Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-27T09:06:26.412Z Has data issue: false hasContentIssue false

Factorial validity, measurement equivalence and cognitive performance of the Cambridge Neuropsychological Test Automated Battery (CANTAB) between patients with first-episode psychosis and healthy volunteers

Published online by Cambridge University Press:  29 December 2014

L. Haring*
Affiliation:
Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia
R. Mõttus
Affiliation:
Department of Psychology, University of Edinburgh, Edinburgh, UK Department of Psychology, University of Tartu, Tartu, Estonia
K. Koch
Affiliation:
Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia
M. Trei
Affiliation:
Department of Psychology, University of Tartu, Tartu, Estonia Psychiatry Clinic of North Estonia Medical Centre, Tallinn, Estonia
E. Maron
Affiliation:
Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia Centre for Mental Health, Imperial College London, London, UK
*
*Address for correspondence: L. Haring, Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia. (Email: Liina.Haring@kliinikum.ee)

Abstract

Background

The purpose of this study was to use selected Cambridge Neuropsychological Test Automated Battery (CANTAB) tests to examine the dimensional structure of cognitive dysfunction in first episode of psychosis (FEP) patients compared with cognition in healthy subjects.

Method

A total of 109 FEP patients and 96 healthy volunteers were administered eight CANTAB tests of cognitive function. Principal components analysis (PCA) was used to estimate dimensionality within the test results. The dimensions identified by the PCA were assumed to reflect underlying cognitive traits. The plausibility of latent factor models was estimated using confirmatory factor analysis (CFA). Multi-group CFA (MGCFA) was used to test for measurement invariance of factors between groups. The nature and severity of cognitive deficits amongst patients as opposed to controls were evaluated using a general linear model.

Results

Amongst subjects PCA identified two underlying cognitive traits: (i) a broad cognitive domain; (ii) attention/memory and executive function domains. Corresponding CFA models were built that fitted data well for both FEP patients and healthy volunteers. As in MGCFA latent variables appeared differently defined in patient and control groups, differences had to be ascribed using subtest scores rather than their aggregates. At subtest score level the patients performed significantly worse than healthy subjects in all comparisons (p < 0.001).

Conclusions

Results of this study demonstrate that the structure of underlying cognitive abilities as measured by a selection of CANTAB tests is not the same for healthy individuals and FEP patients, with patients displaying widespread cognitive impairment.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Addington, J, Brooks, BL, Addington, D (2003). Cognitive functioning in first episode psychosis: initial presentation. Schizophrenia Research 62, 5964.Google Scholar
Barnett, JH, Sahakian, BJ, Werners, U, Hill, KE, Brazil, R, Gallagher, O, Bullmore, ET, Jones, PB (2005). Visuospatial learning and executive function are independently impaired in first-episode psychosis. Psychological Medicine 35, 10311041.Google Scholar
Bentler, PM (1990). Comparative fit indexes in structural models. Psychological Bulletin 107, 238246.Google Scholar
Bilder, RM, Goldman, RS, Robinson, D, Reiter, G, Bell, L, Bates, JA, Pappadopulos, E, Willson, DF, Alvir, JMJ, Woerner, MG, Geisler, S, Kane, JM, Lieberman, JA (2000). Neuropsychology of first-episode schizophrenia: initial characterization and clinical correlates. American Journal of Psychiatry 157, 549559.Google Scholar
Browne, MW, Cudeck, R (1993). Testing structural equation models. In Alternative Ways of Assessing Model Fit (ed. Bollen, K.A. and Long, J.S.), pp. 136162. Sage: Beverly Hills, CA.Google Scholar
Burton, CZ, Vella, L, Harvey, PD, Patterson, TL, Heaton, RK, Twamley, EW (2013). Factor structure of the MATRICS Consensus Cognitive Battery (MCCB) in schizophrenia. Schizophrenia Research 146, 244248.Google Scholar
Byrne, BM, Shavelson, RJ, Muthén, B (1989). Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychological Bulletin 105, 456466.Google Scholar
Deary, IJ, Penke, L, Johnson, W (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience 11, 201211.Google Scholar
Dickinson, D, Goldberg, TE, Gold, JM, Elvevåg, B, Weinberger, DR (2011). Cognitive factor structure and invariance in people with schizophrenia, their unaffected siblings, and controls. Schizophrenia Bulletin 37, 11571167.Google Scholar
Dickinson, D, Iannone, VN, Wilk, CM, Gold, JM (2004). General and specific cognitive deficits in schizophrenia. Biological Psychiatry 55, 826833.Google Scholar
Dickinson, D, Ragland, JD, Calkins, ME, Gold, JM, Gur, RC (2006). A comparison of cognitive structure in schizophrenia patients and healthy controls using confirmatory factor analysis. Schizophrenia Research 85, 2029.Google Scholar
Dickinson, D, Ramsey, ME, Gold, JM (2007). Overlooking the obvious: a meta-analytic comparison of digit symbol coding tasks and other cognitive measures in schizophrenia. Archives of General Psychiatry 64, 532542.Google Scholar
Elliott, R, McKenna, PJ, Robbins, TW, Sahakian, BJ (1995). Neuropsychological evidence for frontostriatal dysfunction in schizophrenia. Psychological Medicine 25, 619630.Google Scholar
Genderson, MR, Dickinson, D, Diaz-Asper, C, Egan, MF, Weinberger, DR, Goldberg, TE (2007). Factor analysis of neurocognitive tests in a large sample of schizophrenic probands, their siblings, and healthy controls. Schizophrenia Research 94, 231239.Google Scholar
Gladsjo, JA, McAdams, LA, Palmer, BW, Moore, DJ, Jeste, DV, Heaton, RK (2004). A six-factor model of cognition in schizophrenia and related psychotic disorders: relationships with clinical symptoms and functional capacity. Schizophrenia Bulletin 30, 739754.Google Scholar
Gold, JM, Carpenter, C, Randolph, C, Goldberg, TE, Weinberger, DR (1997). Auditory working memory and Wisconsin Card Sorting Test performance in schizophrenia. Archives of General Psychiatry 54, 159165.Google Scholar
Gold, JM, Harvey, PD (1993). Cognitive deficits in schizophrenia. Psychiatric Clinics of North America 16, 295312.Google Scholar
Green, MF, Nuechterlein, KH, Gold, JM, Barch, DM, Cohen, J, Essock, S, Fenton, WS, Frese, F, Goldberg, TE, Heaton, RK, Keefe, RSE, Kern, RS, Kraemer, H, Stover, E, Weinberger, DR, Zalcman, S, Marder, SR (2004). Approaching a consensus cognitive battery for clinical trials in schizophrenia: the NIMH-MATRICS conference to select cognitive domains and test criteria. Biological Psychiatry 56, 301307.Google Scholar
Heaton, RK, Gladsjo, JA, Palmer, BW, Kuck, J, Marcotte, TD, Jeste, DV (2001). Stability and course of neuropsychological deficits in schizophrenia. Archives of General Psychiatry 58, 2432.Google Scholar
Heinrichs, RW, Zakzanis, KK (1998). Neurocognitive deficit in schizophrenia: a quantitative review of the evidence. Neuropsychology 12, 426445.Google Scholar
Horn, JL (1965). A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179185.Google Scholar
Horn, JL, McArdle, JJ (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research 18, 117144.Google Scholar
Hu, L, Bentler, PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 155.Google Scholar
Hutton, SB, Puri, BK, Duncan, LJ, Robbins, TW, Barnes, TRE, Joyce, EM (1998). Executive function in first-episode schizophrenia. Psychological Medicine 28, 463473.Google Scholar
Jaeger, J, Czobor, , Berns, SM (2003). Basic neuropsychological dimensions in schizophrenia. Schizophrenia Research 65, 105116.Google Scholar
Joreskog, KG (1971). Simultaneous factor analysis in several populations. Psychometrika 36, 409426.Google Scholar
Joyce, EM, Hutton, SB, Mutsatsa, SH, Barnes, TRE (2005). Cognitive heterogeneity in first-episode schizophrenia. British Journal of Psychiatry 187, 516522.Google Scholar
Kéri, S, Janka, Z (2004). Critical evaluation of cognitive dysfunctions as endophenotypes of schizophrenia. Acta Psychiatrica Scandinavica 110, 8391.Google Scholar
Leeson, VC, Robbins, TW, Franklin, C, Harrison, M, Harrison, I, Ron, MA, Barnes, TRE, Joyce, EM (2009 a). Dissociation of long-term verbal memory and fronto-executive impairment in first-episode psychosis. Psychological Medicine 39, 17991808.CrossRefGoogle ScholarPubMed
Leeson, VC, Robbins, TW, Matheson, E, Hutton, SB, Ron, MA, Barnes, TRE, Joyce, EM (2009 b). Discrimination learning, reversal, and set-shifting in first-episode schizophrenia: stability over six years and specific associations with medication type and disorganization syndrome. Biological Psychiatry 66, 586593.Google Scholar
Meredith, W (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika 58, 525543.Google Scholar
Mohamed, S, Paulsen, JS, O'Leary, D, Arndt, S, Andreasen, N (1999). Generalized cognitive deficits in schizophrenia: a study of first-episode patients. Archives of General Psychiatry 56, 749754.Google Scholar
Murray, GK, Cheng, F, Clark, L, Barnett, JH, Blackwell, AD, Fletcher, PC, Robbins, TW, Bullmore, ET, Jones, PB (2008). Reinforcement and reversal learning in first-episode psychosis. Schizophrenia Bulletin 34, 848855.Google Scholar
Pantelis, C, Barnes, TR, Nelson, HE, Tanner, S, Weatherley, L, Owen, AM, Robbins, TW (1997). Frontal–striatal cognitive deficits in patients with chronic schizophrenia. Brain: A Journal of Neurology 120, 18231843.Google Scholar
Pantelis, C, Wood, SJ, Proffitt, TM, Testa, R, Mahony, K, Brewer, WJ, Buchanan, J, Velakoulis, D, McGorry, PD (2009). Attentional set-shifting ability in first-episode and established schizophrenia: relationship to working memory. Schizophrenia Research 112, 104113.Google Scholar
Pashler, H, Wagenmakers, E (2012). Editors’ introduction to the special section on replicability in psychological science: a crisis of confidence? Perspectives on Psychological Science 7, 528530.Google Scholar
R Development Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria (http://www.R-project.org/).Google Scholar
Reichenberg, A, Weiser, M, Rabinowitz, J, Caspi, A, Schmeidler, J, Mark, M, Kaplan, Z, Davidson, M (2002). A population-based cohort study of premorbid intellectual, language, and behavioral functioning in patients with schizophrenia, schizoaffective disorder, and nonpsychotic bipolar disorder. American Journal of Psychiatry 159, 20272035.Google Scholar
Robbins, TW, James, M, Owen, AM, Sahakian, BJ, Lawrence, AD, McInnes, L, Rabbitt, PM (1998). A study of performance on tests from the CANTAB battery sensitive to frontal lobe dysfunction in a large sample of normal volunteers: implications for theories of executive functioning and cognitive aging. Cambridge Neuropsychological Test Automated Battery. Journal of the International Neuropsychological Society: JINS 4, 474490.Google Scholar
Robbins, TW, James, M, Owen, AM, Sahakian, BJ, McInnes, L, Rabbitt, P (1994). Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia (Basel, Switzerland) 5, 266281.Google Scholar
Robbins, TW, Sahakian, BJ (1994). Computer methods of assessment of cognitive function. In Principles and Practice of Geriatric Psychiatry (ed. Copeland, J. R. M., Abou-Saleh, M. T. and Blazer, D. G.), pp. 205209. John Wiley & Sons, Ltd: Chichester, UK.Google Scholar
Rosseel, Y (2012). Iavaan: an R package for structural equation modeling. Journal of Statistical Software 48, 136.Google Scholar
Saykin, AJ, Gur, RC, Gur, RE, Mozley, PD, Mozley, LH, Resnick, SM, Kester, DB, Stafiniak, P (1991). Neuropsychological function in schizophrenia. Selective impairment in memory and learning. Archives of General Psychiatry 48, 618624.Google Scholar
Steiger, JH (2000). Point estimation, hypothesis testing, and interval estimation using the RMSEA: some comments and a reply to Hayduck and Glaser. Structural Equation Modeling 7, 149162.Google Scholar
Stip, E, Lecardeur, L, Sepehry, AA (2008). Computerised assessment of visuo-spatial cognition in schizophrenia – an exploratory meta-analysis of CANTAB findings. European Psychiatric Review 1, 4854.Google Scholar
Townsend, LA, Norman, RMG (2004). Course of cognitive functioning in first episode schizophrenia spectrum disorders. Expert Review of Neurotherapeutics 4, 6168.Google Scholar
Vandenberg, RJ, Lance, CE (2000). A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organizational Research Methods 3, 469.Google Scholar
Wicherts, JM, Dolan, CV (2010). Measurement invariance in confirmatory factor analysis: an illustration using IQ test performance of minorities. Educational Measurement: Issues and Practice 29, 3947.Google Scholar
Widaman, KF, Reise, SP (1997). Exploring the measurement invariance of psychological instruments: applications in the substance use domain. In The Science of Prevention: Methodological Advances from Alcohol and Substance Abuse Research (ed. Bryant, K. J., Windle, M. and West, S. G.), pp. 281324. American Psychological Association: Washington, DC.Google Scholar
World Health Organization (1992). The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. World Health Organization: Geneva.Google Scholar
Supplementary material: File

Haring Supplementary Material

Supplementary Material

Download Haring Supplementary Material(File)
File 34.8 KB