Psychiatry Research 220 (2014) 664–668

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Investigation of the Montreal Cognitive Assessment (MoCA) as a cognitive screener in severe mental illness Mandi W. Musso n, Alex S. Cohen, Tracey L. Auster, Jessica E. McGovern Louisiana State University, Department of Psychology, Baton Rouge, LA, USA

art ic l e i nf o

a b s t r a c t

Article history: Received 12 June 2013 Received in revised form 28 July 2014 Accepted 31 July 2014 Available online 8 August 2014

This study examined the Montreal Cognitive Assessment (MoCA) as a neurocognitive screener and its relationship with functional outcomes in a sample of outpatients diagnosed with severe mental illness (SMI). The MoCA, Brief Assessment of Cognition in Schizophrenia (BACS), UCSD Performance-Based Skills Assessment Test-2 (UPSA-2), and Global Assessment of Functioning (GAF) were administered to 28 SMI patients and 18 non-psychiatric controls. Patients obtained significantly lower scores on the MoCA, BACS, UPSA-2, and GAF compared to non-patients. The cutoff score o 26 of the MoCA resulted in favorable sensitivity (89%) but lower specificity (61%) in classification of SMI patients. The MoCA was significantly correlated with UPSA scores but not GAF scores, whereas the BACS was not significantly correlated with UPSA or GAF scores. When entered into hierarchical regression analyses, the MoCA accounted for significant variance in UPSA scores above variance accounted for by the BACS. Both the MoCA and the BACS contributed unique variance in GAF scores. Overall, the MoCA demonstrated high sensitivity as a cognitive screener in SMI. Moreover, MoCA scores were related to performance-based measures of functional capacity. & 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords: Schizophrenia Depression Bipolar disorder Cognitive deficits Functional outcomes Neuropsychology

1. Introduction There is a large body of the literature documenting cognitive deficits in patients across the severe mental illness (SMI) spectrum, including schizophrenia (Heinrichs and Zakzanis, 1998) and unipolar (McDermott and Ebmeier, 2009) and bipolar mood disorders (Kurtz and Gerraty, 2009). In schizophrenia, for example, evidence suggests that cognitive deficits reflect a core feature of illness (Goldberg and Green, 2002; Nuechterlein et al., 1994), with moderate to large effect sizes across neurocognitive domains (ds40.60, Heinrichs and Zakzanis, 1998). Performance on standard neuropsychological tests is reliably one of the most robust predictors of functional abilities in patients with schizophrenia (Green, 1996). Accordingly, improvement of cognitive functioning has become a target for clinical trials research (Green and Nuechterlein, 1999; Green et al., 2004). The National Institute of Mental Heath established the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative in 2002 to establish standards, including neuropsychological battery recommendations, for evaluating outcomes in the treatment of schizophrenia (Green

n Correspondence to : LSU Health-Baton Rouge, Department of Emergency Medicine, 5246 Brittany Drive, Baton Rouge, LA 70803, USA. Tel.: þ1 225 757 4148. E-mail address: [email protected] (M.W. Musso).

http://dx.doi.org/10.1016/j.psychres.2014.07.078 0165-1781/& 2014 Elsevier Ireland Ltd. All rights reserved.

et al., 2004). Other neuropsychological batteries, for example, the Brief Assessment of Cognition in Schizophrenia (BACS; Keefe et al., 2004), have also been developed for this purpose. In the last decade or so, a relatively large literature supporting the use of these cognitive batteries has emerged. While much of the research on neurocognition and severe mental illness has focused on schizophrenia, cognitive impairment has been noted in other Axis I disorders. For example, a metaanalysis of 14 studies reported small but significant effect sizes for deficits in episodic memory, executive function, and processing speed in major depression (McDermott and Ebmeier, 2009). In addition, a recent meta-analysis found moderate to large effect sizes for associations between bipolar disorder and deficits in attention, working memory, verbal memory, nonverbal memory, language, psychomotor speed, and executive functioning (Kurtz and Gerraty, 2009). In this meta-analysis, the largest effect sizes were for verbal learning (d ¼0.81) and verbal and nonverbal memory (d ¼0.80–0.92). Two meta-analyses suggest cognitive deficits persisted even during euthymic episodes (Robinson et al., 2006; Kurtz and Gerraty, 2009). Similar to what is seen in schizophrenia, neurocognitive impairment has also been associated with poorer psychosocial functioning in bipolar disorder (Wingo et al., 2009). Extensive neuropsychological testing provided by current batteries has many benefits; however, elaborate testing of all psychiatric

M.W. Musso et al. / Psychiatry Research 220 (2014) 664–668

patients is not practical in some settings, such as medical settings, where sessions are time-limited, resource-limited, and neuropsychologists or trained psychometricians may be unavailable. The MATRICS battery requires approximately 60–90 min for administration. Even shorter batteries such as the BACS require over 30 min for administration. Patients with severe mental illness may have difficulty withstanding the duration and frustration of such batteries. There is a need for brief screening measures that can be quickly and easily administered and that provide important information about cognitive and functional status to inform health care providers about the need for further neuropsychological assessment. There is a paucity of research regarding the use of brief screening measures in outpatients with severe mental illness. Importantly, some studies have reported that brief cognitive screening measures may be useful in predicting functional outcomes in patients with major depression (Withall et al., 2009). The Mini-Mental State Examination (MMSE), widely considered the most oft-used measure of mental status, has been associated with poorer ability to perform Instrumental Activities of Daily Living as measured by self-report on the Personal Self-Maintenance Scale (Lawton and Brody, 1969) in patients with severe depression (McCall and Dunn, 2003). However, a number of studies that examined the utility of the MMSE in community-dwelling patients with schizophrenia noted poor sensitivity to subtle cognitive deficits, as few patients scored below the impaired range (Ganjuli et al., 1998; Moore et al., 2004; Manning et al., 2007). The Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) is a more recently developed, 30-item, screener designed for use by health professionals that shows promise in SMI populations. Compared with the MMSE, it places greater emphasis on the domains of attention and executive functioning. Two recent studies from the University of Sarajevo examined the utility of the MoCA in schizophrenia and found the MoCA appears to be more sensitive to mild cognitive impairment in schizophrenia compared to the MMSE (Fisekovic et al., 2012b, 2012a). Despite this promise, the MoCA has largely gone unexamined in studies of cognition in SMI. This was an experimental study designed to examine the utility and psychometric properties of the MoCA in a group of outpatients with SMI. This study also compared the MoCA to the BACS, a validated but lengthier and more extensive neuropsychological battery commonly employed in SMI research. We tested the following hypotheses: (1) SMI patients would obtain significantly lower scores on the MoCA compared to healthy controls, and the MoCA would offer acceptable sensitivity to subtle cognitive deficits in the SMI group. (2) Poorer MoCA performance would be associated with functional deficits measured by both a performancebased measure of functional abilities (the UPSA-2) and clinician ratings on the Global Assessment of Functioning (GAF). (3) In exploratory analyses, the MoCA would be significantly associated with functional deficits to a similar degree in comparison with the BACS.

2. Methods 2.1. Participants 2.1.1. Patients Twenty-eight participants with severe mental illness were recruited from outpatient treatment programs located in Louisiana (see Table 1 for demographic information). At the time of testing, all participants were under the supervision of a multi-disciplinary team within a community mental health clinic. All participants met criteria for past or present psychotic or mood disorders. All patients were prescribed psychotropic medications at the time of testing, and there was substantial variability in type and dosage of medication based on patients' needs. Diagnoses were made using information obtained from a structured clinical interview (SCID-CV; First et al., 1996) and medical records. Interviews were

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conducted by a team of doctoral students under the supervision of a licensed clinical psychologist (Dr. Alex Cohen). Diagnostic decisions were made based on unanimous agreement at a case conference meeting led by Dr. Cohen. To be eligible for this study, participants had to be designated severely mentally ill by the state (i.e., psychiatric disorder with profound impairment). To be included in the current study, participants also had to have completed the MoCA during neuropsychological testing. Exclusion criteria included the following: (a) significant visual, auditory, or other sensory deficit that may have affected task performance; (b) current or history of DSM-IV-TR or otherwise significant substance dependence as indicated by physical symptoms; (c) neurological insult or head trauma that required overnight hospitalization; (d) inhalant use or ingestion of volatile vapors (e.g., aerosols) with significant lifetime frequency (i.e., greater than six times); and (e) GAF (American Psychiatric Association, 2000) ratings below 30, which characterized a disturbance caused by symptoms that would potentially have interfered with study performance. Participants were compensated $40 for completion of the study. Informed consent was obtained for all participants. All procedures complied with the Declaration of Helsinki and were approved by the university's Institutional Review Board. 2.1.2. Non-patients The non-patient group consisted of 18 non-psychiatric control participants (38.9% males, 61.1% females) who were recruited from the southern Louisiana community. Inclusion and exclusion criteria for this sample were the same as with the severe mental illness sample with the exception they be free of current and past psychotic disorders and mood disorders, based on structured clinical interviews (SCID-I/NP First et al., 2002). 2.2. Measures 2.2.1. The Brief Assessment of Cognition in Schizophrenia (BACS) Cognition was evaluated using the BACS (Keefe et al., 2004), a comprehensive battery assessing verbal fluency, verbal memory, problem solving, attention, and working memory. A composite score, computed as a z-score of each of the subscale scores, was employed in this study. 2.2.2. Montreal Cognitive Assessment (MoCA) The MoCA (Nasreddine et al., 2005) is a brief 30-item screening tool that examines cognitive domains including executive functioning, confrontation naming, attention, sentence repetition, verbal fluency, delayed verbal recall, and orientation. Individuals also complete two verbal learning trials; however, verbal learning is not scored. One additional point is allotted for individuals who have 12 or fewer years of education. Scores o 26 are suggestive of cognitive impairment. 2.2.3. UCSD Performance-Based Skills Assessment Test (UPSA-2) The UPSA-2 is the brief version of the UCSD Performance-Based Skills Assessment Test and is a performance measure of a person's everyday living skills in the following five selected domains of daily living: (1) communication, (2) organization/planning, (3) financial skills, (4) household management, and (5) transportation. The UPSA-2 takes approximately 30 min to administer and has shown high test–retest reliability and participant tolerance above and beyond other co-primary measures (Harvey et al., 2010). Each subsection requires role play tasks that evaluate participants' performance in carrying out activities of daily living such as being able to shop for food and correctly use a telephone and transportation. A composite score, computed as a z-score of the subscale scores, was used in this study. 2.3. Statistical analyses Analyses were conducted in four steps. First, demographic and descriptive information was examined using independent samples t-tests. A series of analyses of covariance (ANCOVAs) were used to determine whether groups differed on cognitive or functional measures. For these ANCOVAs, group (patients vs. nonpatients) was the independent variable, and the MoCA, BACS, and UPSA-2 were dependent variables. Gender and education were used as covariates. Second, we examined the psychometric properties of the MoCA in the SMI sample. For these analyses, sensitivity and specificity were calculated for the MoCA cutoff score of o26 for the patients and non-patients, respectively. We went one step further in investigating performance of the MoCA in patients with functional deficits by examining MoCA scores in individuals who obtained UPSA-2 scores that were 1 S.D. below the mean to determine whether the MoCA would have identified these patients as impaired. Third, we examined the relationships between the cognitive tests (MoCA and BACS) and functional scores (e.g., GAF and UPSA-2) using Spearman's correlations for the SMI sample. Finally, we compared the MoCA and BACS in their prediction of variance in UPSA-2 and GAF scores using hierarchical regressions. For a first set of hierarchical regressions, the BACS z-score was entered into Step 1 then the MoCA z-score was entered into Step 2. For the second set of hierarchical regressions, entry of the MoCA and BACS scores was reversed.

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Table 1 Descriptive statistics for non-patients and patients.

Non-patients (n¼ 18) Patients (n¼ 28) Schizophrenia (n¼ 18) Mood/affective (n¼10)

Age Mean (S.D.)

Education Mean (S.D.)

Gender %Male

Ethnicity %Caucasian

MoCA Mean z (S.D.)

BACS Mean z (S.D.)

UPSA-2 Mean z (S.D.)

GAF Mean z (S.D.)

41.67 39.68 38.17 42.40

14.00 11.13 10.89 11.60

39 75 89 50

55 46 60 39

26.72 20.57 20.17 21.30

0.82  0.55  0.61  0.44

0.67  0.43  0.55  0.21

82.00 44.12 42.31 47.00

(12.32) (11.63) (12.34) (10.27)

(2.50) (1.98) (2.03) (1.90)

3. Results 3.1. Demographics and descriptive statistics Within the SMI group, 10 participants were diagnosed with mood disorders and 18 were diagnosed with schizophrenia. BACS data was missing for one patient, and two patients were missing data for the UPSA-2 and GAF. There were no significant differences between the schizophrenia and mood disorders group for age, t (26) ¼0.97, p¼ 0.34, or education, t(26) ¼0.93, p ¼0.37. In addition, the groups did not differ in MoCA scores, t(26) ¼0.56, p ¼0.58, BACS z-scores, t(25) ¼ 0.46, p ¼0.65, UPSA-2 z-score, t(23) ¼0.90, p ¼0.38, GAF score, t(24)¼1.21, p ¼0.24. Because there were no significant differences between groups, the mood disorder and schizophrenia groups were combined into a common severe mental illness (patients) group for the remaining analyses. When the 28 patients were compared with the 18 nonpatients, there were no significant differences in age, t(44) ¼ 0.583, p ¼0.55. Non-patients obtained approximately three more years of education compared to patients, t(44) ¼ 4.13, p o0.001. Chi-square analyses indicated there were significant differences, χ2(1) ¼6.00, p o0.01, between percentages of males in the patients (75%) and non-patients (39%) groups. There were no significant differences in race between the two groups, χ2(3) ¼6.53, p ¼0.09.

3.2. Patients vs. non-patients Separate one-way ANCOVAs with gender and education as covariates revealed significant main effects for each of the following scores: (1) MoCA, F (1, 42)¼15.29, po0.001, η2 ¼0.27; (2) BACS z-composite, F (1, 41)¼ 20.78, po0.001, η2 ¼ 0.23; (3) UPSA-2, F (1, 37)¼9.91, po0.01; η2 ¼0.21; and (4) GAF, F (1, 38)¼131.37, po0.001, η2 ¼0.78. Of note, patients scored significantly lower compared with nonpatients in all cases.

3.3. Psychometric properties of the MoCA Sensitivity and specificity of the MoCA cutoff score of o26 were examined. The cutoff of o 26 demonstrated 89% sensitivity (95% confidence interval (CI): 72–98) and 61% specificity (95% CI: 36–83). Positive and Negative Predictive Powers were 78 (95% CI: 60–91) and 79, (95% CI: 49–95), respectively. The odds ratio was 13.1 (95% CI: 2.8–60.3). The positive likelihood ratio was 2.30 (95% CI: 1.27–4.15) and the negative likelihood ratio was 0.18 (95% CI: 0.06–0.54). When we examined performance of the MoCA in individuals with functional deficits, five individuals obtained z-scores below 1 S.D. on the UPSA-2. All five individuals were from the patient group (four were diagnosed with schizophrenia and one with bipolar disorder). MoCA raw scores for these individuals ranged from 14 to 20, suggesting all of these individuals would have screened positive for cognitive impairment. Overall, the MoCA demonstrated excellent sensitivity in patients with SMI.

(2.63) (4.64) (4.19) (5.39)

(0.64) (0.81) (0.71) (0.98)

(0.67) (0.94) (1.0) (0.85)

(8.19) (9.02) (7.7) (10.6)

Table 2 Spearman's rho correlations between variables for patients with severe mental illness.

Age Education WRAT reading UPSA-2 z-score GAF MoCA raw score BACS z-score nn

Age

Education

1 0.16  0.25  0.10 0.16  0.62  0.08

1 0.01  0.14 0.19 0.18  0.07

WRAT Reading

UPSA-2 GAF MoCA z-score raw score

1 0.25 0.11 0.33 0.04

1 0.08 0.66nn 0.27

1 0.24 1 0.10 0.34

p o 0.001.

3.4. Relationship between the cognitive and functional measures For SMI patients, the MoCA total score was significantly correlated with the UPSA-2, r(24)¼0.66, po 0.001 but not with GAF scores, r(24)¼0.28, p ¼0.24. The BACS was not significantly correlated with the UPSA-2, r(23) ¼0.25, p ¼0.19, or GAF scores, r(23) ¼0.10, p ¼ 0.63. Also, there was no significant correlation between the MoCA total raw score and the BACS z-score, r(25) ¼ 0.34, p ¼ 0.08. Neither the MoCA nor the BACS scores were significantly correlated with age or education level (Table 2). Interestingly, the GAF and UPSA-2 were not significantly correlated in the current study, r(21) ¼ 0.08, p¼ 0.72. Overall, the MoCA was more closely associated with functional measures than the BACS. 3.5. Predictive validity of the MoCA and BACS The first two hierarchical regression analyses examined prediction variance in UPSA-2 scores for the MoCA and BACS as follows: (1) after Step 1, with the BACS z-score in the equation, the model was significant, R2 Δ¼0.28, FΔ (1, 39) ¼15.32, p o0.001. Addition of the MoCA z-score to the equation resulted in a significant increment in R2, R2Δ¼0.19, FΔ (1, 38) ¼13.93, po 0.001. (2) The model with MoCA z-score in Step 1 was significant, R2Δ¼ 0.45, FΔ (1, 39) ¼32.10, p o0.001. Prediction of the UPSA-2 z-score was not improved when the BACS z-score was added to Step 2, R2Δ¼0.02, FΔ (1, 38) ¼1.68, p¼ 0.20. Next, two hierarchical regressions were used to predict GAF scores. In the first regression, the BACS z-score was entered into Step 1, and the MoCA z-score was entered into Step 2. After Step 1, with the BACS z-score in the equation, the model was significant, R2Δ¼0.42, FΔ (1, 39)¼ 28.14, po0.001. Addition of the MoCA z-score to the equation resulted in a significant increment in R2, R2Δ¼0.07, FΔ (1, 38)¼5.17, po0.03. Entry of the MoCA and BACS scores was reversed for Steps 1 and 2 for the second hierarchical regression. The model was significant after Step 1, R2Δ¼ 0.40, FΔ (1, 39)¼26.14, po0.001). Prediction of the GAF score was improved when the BACS z-score was added to Step 2, R2Δ¼0.09, FΔ (1, 38)¼6.49, p¼0.02.

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Overall, the MoCA contributed unique variance beyond the BACS in predicting UPSA scores, and both the MoCA and BACS contributed unique variance in predicting GAF scores.

4. Discussion The purpose of the present study was to examine the utility of the MoCA, a brief cognitive screening instrument, in a sample of outpatients diagnosed with SMI. Overall, the results support the use of the MoCA for understanding neurocognitive deficits in patients with SMI. There were four important findings from this study. First, replicating prior research, patients obtained significantly lower scores on the cognitive measures (MoCA and BACS) and functional measures (GAF and UPSA-2) compared to healthy controls. Perhaps more importantly, there were no significant differences in scores between patients diagnosed with mood/affective disorders or schizophrenia on any cognitive or functional measures. Second, the cutoff scores of the MoCA resulted in favorable sensitivity for predicting SMI group membership. Third, the MoCA was significantly correlated with UPSA-2 scores whereas the BACS was not significantly correlated with either functional outcome measure. Finally, when entered in hierarchical regression analyses, the MoCA accounted for significant variance in UPSA-2 scores above variance accounted for by the BACS. In terms of GAF scores, both the MoCA and the BACS contributed unique variance in GAF scores. Our results suggest that the MoCA is a promising measure for use with SMI populations. In the present study, the MoCA demonstrated reasonably good utility as a clinical predictor of SMI group status. Of the patient sample, 89% scored below the cutoff of o26 on the MoCA, suggesting the MoCA is sensitive to subtle cognitive impairment. It is noteworthy that seven of the 18 healthy controls also scored below the cutoff for cognitive impairment, suggesting somewhat low specificity (61%). This finding is consistent with Rossetti et al. (2011) who reported that a large number of individuals score below the recommended cutoff of o26. As a screening measure, high levels of sensitivity are of utmost concern and lower specificity is acceptable. For this reason, alternative cutoff scores were not examined, though this may be a valuable direction for future research (Larner, 2012). In the present study, the BACS was not significantly correlated with the UPSA-2 or GAF scores. These results are not consistent with Keefe et al. (2006), who found the BACS was correlated r ¼0.65 with the UPSA-2. One possible explanation for the discrepancy in findings is differences in populations between the two studies. Keefe et al. (2006) utilized inpatients undergoing behavioral therapy while the current study assessed outpatients at a community mental health clinic. Not only does inpatient hospitalization suggest more severe functional deficits, but their lower zscores relative to those reported in the current study suggest more significant cognitive impairment in their sample as well. This may account for more significant associations between the two measures than those found in the current study. This study employed two measures of functional ability: a performance-based measure (UPSA-2 scores) and clinicians' ratings of functioning (GAF scores). We found that clinician-rated assessments of global functioning were not significantly correlated with performance-based measures of functional ability, suggesting the two measures index different constructs. Versterager et al. (2012) suggested performance-based measures of functional capacity may be more similar to neurocognitive measures than realworld function, measured by GAF scores. With this caveat in mind, we examined whether the BACS and MoCA contributed significant unique variance to the UPSA-2 and GAF scores. First, we found the MoCA, not the BACS, contributed significant variance to the UPSA2. Second, MoCA and the BACS contributed variance to GAF scores

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in the current study. Of note, Keefe et al. (2006) reported that UPSA-2 scores did not contribute unique variance in predicting real-world functioning, measured by patients' self-report, beyond variance accounted for by the BACS. These findings further support the hypothesis that performance-based measures and other subjective measures of real-world functioning do not measure a unitary construct of functional abilities. However, the MoCA contributed unique variance to both clinician-rated and objective measures of functioning, further supporting its utility in cognitive screening for schizophrenia. Limitations of the current study include a relatively modest sample size. Despite small sample sizes, statistical significance was observed at medium to large effect sizes. Another limitation is a disproportionate number of males in the patient sample. In addition, individuals in the healthy control sample had significantly more years of education. Gender differences and years of education were controlled for in statistical analyses, though results did not change significantly when these covariates were used. Further, scores on the MoCA, BACS, UPSA-2, and GAF were not correlated with demographic variables such as age or education. Also, the MoCA was not designed to assess specific cognitive deficits of schizophrenia, as schizophrenia has also been associated with impaired verbal learning and processing speed deficits. It is possible that minor adjustments could be made to the current administration and scoring that would incorporate additional domains without extending time of testing. For example, two verbal learning trials are administered but are not scored. In addition, there are no processing speed measures in the MoCA; however, several executive functioning tasks could potentially be timed. Introduction of scoring criteria for these domains may prove useful in increasing sensitivity to cognitive deficits in individuals with severe mental illness. While examination of alternative scoring of the MoCA is beyond the scope of the current study, future research may wish to consider examination of these domains. The MoCA has demonstrated utility in the assessment of a variety of cognitive disorders and has been growing in popularity since its introduction in 2005. The findings of the current study provide evidence for the validity of the MoCA as a screening tool for measuring cognitive impairment in outpatients with severe mental illness. While preliminary, these findings also suggest the MoCA is significantly associated with performance-based measures of real-world functioning, the UPSA-2, and may provide additional information regarding functional abilities to patients' inter-disciplinary teams. Future studies should continue to examine the reliability and validity of the brief screening measures, including the MoCA for use in screening for cognitive impairment in severe mental illness. Test–retest reliability of the MoCA as well as its ability to monitor cognitive changes over time and across episodes also needs to be established in this population.

Acknowledgments The authors acknowledge the efforts of Gina Najolia, Kyle Minor, Laura Brown, and Rebecca MacAulay for their help with data collection. They also thank the subjects for their participation and MMO Behavioral Health Systems for the assistance in subject outreach. This study received no specific grant from any funding agency, commercial or not-for-profit sectors. There are no conflicts of interest. References American Psychiatric Association, 2000. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. American Psychiatric Association, Washington, DC (text rev. Author).

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Investigation of the Montreal Cognitive Assessment (MoCA) as a cognitive screener in severe mental illness.

This study examined the Montreal Cognitive Assessment (MoCA) as a neurocognitive screener and its relationship with functional outcomes in a sample of...
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