Supplemental Materials for “What accounts for poor functioning in people with schizophrenia: a re-evaluation of the contributions of neurocognitive v. attitudinal and motivational factors” paper. For a look at the paper, click here
Evidence Supporting Tests of Neurocognitive Deficits
For the purposes of this review, we will focus on findings from the two test batteries that have been used most widely worldwide: Gur and colleagues’ Penn Computerized Neurocognitive Battery [CNB] (Gur et al., 2001) and the Measurement and Treatment Research to Improve Cognition in Schizophrenia [MATRICS] Consensus Cognitive Battery (MCCB) (Kern, Green, Nuechterlein, & Deng, 2004). The CNB and MCCB are based on reliable and valid tests used to study brain damage (Heinrichs, 2001). The CNB assesses the domains of executive functioning, attention, word memory, facial memory, spatial memory, language, spatial abilities, and sensory-motor abilities (Gur et al., 2001; Gur et al., 2010), while the MCCB measures processing speed, attention, and vigilance, working memory, verbal learning and memory, visual learning and memory, reasoning and problem solving, and social cognition (Schulz & Murray, 2016). These domains have traditionally been identified in schizophrenia, dating back to Kraepelin and Bleuler.
Psychometric Properties of the MCCB and CNB
Reliability. The CNB demonstrates adequate internal consistency (Cronbach’s alphas are usually between 0.70 and 0.90 across studies) and comparable results from separate samples (Swagerman et al., 2016). Similarly, the MCCB exhibits adequate internal consistency (Cronbach’s alpha between 0.70 and 0.90; Kaneda et al., 2013), moderate to strong intercorrelations between the individual domains (August, Kiwanuka, McMahon, & Gold, 2012), and high test-retest reliability (r values greater than 0.70; Green, Harris, & Nuechterlein, 2014).
Convergent validity. The CNB correlates with traditional measures of neurocognition (Gur et al., 2001). The MCCB correlates with the IntegNeuro Test, a highly reliable and validated computerized neurocognitive test (Silverstein et al., 2010).
Criterion validity. The CNB is sensitive to key demographic variables linked with neurocognitive differences, including age and gender (Gur et al., 2001; Gur et al., 2010; Swagerman et al., 2016). Further, the CNB differentiates between individuals with schizophrenia, unaffected relatives, and healthy controls (Gur et al., 2007). Additionally, the test correlates with premorbid adjustment, age of illness onset, illness duration, quality of life, and symptom severity (Grant & Beck, 2009). The MCCB also demonstrates criterion validity, as it correlates with responsiveness to treatments such as cognitive remediation, neuroplasticity-based auditory training, and antipsychotic medication (Green et al., 2014).
Predictive validity. Scores on the CNB correlate with patient and parental education levels (Gur et al., 2010; Swagerman et al., 2016), while scores on the MCCB are associated with education level and employment status (August et al., 2012; Lystad et al., 2014). MCCB performance correlates with community functioning cross-sectionally and longitudinally (Bryson & Bell, 2003; Shamsi et al., 2011). The MCCB performance correlates with self-rated social functioning (Lystad et al., 2014), clinical ratings of social functioning (Shamsi et al., 2011), and social problem solving abilities (Quinlan, Roesch, & Granholm, 2014). Finally, meta-analyses indicate that performance on domains measured by the batteries is associated with functional outcomes (Heinrichs, 2001; Green, Kern, & Heaton, 2004).
Additionally, scores on the CNB and MCCB can predict conversion to psychosis. Individuals with psychosis spectrum disorders showed greater neurocognitive developmental lag on the CNB than individuals with subthreshold psychotic symptoms, detectable as early as age 8 (Gur et al. 2014). Similarly, at-risk individuals who later transitioned to a psychotic disorder exhibited poorer performance on the MCCB than controls and at-risk individuals who did not transition (Seidman et al., 2016). In another recent study, individuals in an at-risk mental state for psychosis performed significantly worse on the MCCB compared to controls (Eisenacher et al., 2016).
August, S. M., Kiwanuka, J. N., McMahon, R. P., & Gold, J. M. (2012). The MATRICS
Consensus Cognitive Battery (MCCB): clinical and cognitive correlates. Schizophrenia Research, 134, 76-82. http://dx.doi.org/10.1016/j.schres.2011.10.015
Bryson, G., & Bell, M. D. (2003). Initial and final work performance in schizophrenia: cognitive
and symptom predictors. The Journal of Nervous and Mental Disease, 191, 87-92. Retrieved from http://journals.lww.com/jonmd/Pages/default.aspx
Eisenacher, S., Rausch, F., Ainser, F., Englisch, S., Becker, A., Mier, D., . . . Zink, M. (2016). Early cognitive basic symptoms are accompanied by neurocognitive impairment in patients with an ‘at-risk mental state’ for psychosis. Early Intervention Psychiatry. doi:10.1111/eip.12350
Grant, P. M., & Beck, A. T. (2009). Defeatist beliefs as a mediator of cognitive impairment, negative symptoms, and functioning in schizophrenia. Schizophrenia Bulletin, 35, 798-806. https://doi.org/10.1093/schbul/sbn008
Green, M. F., Harris, J. G., & Nuechterlein, K. H. (2014). The MATRICS consensus cognitive battery: what we know 6 years later. American Journal of Psychiatry, 171, 1151-1154. http://dx.doi.org/10.1176/appi.ajp.2014.14070936
Green, M. F., Kern, R. S., & Heaton, R. K. (2004). Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophrenia Research, 72, 41-51. http://dx.doi.org/10.1016/j.schres.2004.09.009
Gur, R. C., Calkins, M. E., Satterthwaite, T. D., Ruparel, K., Bilker, W. B., Moore, T. M., … & Gur, R. E. (2014). Neurocognitive growth charting in psychosis spectrum youths. JAMA Psychiatry, 71, 366-374. http://dx.doi.org/10.1001/jamapsychiatry.2013.4190
Gur, R. E., Nimgaonkar, V. L., Almasy, L., Calkins, M. E., Ragland, J. D., Pogue-Geile, M. F.,… & Gur, R. C. (2007). Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia. American Journal of Psychiatry, 164, 813-819. http://dx.doi.org/10.1176/ajp.2007.164.5.813
Gur, R. C., Ragland, J. D., Moberg, P. J., Turner, T. H., Bilker, W. B., Kohler, C., … & Gur, R. (2001). Computerized neurocognitive scanning: I. Methodology and validation in healthy people. Neuropsychopharmacology, 25, 766-776. http://dx.doi.org/10.1016/S0893-133X(01)00278-0
Gur, R. C., Richard, J., Hughett, P., Calkins, M. E., Macy, L., Bilker, W. B., … & Gur, R. E. (2010). A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation. Journal of Neuroscience Methods, 187(2), 254-262. http://dx.doi.org/10.1016/j.jneumeth.2009.11.017
Heinrichs, R. W. (2001). In search of madness: Schizophrenia and neuroscience. New York:Oxford University Press.
Kaneda, Y., Ohmori, T., Okahisa, Y., Sumiyoshi, T., Pu, S., Ueoka, Y., … & Sora, I. (2013). Measurement and treatment research to improve cognition in schizophrenia consensus cognitive battery: validation of the Japanese version. Psychiatry and Clinical Neurosciences, 67, 182-188. http://dx.doi.org/10.1111/pcn.12029
Kern, R. S., Green, M. F., Nuechterlein, K. H., & Deng, B. H. (2004). NIMH-MATRICS survey on assessment of neurocognition in schizophrenia. Schizophrenia Research, 72, 11-19. http://dx.doi.org/10.1016/j.schres.2004.09.004
Lystad, J. U., Falkum, E., Mohn, C., Haaland, V. Ø., Bull, H., Evensen, S., … & Ueland, T. (2014). The MATRICS Consensus Cognitive Battery (MCCB): performance and functional correlates. Psychiatry Research, 220(3), 1094-1101. http://dx.doi.org/10.1016/j.psychres.2014.08.060
Quinlan, T., Roesch, S., & Granholm, E. (2014). The role of dysfunctional attitudes in models of negative symptoms and functioning in schizophrenia. Schizophrenia Research, 157, 182-189. http://dx.doi.org/10.1016/j.schres.2014.05.025
Schulz, S. C., & Murray, A. (2016). Assessing Cognitive Impairment in Patients With Schizophrenia. The Journal of Clinical Psychiatry, 77(Suppl. 2), 3-7. http://dx.doi.org/10.4088/JCP.14074su1c.01
Seidman, L. J., Shapiro, D. I., Stone, W. S., Woodberry, K. A., Ronzio, A., Cornblatt, B. A., … & Mathalon, D. H. (2016). Association of neurocognition with transition to psychosis: baseline functioning in the second phase of the North American Prodrome Longitudinal Study. JAMA Psychiatry, 73, 1239-1248. http://dx.doi.org/10.1001/jamapsychiatry.2016.2479
Shamsi, S., Lau, A., Lencz, T., Burdick, K. E., DeRosse, P., Brenner, R., … & Malhotra, A. K. (2011). Cognitive and symptomatic predictors of functional disability in schizophrenia. Schizophrenia Research, 126, 257-264. http://dx.doi.org/10.1016/j.schres.2010.08.007
Silverstein, S. M., Jaeger, J., Donovan-Lepore, A. M., Wilkniss, S. M., Savitz, A., Malinovsky, I., … & Zukin, S. R. (2010). A comparative study of the MATRICS and IntegNeuro cognitive assessment batteries. Journal of Clinical and Experimental Neuropsychology, 32, 937-952. http://dx.doi.org/10.1080/13803391003596496
Swagerman, S. C., de Geus, E. J., Kan, K. J., van Bergen, E., Nieuwboer, H. A., Koenis, M. M., … & Boomsma, D. I. (2016). The Computerized Neurocognitive Battery: Validation, aging effects, and heritability across cognitive domains. Neuropsychology, 30, 53-64. http://dx.doi.org/10.1037/neu0000248