Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus Cognitive Battery

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Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus Cognitive Battery Ondrej Bezdicek a,⁎, Jiří Michalec b, Lucie Kališová b, Tomáš Kufa b, Filip Děchtěrenko c, Miriama Chlebovcová b, Filip Havlík a, Michael F. Green d,e, Keith H. Nuechterlein d,f a

Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine and General University Hospital in Prague, Charles University, Czech Republic Department of Psychiatry, First Faculty of Medicine and General University Hospital in Prague, Charles University, Prague, Czech Republic The Czech Academy of Sciences, Prague, Czech Republic d Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA e Department of Veterans Affairs VISN 22 Mental Illness Research, Education, and Clinical Center, Los Angeles, CA, USA f Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA b c

a r t i c l e

i n f o

Article history: Received 22 November 2019 Received in revised form 28 January 2020 Accepted 9 February 2020 Available online xxxx Keywords: Cognition MCCB Reliability Schizophrenia Validity

a b s t r a c t We aimed to validate the Czech version of the MATRICS Consensus Cognitive Battery (MCCB). The MCCB is a test battery designed to assess cognitive treatment effects in clinical trials of patients with schizophrenia. The valid, reliable and replicable measurement of cognition in schizophrenia is of substantial importance for such clinical trial studies. We performed a psychometric analysis of the MCCB composite and domain scores based on ROC analysis of 67 schizophrenia patients and 67 age- and education-matched healthy controls from a total sample of 220 controls. Also, we correlated MCCB variables with scales measuring psychosocial functioning (Personal and Social Performance scale; PSP). The internal consistency of all 10 tests in the MCCB battery was good (Cronbach's α = 0.85 (95% CI [0.83, 0.88])). The discriminative validity for the detection of neurocognitive dysfunction in schizophrenia based on the area under the curve of MCCB composite T-score was ≥90% (95% CI [0.85, 0.96]) and all MCCB domains showed ps b .001. The MCCB global composite and the Speed of Processing domain score significantly predicted the PSP ratings. A confirmatory factor analysis on the whole control sample (N = 220) showed an optimal fit for a 6-factor in comparison to 1-factor solution. In conclusion, we found high discriminative validity for the Czech MCCB version, similar to the differentiation of schizophrenia versus healthy control groups in the original MCCB studies. We also established the factorial validity of the MCCB and showed that the overall composite of the MCCB predicts psychosocial functioning in the patient group. © 2020 Elsevier B.V. All rights reserved.

1. Introduction Schizophrenia is one of the most important public health problems in the world (Andreasen, 2011; Murray, 1996; van Os and Kapur, 2009). Estimates of schizophrenia rates in the general population vary, according to the World Health Organization, from 0.3% to 1%, so N21 million people worldwide are affected as of 2019. One of the hallmarks of schizophrenia as a lifespan neuropsychiatric disease is that ca. 60–80% of patients, although not all, demonstrate significant cognitive impairments in a range of neurocognitive domains (Fatouros-Bergman et al., 2014; Goldberg et al., 2011; Heinrichs and Zakzanis, 1998; Mesholam-Gately et al., 2009; Savla et al., 2008; Sheffield et al., 2018).

⁎ Corresponding author at: Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine and General University Hospital in Prague, Charles University in Prague, Kateřinská 30, 128 21 Praha 2, Czech Republic. E-mail addresses: [email protected] (O. Bezdicek), [email protected] (M.F. Green), [email protected] (K.H. Nuechterlein).

There are several important reasons for seeking a better understanding of neurocognitive impairment in schizophrenia. First, neurocognitive deficits are a core feature of multiple psychotic disorders (e.g., schizophrenia, bipolar disorder) and provide a window into understanding their developmental course, the risk for psychosis, and treatment targets (Gold, 2004; Green and Harvey, 2014). Second, better cognitive performance is associated with a better psychosocial outcome (Fett et al., 2011; Green, 1996; Green et al., 2000). Furthermore, the identification of a neurocognitive profile of schizophrenia would provide a scientific basis for identifying endophenotypes in genetic studies or functional neuroimaging studies (Andreasen et al., 1992; Braff et al., 2007; Buchsbaum et al., 1992; Goldberg et al., 2011; Gur and Pearlson, 1993; Raz and Raz, 1990; Wykes et al., 2011). It appears that measures of processing speed are the most impaired in comparison to other cognitive domains, followed by measures of executive functioning, declarative memory, attention, and general intelligence level based on effect sizes across studies (Barch, 2005; Dickinson et al., 2007; Heinrichs and Zakzanis, 1998; Mesholam-

https://doi.org/10.1016/j.schres.2020.02.004 0920-9964/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004

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Table 1 Demographic and clinical characteristics of controls and schizophrenia patients. Variables

Schizophrenia M ± SD n = 67

Controls M ± SD n = 220

p-Value (schizophrenia vs. all controls)

Age (years) Education (years) Race (Caucasian, %) Sex (male, %) NART/CRT (IQ) MCCB (composite T score) PSP PANSS positive total PANSS negative total PANSS total score Olanzapine (equivalency ratio)

36.13 ± 9.52 13.54 ± 2.53 100 (100%) 50 (75%) 111.18 ± 10.84 40.04 ± 7.64 49.64 ± 12.01 12.91 ± 2.35 23.80 ± 6.92 69.71 ± 13.33 23.03 ± 7.62

27.50 ± 9.28 15.06 ± 3.30 100 (100%) 92 (42%) – 53.06 ± 3.95 – – – – –

b.001 b.001 ns. .001 – b.001 – – – – –

Note. MCCB = MATRICS Consensus Cognitive Battery; ns. = non-significant; NART/CRT = National Adult Reading Test/Czech Reading Test (premorbid intelligence level); PANSS = Positive and Negative Syndrome Scale; PSP = Personal and Social Performance scale.

Gately et al., 2009). Factor analytic studies have confirmed a very robust first (common) factor solution with loadings from nearly all cognitive measures, implying that there might be a common mechanism driving deficits in these domains (Barch and Ceaser, 2012; Dickinson et al., 2006; Dickinson et al., 2004; Keefe et al., 2006). On the other hand, a number of factor-analytic studies have suggested that there may be six to seven separable dimensions of cognitive deficit in schizophrenia (Gladsjo et al., 2004; Nuechterlein et al., 2004). To facilitate the development of pharmacological treatment and cognitive remediation for cognitive deficits in psychotic disorders, the NIMH Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Initiative were developed (Green and Nuechterlein, 2004; Marder and Fenton, 2004). One product of the MATRICS initiative was an assessment battery, the MATRICS Consensus Cognitive Battery (MCCB). It has become a widely used battery for assessing neurocognitive impairment in schizophrenia, has been validated in clinical populations, and has been translated into several languages (Bezdicek et al., 2015; Fonseca et al., 2017; Jędrasik-Styła et al., 2015; Keefe et al., 2011; Lystad et al., 2014; Mohn et al., 2012; Rodriguez-Jimenez et al., 2012; Shi et al., 2015). However, the reliability

and validity of the Czech MCCB are unknown and cannot be compared to other studies. In the current research, we present data from the Czech MATRICS Psychometric Study (Bezdicek et al., 2015). We administered the Czech MCCB to a clinical sample of adult patients with schizophrenia and a demographically matched healthy sample to confirm its efficacy for detecting the characteristic cognitive deficits and to examine the profile of cognitive deficit. A larger healthy sample was used to determine the factor structure of the Czech MCCB battery. We aimed to examine the factor structure of the ten MCCB cognitive variables in comparison to published studies of the English version (Burton et al., 2013; Harvey et al., 2013; Lo et al., 2016; McCleery et al., 2015). We hypothesized that the Processing Speed domain score would show the highest discriminative validity based on Receiver Operating Curve Analysis (ROC). Although a confirmatory factor analysis found support for the full seven-factor model underlying the MCCB structure when a larger MCCB beta battery was examined by McCleery et al. (2015), we expected that a single factor might be a better fit when only the ten final tests in the MCCB were examined.

Fig. 1. Profile graph for MCCB domain scores and average MCCB T-score (n = 67 controls and n = 67 schizophrenia patients). Note. MCCB domains: SP = Speed of Processing; AV = Attention/Vigilance; WM = Working Memory; Vbl Lng = Verbal Learning; Vis Lng = Visual Learning; RPS = Reasoning and Problem Solving; SC = Social Cognition; MCCB = MATRICS Consensus Cognitive Battery; T-score is a standard score with a mean of 50 and a standard deviation of 10. All differences between schizophrenia patients and controls were highly significant, p b .001.

Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004

O. Bezdicek et al. / Schizophrenia Research xxx (xxxx) xxx

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Table 2 Discriminative validity indices of the MCCB composite and single measures (Table A) and MCCB domain scores (Table B) based on ROC analysis (n = 67 controls and n = 67 schizophrenia patients) paired according to MatchIt. Table A (measures)

Sensitivity

Specificity

PPV

NPV

AUC

Lower CI

Upper CI

MCCB (composite T-score) Category Fluency (animals) Trail Making Test, Part A BACS Symbol Coding CPT-IP Letter Number Span WMS-III Spatial Span HVLT-R BVMT-R NAB Mazes MSCEIT-ME

0.925 0.806 0.642 0.925 0.851 0.881 0.746 0.881 0.761 0.866 0.701

0.746 0.701 0.896 0.761 0.672 0.687 0.776 0.776 0.657 0.582 0.761

0.785 0.730 0.860 0.795 0.722 0.738 0.769 0.797 0.689 0.674 0.746

0.909 0.783 0.714 0.911 0.818 0.852 0.754 0.867 0.733 0.812 0.718

0.902 0.821 0.810 0.896 0.808 0.850 0.807 0.885 0.773 0.792 0.733

0.848 0.750 0.736 0.841 0.735 0.784 0.733 0.827 0.694 0.715 0.649

0.956 0.893 0.883 0.952 0.882 0.916 0.881 0.943 0.853 0.868 0.818

Table B (domains)

Sensitivity

Specificity

PPV

NPV

AUC

Lower CI

Upper CI

SP AV WM Vbl Lng Vis Lng RPS SC

0.940 0.851 0.925 0.776 0.866 0.866 0.687

0.806 0.746 0.731 0.896 0.597 0.597 0.627

0.829 0.770 0.775 0.881 0.682 0.682 0.648

0.931 0.833 0.907 0.800 0.816 0.816 0.667

0.930 0.853 0.881 0.909 0.755 0.757 0.622

0.884 0.787 0.823 0.857 0.674 0.676 0.528

0.975 0.918 0.940 0.961 0.837 0.839 0.717

Note. AUC = Area Under Curve (ROC); BACS = Brief Assessment of Cognition in Schizophrenia; BVMT-R = Brief Visuospatial Memory Test-Revised; CI = 95% confidence interval; CPT-IP = Continuous Performance Test-Identical Pairs; HVLT-R = Hopkins Verbal Learning Test-Revised; MCCB = MATRICS Consensus Cognitive Battery; MSCEIT-ME = Mayer-Salovey-Caruso Emotional Intelligence Test: Managing Emotions; NAB = Neuropsychological Assessment Battery; NPV = negative predictive value; PPV = positive predictive value; WMS-III = Wechsler Memory Scale, Third Edition; MCCB domains: SP = Speed of Processing; AV = Attention/Vigilance; WM = Working Memory; Vbl Lng = Verbal Learning; Vis Lng = Visual Learning; RPS = Reasoning and Problem Solving; SC = Social Cognition. The single and composite measures with the highest AUC are in bold.

2. Methods 2.1. Participants We collected neuropsychological, clinical, and socio-demographic data from schizophrenia patients and healthy controls (Table 1). Study participants were recruited from schizophrenia patients who were previously hospitalized at the Psychiatric Department of the General University Hospital in Prague. At the time of testing, the patients were outpatients. Inclusion criteria were a diagnosis of schizophrenia according to ICD-10 and age between 18 and 65 years. Exclusion criteria were a history of low intellectual ability (IQ b 70), neurological diseases including head injury, and dependence on psychoactive substances. All participants signed written informed consent. All patients were without acute psychotic symptoms and were stabilized on antipsychotic medication (olanzapine equivalents; Gardner et al., 2010) and without intoxication and dependence on psychoactive substances, i.e., none of the included patients was diagnosed with substance-induced psychosis (Table 1). The study was approved by the ethical committee of the General University Hospital in Prague. Two patients were excluded from the study as they did not complete the whole MCCB (the drop-out rate was below 1%, i.e., 2 subjects from the whole sample). The socioeconomic status in 67 schizophrenia patients: 13 patients were at the time of the assessment fully employed; 13 patients were unemployed; 31 got a full disability pension; 10 patients were partially employed and got a partial disability pension. Regarding the duration of an illness 22 patients suffered from schizophrenia from 1 to 2 years; 22 patients from 2 to 10 years, and 25 patients for N10 years. Clinical symptoms of patients were rated with the Positive and Negative Syndrome Scale (PANSS; positive, negative and global score) and level of psychosocial functioning was measured with the Personal and Social Performance (PSP) scale. The PANSS includes 30 items assessing the severity of psychopathology scaled from 1 (absent symptom) to 7 (a symptom of extreme severity). The PANSS yields 3 subscales, 7 items for positive psychotic symptoms (scores 7–49), 7 items for negative symptoms (scores 7–49) and 16 items of general psychopathology scale (scores 16–112) (Kay et al., 1987). The PSP assesses four domains of psychosocial functioning (socially useful activities, personal and social relationships, self-care and disturbing and aggressive behaviour) on a scale

1–100, where higher scores indicate better psychosocial functioning (Morosini et al., 2000). Healthy controls were recruited from the general community through advertisements. A brief medical history for each subject was obtained via telephone. A detailed interview then excluded all participants with a history of head injury, stroke, abuse of alcohol or other psychoactive substances including medication (antidepressants, antipsychotics, stimulants, and anti-anxiety medications), and individuals with a history of a major neurological or psychiatric disease (e.g., epilepsy, multiple sclerosis, depression, anxiety, bipolar affective disorder, schizophrenia and other psychoses, or delirium), with a major somatic illness (myocardial infarction, diabetes mellitus, etc.), with uncompensated sensory deficits, and individuals currently undergoing radio- or chemotherapy. All controls were without abuse and dependence of psychoactive substances. The socioeconomic status in

Fig. 2. Diagnostic accuracy of the MCCB composite score T-score based on ROC in assessing neurocognitive dysfunction with optimal sensitivity/specificity point (n = 67 controls and n = 67 schizophrenia patients). Note. y-axis = sensitivity; x-axis = 1 − specificity; AUC = 0.902; p b .001.

Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004

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Table 3 Correlation analysis of primary MCCB measures in controls and schizophrenia patients based on Pearson correlation coefficient. Measure

CF

TMT-A

BACS-SC

CPT-IP

LNS

WMS-III-SS

HVLT-R

BVMT-R

NAB-M

MSCEIT-ME

CF TMT-A BACS-SC CPT-IP LNS WMS-III-SS HVLT-R BVMT-R NAB-M MSCEIT-ME

– −0.46 0.58 0.33 0.48 0.46 0.62 0.52 0.37 0.38

– −0.68 −0.51 −0.55 −0.57 −0.55 −0.49 −0.64 −0.49

– 0.56 0.67 0.61 0.69 0.65 0.61 0.53

– 0.52 0.40 0.45 0.43 0.44 0.54

– 0.54 0.64 0.53 0.46 0.44

– 0.52 0.48 0.55 0.47

– 0.57 0.41 0.42

– 0.48 0.44

– 0.46

–

Note. BACS-SC = Brief Assessment of Cognition in Schizophrenia-Symbol Coding; BVMT-R = Brief Visuospatial Memory Test-Revised; CF = Category fluency (animals); CPT-IP = Continuous Performance Test-Identical Pairs; HVLT-R = Hopkins Verbal Learning Test-Revised; LNS = Letter-Number Span; MCCB = MATRICS Consensus Cognitive Battery; MSCEIT-ME = Mayer-Salovey-Caruso Emotional Intelligence Test: Managing Emotions; NAB-M = Neuropsychological Assessment Battery-Mazes; TMT-A = Trail Making Test, Part A; WMS-III-SS = Wechsler Memory Scale, Third Edition-Spatial Span. All p-values b .001.

controls: all were partially or fully employed. Demographic and clinical cohort characteristics are presented in Table 1. All tests were administered under standard neuropsychological laboratory conditions and were completed by licensed clinical psychologists.

data developed for the USA population (Kern et al., 2008; Nuechterlein and Green, 2006; Nuechterlein et al., 2008).

3. Assessments

Data analysis was performed in R (Team, 2018). Visual inspection of all domain scores and all 10 tests showed that they were normally distributed with the exception of TMT-A, which was positively skewed. Only the TMT-A raw scores (time to completion) were logtransformed, not the scores from other MCCB tests. The MCCB domain scores (including TMT) and the overall composite score were computed as T-scores by supplied software using the North American English healthy sample as the base for calculating T-scores corrected for age and gender (Nuechterlein and Green, 2006). To examine the discriminant validity of the Czech MCCB version, we completed a ROC analysis with the total MCCB composite score. The areas under the curves (AUC) were compared using the test from DeLong et al. (1988). To assess the relationship between psychiatric symptoms and domain scores, we computed several multiple linear regressions. To explore the factor structure of the MCCB, we followed the approach of Burton et al. (2013) on controls only because sharedvariance procedures applied to “mixed” (clinical + normal) populations can mask some of the most vital cognitive constructs (Delis et al., 2003). Burton et al. (2013) omitted the social cognition measure and performed the factor analysis on the remaining 9 variables. We selected

3.1. Neuropsychological assessment Both schizophrenia patients and controls completed the Czech version of the MCCB (Nuechterlein and Green, 2006). Translation and back-translation and feasibility studies of the Czech MCCB version were done previously (Bezdicek et al., 2015; Kalisova et al., 2018). The MCCB consists of 10 tests that cover seven cognitive domains: Speed of processing (Brief Assessment of Cognition in Schizophrenia - Symbol Coding (BACS SC), Category Fluency (animals); and Trail Making TestPart A (TMT-A)); Attention/Vigilance (Continuous Performance Test Identical Pairs (CPT-IP)); Working memory verbal domain (LetterNumber Span (LNS)) and non-verbal domain (Wechsler Memory Scale-III Spatial Span WMS-III-SS); Verbal learning (Hopkins Verbal Learning Test-Revised (HVLT-R)); Visual learning (Brief Visuospatial Memory Test-Revised (BVMT-R)); Reasoning and problem solving (Neuropsychological Assessment Battery – Mazes (NAB-Mazes)); Social cognition (Mayer-Salovey-Caruso Emotional Intelligence Test – Managing Emotions (MSCEIT-ME)), have been standardized with normative

3.2. Statistical analyses

Table 4 Correlations between MCCB domain scores and psychiatric scales.

SP AV WM Vbl Lng Vis Lng RPS SC PANSS-p PANSS-n PANSS-g PANSS-t PSP PSP-A PSP-B PSP-C PSP-D

MCCB-US SP

AV

WM

Vbl Lng Vis Lng

0.91⁎⁎⁎ 0.67⁎⁎⁎ 0.82⁎⁎⁎ 0.58⁎⁎⁎ 0.66⁎⁎⁎ 0.69⁎⁎⁎ 0.89⁎⁎⁎

0.52⁎⁎⁎ 0.66⁎⁎⁎ 0.50⁎⁎⁎ 0.54⁎⁎⁎ 0.60⁎⁎⁎ 0.84⁎⁎⁎

0.46⁎⁎⁎ 0.32⁎⁎ 0.35⁎⁎ 0.51⁎⁎⁎ 0.53⁎⁎⁎

0.50⁎⁎⁎ 0.53⁎⁎⁎ 0.51⁎⁎⁎ 0.67⁎⁎⁎

0.29⁎ 0.14 0.49⁎⁎⁎

−0.16 −0.48⁎⁎⁎ −0.41⁎⁎⁎ −0.44⁎⁎⁎ 0.46⁎⁎⁎ −0.42⁎⁎⁎ −0.36⁎⁎ −0.35⁎⁎

−0.18 −0.51⁎⁎⁎ −0.46⁎⁎⁎ −0.49⁎⁎⁎ 0.52⁎⁎⁎ −0.49⁎⁎⁎ −0.33⁎⁎ −0.36⁎⁎

−0.03 −0.43⁎⁎⁎ −0.34⁎⁎ −0.36⁎⁎ 0.37⁎⁎ −0.31⁎⁎ −0.33⁎⁎ −0.36⁎⁎

−0.02 −0.23 −0.19 −0.20 0.26⁎ −0.29⁎ −0.22 −0.28⁎

−0.12

−0.06

−0.05

−0.09

−0.08 −0.19 −0.22 −0.21 0.19 −0.11 −0.18 −0.12 −0.17

0.34⁎⁎ 0.40⁎⁎⁎ −0.11 −0.30⁎

RPS

0.56⁎⁎⁎ −0.13 −0.21 −0.24 −0.22 −0.27⁎ −0.23 0.33⁎⁎ 0.21 −0.32⁎⁎ −0.15 −0.30⁎ −0.19 −0.23 −0.09 −0.09 −0.12

SC

−0.20 −0.47⁎⁎⁎ −0.38⁎⁎ −0.44⁎⁎⁎ 0.45⁎⁎⁎ −0.44⁎⁎⁎ −0.30⁎ −0.32⁎⁎ −0.11

PANSS-p

PANSS-n

PANSS-g

0.35⁎⁎ 0.54⁎⁎⁎ 0.63⁎⁎⁎ −0.62⁎⁎⁎ 0.54⁎⁎⁎ 0.44⁎⁎⁎ 0.42⁎⁎⁎ 0.44⁎⁎⁎

0.81⁎⁎⁎ 0.91⁎⁎⁎ −0.70⁎⁎⁎ 0.59⁎⁎⁎ 0.58⁎⁎⁎ 0.61⁎⁎⁎

0.96⁎⁎⁎ −0.72⁎⁎⁎ 0.58⁎⁎⁎ 0.56⁎⁎⁎ 0.71⁎⁎⁎

−0.04

0.20

PANSS-t

PSP

PSP-A

PSP-B

PSP-C

−0.79⁎⁎⁎ 0.66⁎⁎⁎ −0.83⁎⁎⁎ 0.62⁎⁎⁎ −0.75⁎⁎⁎ 0.59⁎⁎⁎ 0.71⁎⁎⁎ −0.72⁎⁎⁎ 0.50⁎⁎⁎ 0.63⁎⁎⁎ 0.24 0.23 0.19 0.17 −0.27⁎

Note. MCCB-US = MATRICS Consensus Cognitive Battery total score; MCCB domains: SP = Speed of Processing; AV = Attention/Vigilance; WM = Working Memory; Vbl Lng = Verbal Learning; Vis Lng = Visual Learning; RPS = Reasoning and Problem Solving; SC = Social Cognition; PANSS = Positive and Negative Syndrome Scale; PANSS-p = positive scale; PANSS-n = negative scale; PANSS-g = General Psychopathology Scale; PANSS-t = total scale; PSP = Personal and Social Performance scale; PSP-A = socially useful activities; PSP-B = personal and social relationships; PSP-C = self-care; PSP-D = disturbing and aggressive behaviour; the highest correlation in each instrument in bold. ⁎⁎⁎ α = b0.001. ⁎⁎ α = b0.01. ⁎ α = b0.05.

Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004

O. Bezdicek et al. / Schizophrenia Research xxx (xxxx) xxx Table 5 Multiple regression analysis with psychiatric scales as independent variables and MCCB score and domain scores as dependent variables. DV

IV

β

SE

t

p

MCCB-US

PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP PANSS-n PANSS-p PSP

−0.23 0.18 0.39 −0.26 0.20 0.47 −0.09 0.05 0.16 −0.29 0.28 0.35 −0.06 0.22 0.35 −0.11 0.14 0.34 −0.13 −0.01 0.11 −0.29 0.09 0.30

0.15 0.14 0.18 0.15 0.13 0.17 0.17 0.16 0.21 0.16 0.14 0.19 0.17 0.16 0.20 0.17 0.15 0.20 0.17 0.16 0.21 0.15 0.14 0.18

−1.75 1.26 2.13 −1.76 1.53 2.71 −0.54 0.30 0.75 −1.88 2.00 1.87 −0.37 1.40 1.71 −0.67 0.92 1.68 −0.76 −0.07 0.55 −1.88 0.63 1.65

.084 .212 .037 .083 .130 .009 .594 .767 .454 .065 .049 .067 .711 .167 .092 .507 .363 .098 .453 .945 .587 .065 .533 .105

SP

AV

WM

Vbl Lng

Vis Lng

RPS

SC

Note. DV = dependent variable; IV = independent variable; MCCB domains: SP = Speed of Processing; AV = Attention/Vigilance; WM = Working Memory; Vbl Lng = Verbal Learning; Vis Lng = Visual Learning; RPS = Reasoning and Problem Solving; SC = Social Cognition; PANSS = Positive and Negative Syndrome Scale; PANSS-p = Positive scale; PANSS-n = Negative scale; PSP = Personal and Social Performance scale; bold values denote significant predictors (α = b0.05).

three-factor model structures: 1-factor model, 3-factor model similar as proposed by Burton et al. (2013), and the 6-factor model, as proposed by Nuechterlein et al. (2008). In the next round of analysis, we also included the social cognition measure (MSCEIT-ME) and reran the analyses to determine if social cognition formed an individual factor (Eack et al., 2009). We used Maximum likelihood as the estimation method for fit. The fit of each model was assessed using confirmatory factor analysis (CFA) performed in the R package lavaan (Rosseel, 2012). We used commonly used measures, following recommendation from Hu and Bentler (1999), in which the fit is considered to be good when Standardized Root Mean Square Residual (SRMR) is lower than 0.08, Root Mean Square Error of Approximation (RMSEA) is lower than 0.06, Comparative index fit (CFI) is N0.95. For the acceptable fit, the RMSEA is lower than 0.08 and CFI higher than 0.9. We compared the fit of the models using the likelihood ratio test. Reliability estimates: The internal consistency of the MCCB consisting of ten variables was computed using Cronbach's alpha (α) and McDonald's omega total (ωT) as suggested by Board of Assessments of the European Federation of Psychologists' Associations (EFPA, 2013). The confidence intervals for Cronbach's α were computed using the formula from Duhachek and Lacobucci (2004). Confidence intervals for ωT were computed using bias-corrected and accelerated bootstrap as recommended by Kelley and Pornprasertmanit (2016).

5

4. Results Sociodemographic characteristics of the full sample of controls, as well as clinical characteristics of schizophrenia patients, can be found in Table 1. Prior to the discriminative validity and the ROC analyses, we selected a subsample of healthy controls to match the patient sample in age and education using MatchIt (Ho et al., 2011) with the nonparametric matching of samples to reduce the effect of possible confounds. This step reduced controls to 67 participants (same as schizophrenia patient group). The profile graph in Fig. 1 showed superior performance of paired controls over patients in each MCCB domain score (all ps b .001); a comparison of patients with all controls (n = 220) can be found in Supplemental Fig. 1. We also performed a ROC analysis on the MCCB overall composite T-score (Table 2A, Fig. 2) and the domain scores (Table 2B). Slightly different values in Table 2B (in comparison to 2A) for the domains that are based on only one test, e.g., Attention/Vigilance, Verbal and Visual Learning, Reasoning and Problem Solving and Social cognition can be explained by paired sample selection algorithm based on MatchIt that selects always the most efficient pairing to the schizophrenia patients which is slightly different for pairing based on MCCB single measures (Table 2A) and domains (Table 2B). ROC analysis showed that the average composite MCCB Tscore showed higher sensitivity than using the individual measures alone while maintaining high specificity as well. AUC for MCCB average composite T-score was the largest (AUC = 0.902, 95% CI [0.848, 0.956]). Delong's test showed that AUC for MCCB composite was significantly higher than AUC for all other variables with the exception of the Speed of Processing domain score (p = .883) and Verbal Learning (p = .262). A ROC curve including the optimal sensitivity and specificity point for the MCCB composite is shown in Fig. 2. As shown in Table 3, all individual test scores were correlated to each other (all ps b .001). The internal consistency based on Cronbach's α for the MCCB was 0.85 (95% CI [0.83, 0.88]) and 0.75 (95% CI [0.67, 0.79]) using ωT. This level of internal consistency provides a justification for use of the overall composite T-score as an index of the global cognitive performance level. We analyzed the relationship of MCCB performance with psychiatric symptoms and everyday functioning. The bivariate correlations can be found in Table 4. For further analysis, we used the PANSS Negative and Positive scales and PSP-A to reduce the effect of correlated regressors (Table 5). We tested the relationship of psychiatric symptoms and everyday functioning with the MATRICS average composite score and the domain scores using multiple linear regressions. In general, relationships between psychiatric symptom severity and domain scores were mostly non-significant. However, in the case of everyday functioning (PSP-A), the relationship was significant for the MCCB composite and the Speed of Processing domain score. For other domain scores, the relationship was around β = 0.3, showing a moderate relationship between variables. The results for CFA based on controls only are depicted in Table 6. For the CFA without the emotional intelligence scale (MSCEIT-ME) as in Burton et al. (2013), all three models showed similar fit. All models had RMSEA and CFI around the threshold for acceptable fit, while other measures showed a good fit. Similar results were obtained for

Table 6 Model fit results of confirmatory factor analysis based on controls (n = 220). MCCB version

Type

χ2

df

p-Value

AIC

BIC

SRMR

RMSEA

CFI

Without MSCEIT-ME Without MSCEIT-ME Without MSCEIT-ME With MSCEIT-ME With MSCEIT-ME With MSCEIT-ME

1-Factor 3-Factor 6-Factor 1-Factor 4-Factor 7-Factor

64.96 63.38 44.88 70.33 67.81 46.95

27 24 16 35 30 19

b.001 b.001 b.001 b.001 b.001 b.001

10,691 10,696 10,693 12,268 12,276 12,277

10,753 10,767 10,792 12,336 12,361 12,399

0.06 0.06 0.05 0.06 0.06 0.05

0.08 0.09 0.09 0.07 0.08 0.08

0.88 0.88 0.91 0.89 0.89 0.92

Note. AIC = Akaike Information Criterion; CFI = Comparative Fit Index; MCCB = MATRICS Consensus Cognitive Battery; MSCEIT-ME = Mayer-Salovey-Caruso Emotional Intelligence Test: Managing Emotions; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual.

Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004

6

O. Bezdicek et al. / Schizophrenia Research xxx (xxxx) xxx

Table 7 Individual factor loadings for 7-factor solution based on controls (n = 220). Measure

Latent factor

Beta

Trail Making Test, Part A BACS Symbol Coding Category Fluency (animals) CPT-IP WMS-III Spatial Span Letter-Number Span HVLT-R BVMT-R NAB Mazes MSCEIT-ME

Speed of Processing Speed of Processing Speed of Processing Attention/Vigilance Working Memory (nonverbal) Working Memory (verbal) Verbal Learning Visual Learning Reasoning and Problem Solving Social Cognition

0.53 −0.69 −0.43 1.00 0.55 0.55 1.00 1.00 1.00 1.00

Note. BACS = Brief Assessment of Cognition in Schizophrenia; BVMT-R = Brief Visuospatial Memory Test-Revised; CPT-IP = Continuous Performance Test-Identical Pairs; HVLT-R = Hopkins Verbal Learning Test-Revised; MCCB = MATRICS Consensus Cognitive Battery; MSCEIT-ME = Mayer-Salovey-Caruso Emotional Intelligence Test: Managing Emotions; NAB = Neuropsychological Assessment Battery; WMS-III = Wechsler Memory Scale, Third Edition.

solutions that contained social cognition measure as an additional factor (or new variable in the case of 1-factor solution). When testing of differences between the fits, the 6-factor model had slightly better fit than both 1-factor model (Δχ2 = 20.07, ΔDf = 11, p = .044) and 3-factor model (Δχ2 = 18.50, ΔDf = 8, p = .018). The difference between fits of 1-factor and 3-factor model was not significant (Δχ2 = 1.57, ΔDf = 3, p = .666). For the 7-factor solution, the fit was not significantly better than 1-factor solution (Δχ2 = 23.38, ΔDf = 16, p = .104), and it outperformed the 4-factor solution (Δχ2 = 20.86, ΔDf = 11, p = .035). The individual indices of fit showed mixed results. The 7-factor model had slightly better fit described by CFI and SRMR, but a 1-factor solution with social scale had better RMSEA index. A concordant replication of CFA 6-factor solution on mixed samples (controls and schizophrenia patients) can be found in Supplemental material (Tables 1–3). 5. Discussion In the present study, we performed systematic psychometric analyses and evaluated the potential of the MCCB Czech version for detection of cognitive deficit in schizophrenia. Thus, we examined the psychometric generalizability of the MCCB in its Czech translation and concurrently added to its applicability. The findings can be applied in several ways: (1) Clinically, the control sample can serve as a validity sample before complete normative data are collected and can be used as a source of preliminary normative values; (2) Scientifically, the additional evidence of the reliability and validity of the Czech MCCB justifies its use in double-blind studies with potential new cognitive enhancers and other cognitive interventions. The findings indicate that the Czech

Table 8 Factor loadings for 1-factor solution based on controls (n = 220). Measure

Mean

SD

Min

Max

Beta

Category Fluency (animals) BACS Symbol Coding BVMT-R CPT-IP HVLT-R Letter-Number Span NAB Mazes MSCEIT-ME Trail Making Test, Part A WMS-III Spatial Span

29.35 63.95 29.67 2.83 29.24 16.69 21.96 89.48 24.61 18.70

5.87 10.22 4.56 0.64 3.96 2.93 4.43 8.90 7.07 2.93

14.00 35.00 15.00 1.03 18.00 9.00 4.00 50.42 9.00 11.00

42.00 98.00 36.00 6.86 36.00 23.00 26.00 108.89 55.00 26.00

0.44 0.72 0.43 0.38 0.53 0.57 0.38 0.26 0.54 0.54

Note. BACS = Brief Assessment of Cognition in Schizophrenia; BVMT-R = Brief Visuospatial Memory Test-Revised; CPT-IP = Continuous Performance Test-Identical Pairs; HVLT-R = Hopkins Verbal Learning Test-Revised; MCCB = MATRICS Consensus Cognitive Battery; MSCEIT-ME = Mayer-Salovey-Caruso Emotional Intelligence Test: Managing Emotions; NAB = Neuropsychological Assessment Battery; WMS-III = Wechsler Memory Scale, Third Edition.

MCCB results are comparable to MCCB results from studies in the USA and Europe. More specifically, we compared two samples, schizophrenia patients and healthy controls (Bezdicek et al., 2015; Burton et al., 2013; Cronbach and Meehl, 1955; Delis et al., 2003; Floyd and Widaman, 1995; Gorsuch, 2015; Mohn et al., 2017). For determining the discriminative validity of each MCCB measure and of the composite score for differentiating patients from controls, we used a ROC analysis. Also, we evaluated the relationships of MCCB variables to psychiatric symptoms and everyday functioning (Keefe et al., 2011). Furthermore, we determined the factorial structure of MCCB and shed new light on the factorial validity of the MCCB in a non-English language (Table 7 and Table 8). Regarding the internal consistency of the MCCB battery measures, we found good (Cronbach's α) or acceptable (McDonald's ωT) levels. One should take into account that the battery consists of tests measuring several cognitive domains and was not designed to maximize internal consistency (Nuechterlein et al., 2008). Regarding validity, we found solid evidence for high detection potential of MCCB᾽s overall composite T-score (AUC ≥ 90%) and high discriminative validity of ten single measures based on the ROC analysis. BACS Symbol Coding (AUC = 90%; 95% CI [0.84, 0.95]) and HVLT-R immediate recall (AUC = 88.5%; 95% CI [0.83, 0.94]) showed the highest potential for differentiating schizophrenia patients from healthy controls, with superb classification accuracy characteristics. The ability of speed of processing measures to detect cognitive deficit level in schizophrenia has been emphasized previously (Dickinson et al., 2007). Importantly from a clinical point of view, selected MCCB measures correlate with psychosocial functioning and psychiatric symptoms measures, e.g., PSP correlates 0.52 with the Attention/Vigilance domain, and PANSS negative symptoms correlate inversely (−0.51) with Speed of Processing. MCCB-US overall composite T score predicted psychosocial in the regression analysis psychosocial functioning (p = .037), Speed of Processing predicted also psychosocial functioning (p = .009), and Working Memory predicted PANSS positive symptoms (p = .049). These results underscore the clinical relevance of MCCB performance for clinical practice (Heinrichs and Zakzanis, 1998; Mesholam-Gately et al., 2009) The results of CFA based on the whole control sample showed that the 6-factor model was statistically superior to other factor solutions (1-factor and 3-factor models) in Δχ2 analyses and thus had a somewhat better fit than other models. This finding is consistent with the 6-factor solution proposed by Burton et al. (2013) and McCleery et al. (2015). For completeness, we reran the CFA with mixed samples (controls and schizophrenia patients), and it provided further evidence for a 6-factor solution (Delis et al., 2003). However, this finding is not in line with other MCCB studies that examined only the final MCCB tests (Lo et al., 2016; Mohn et al., 2017). Use of additional tests in the beta battery of the MCCB (McCleery et al., 2015) may increase the opportunity to detect a larger number of factors, but the present study shows that it is possible to find a good fitting 6-factor model using only 9 of the final MCCB tests. The 6-factor solution covers six independent cognitive domains (without MCCB's emotional intelligence measure), thus supporting the putative latent structure of the MCCB (McCleery et al., 2015; Nuechterlein and Green, 2006; Nuechterlein et al., 2008). In comparison to previous factor-analytic studies, we did not exclude the MSCEIT social cognition measure of the MCCB (Burton et al., 2013). We see that, surprisingly, it did not affect the factor structure of the remainder of the MCCB battery and that it did not reduce the fit (Eack et al., 2009). The 6-factor solution (besides slightly superior fit) could also contribute to differential analysis of neurocognitive impairment patterns within schizophrenia endophenotypes (Allen et al., 2009). However, one should keep in mind that the MCCB subtests are moderate to highly correlated in schizophrenia patients and controls, a finding consistent

Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004

O. Bezdicek et al. / Schizophrenia Research xxx (xxxx) xxx

with previous research (August et al., 2012) and that the MCCB overall composite T-score has superior discriminative validity and classification accuracy and may be used as an index of generalized cognitive impairment in schizophrenia (Dickinson and Harvey, 2009; Dickinson et al., 2008). The limitations of the current study must be clearly stated. First, the sample size of our schizophrenia sample was relatively small, as was the number of participants in the control sample for CFA (Burton et al., 2013; Floyd and Widaman, 1995; Gorsuch, 2015; Lo et al., 2016). Second, we assessed only clinically stable outpatients on antipsychotic medication, which may significantly influence the level of neurocognitive impairment. The results may not generalize to first-episode or chronically psychotic patients (Keefe et al., 2011). Third, our CFA results are for healthy controls only and need to be replicated also with a large clinical sample (Delis et al., 2003). Fourth, we did not assess the test-retest reliability of the MCCB, which is important in measuring patients' neurocognitive change over time (Chelune et al., 1993). In conclusion, we bring new evidence regarding the internal consistency and validity of the Czech version of the MCCB battery. We show that the Czech MCCB predicts some psychosocial characteristics and psychiatric symptoms within schizophrenia outpatients. The MCCB overall composite score has superb classification accuracy for the detection of neurocognitive dysfunction due to schizophrenia. Also, we present a confirmatory factor analysis of the MCCB battery that basically replicates the findings and factorial structure of the original set of MCCB cognitive domains (McCleery et al., 2015). We found the optimal fit in a 6-factor solution. At the same time, we found that a parsimonious and psychometrically simplistic solution based on the MCCB average Tscore had optimal classification accuracy for differentiating schizophrenia patients from controls. Overall, we have demonstrated substantial validity for the Czech version of the MCCB. The battery is appropriate for use in clinical settings and pharmacological research of treatment effects in schizophrenia.

Role of funding source This study was supported by the Czech Science Foundation (“Cognitive Predictors of Neurodegeneration”, grant Nr. 16-01781S) and by RVO 68081740 and by Ministry of Health of the Czech Republic, grant Nr. AZV 17-32445A.

Contributors Drs. Bezdicek, Nuechterlein, and Green were responsible for the conceptualization of the paper. Drs. Kališová, Chlebovcová, Michalec, Havlík, and Kufa collected the data. Drs. Děchtěrenko and Havlík, Green, and Nuechterlein analyzed all data. All authors wrote and approved the final manuscript.

Declaration of competing interest Drs. Kališová, Chlebovcová, Bezdicek, Děchtěrenko, Michalec, Havlík, and Kufa have no financial or ethical conflict of interests to report. Drs. Nuechterlein, and Green are Officers in MATRICS Assessment, Inc., but they receive no financial compensation for his role in the company or from the sale of the MATRICS Consensus Cognitive Battery.

Acknowledgments We would like to thank Monika Vodová, Terezie Zuntychová, Kristýna Schinková, Markéta Smrčková, Gabriela Míková, and Tadeáš Janda for help in data collection and management.

Data availability statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.schres.2020.02.004.

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Please cite this article as: O. Bezdicek, J. Michalec, L. Kališová, et al., Profile of cognitive deficits in schizophrenia and factor structure of the Czech MATRICS Consensus C..., Schizophrenia Research, https://doi.org/10.1016/j.schres.2020.02.004