friedman  D, Erlenmeyer-Kimling  L.  The Continuous Performance Test, Identical Pairs Version (CPT-IP): I. new findings about sustained attention in normal families.  Psychiatry Res. 1988;26(2):223-238. doi:10.1016/0165-1781(88)90076-5PubMedGoogle ScholarCrossref
61.
Gibson  CM, Penn  DL, Prinstein  MJ, Perkins  DO, Belger  A.  Social skill and social cognition in adolescents at genetic risk for psychosis.  Schizophr Res. 2010;122(1-3):179-184. doi:10.1016/j.schres.2010.04.018PubMedGoogle ScholarCrossref
62.
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. doi:10.1192/bjp.bp.106.031195PubMedGoogle ScholarCrossref
63.
Fox  E, Griggs  L, Mouchlianitis  E.  The detection of fear-relevant stimuli: are guns noticed as quickly as snakes?  Emotion. 2007;7(4):691-696. doi:10.1037/1528-3542.7.4.691PubMedGoogle ScholarCrossref
64.
Trampush  JW, Lencz  T, Knowles  E,  et al.  Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment.  Am J Med Genet B Neuropsychiatr Genet. 2015;168B(5):363-373. doi:10.1002/ajmg.b.32319PubMedGoogle ScholarCrossref
65.
Kaiser  HF.  The varimax criterion for analytic rotation in factor analysis.  Psychometrika. 1958;23(3):187-200.doi: doi:10.1007/BF02289233Google ScholarCrossref
66.
Reichenberg  A.  Cognitive impairment as a risk factor for psychosis.  Dialogues Clin Neurosci. 2005;7(1):31-38.PubMedGoogle Scholar
67.
Kendler  KS, Ohlsson  H, Mezuk  B, Sundquist  JO, Sundquist  K.  Observed cognitive performance and deviation from familial cognitive aptitude at age 16 years and ages 18 to 20 years and risk for schizophrenia and bipolar illness in a Swedish national sample.  JAMA Psychiatry. 2016;73(5):465-471. doi:10.1001/jamapsychiatry.2016.0053PubMedGoogle ScholarCrossref
68.
Hülür  G, Ram  N, Willis  SL, Schaie  KW, Gerstorf  D.  Cognitive dedifferentiation with increasing age and proximity of death: within-person evidence from the Seattle Longitudinal Study.  Psychol Aging. 2015;30(2):311-323. doi:10.1037/a0039260PubMedGoogle ScholarCrossref
69.
Kraus  M, Rapisarda  A, Lam  M,  et al.  Disrupted latent inhibition in individuals at ultra high-risk for developing psychosis.  Schizophr Res Cogn. 2016;6:1-8. doi:10.1016/j.scog.2016.07.003PubMedGoogle ScholarCrossref
70.
Öhman  A, Flykt  A, Esteves  F.  Emotion drives attention: detecting the snake in the grass.  J Exp Psychol Gen. 2001;130(3):466-478. doi:10.1037/0096-3445.130.3.466PubMedGoogle ScholarCrossref
71.
Kraus  MS, Keefe  RS, Krishnan  RK.  Memory-prediction errors and their consequences in schizophrenia.  Neuropsychol Rev. 2009;19(3):336-352. doi:10.1007/s11065-009-9106-1PubMedGoogle ScholarCrossref
72.
Carter  CS, Barch  DM, Buchanan  RW,  et al.  Identifying cognitive mechanisms targeted for treatment development in schizophrenia: an overview of the first meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia Initiative.  Biol Psychiatry. 2008;64(1):4-10. doi:10.1016/j.biopsych.2008.03.020PubMedGoogle ScholarCrossref
73.
Allen  DN, Strauss  GP, Donohue  B, van Kammen  DP.  Factor analytic support for social cognition as a separable cognitive domain in schizophrenia.  Schizophr Res. 2007;93(1-3):325-333. doi:10.1016/j.schres.2007.02.008PubMedGoogle ScholarCrossref
74.
Nuechterlein  KH, Barch  DM, Gold  JM, Goldberg  TE, Green  MF, Heaton  RK.  Identification of separable cognitive factors in schizophrenia.  Schizophr Res. 2004;72(1):29-39. doi:10.1016/j.schres.2004.09.007PubMedGoogle ScholarCrossref
75.
van Hooren  S, Versmissen  D, Janssen  I,  et al.  Social cognition and neurocognition as independent domains in psychosis.  Schizophr Res. 2008;103(1-3):257-265. doi:10.1016/j.schres.2008.02.022PubMedGoogle ScholarCrossref
76.
Green  MF, Horan  WP.  Social cognition in schizophrenia.  Curr Dir Psychol Sci. 2010;19(4):243-248. doi:10.1177/0963721410377600Google ScholarCrossref
77.
Green  MF, Bearden  CE, Cannon  TD,  et al.  Social cognition in schizophrenia, part 1: performance across phase of illness.  Schizophr Bull. 2012;38(4):854-864. doi:10.1093/schbul/sbq171PubMedGoogle ScholarCrossref
78.
Keefe  RSE, McClintock  SM, Roth  RM, Doraiswamy  PM, Tiger  S, Madhoo  M.  Cognitive effects of pharmacotherapy for major depressive disorder: a systematic review.  J Clin Psychiatry. 2014;75(8):864-876. doi:10.4088/JCP.13r08609PubMedGoogle ScholarCrossref
79.
Rosenblat  JD, Kakar  R, McIntyre  RS.  The cognitive effects of antidepressants in major depressive disorder: a systematic review and meta-analysis of randomized clinical trials.  Int J Neuropsychopharmacol. 2015;19(2):pyv082. doi:10.1093/ijnp/pyv082PubMedGoogle ScholarCrossref
×