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. Author manuscript; available in PMC: 2012 Dec 1.
Published in final edited form as: Schizophr Res. 2011 Oct 12;133(1-3):212–217. doi: 10.1016/j.schres.2011.09.004

Relationship of Neurocognitive Deficits to Diagnosis and Symptoms across Affective and Non-Affective Psychoses

Kathryn E Lewandowski a,b, Bruce M Cohen a,b, Matcheri S Keshavan b,c, Dost Öngür a,b
PMCID: PMC3225688  NIHMSID: NIHMS325821  PMID: 21996265

Abstract

Introduction

Neurocognitive dysfunction is believed to be a core feature of schizophrenia and is increasingly recognized as a common symptom dimension in bipolar disorder. Despite a copious literature on neurocognition in these disorders, the relationship amongst neurocognition, symptoms, and diagnosis remains unclear. We examined neurocognitive functioning in a cross-diagnostic sample of patients with psychotic disorders. Based on previous findings, it was hypothesized that neurocognitive functioning would be impaired in all three patient groups, and that groups would be similarly impaired on all neuropsychological measures. Additionally, we predicted that negative symptoms but not positive, general, or mood symptoms, would be associated with neurocognitive functioning.

Method

Neurocognitive functioning and symptoms were assessed in participants with schizophrenia (n=25), schizoaffective disorder (n=29), or bipolar disorder with psychosis (n=31), and in healthy controls (n=20).

Results

Neurocognitive functioning was significantly impaired in all patient groups, and groups did not differ by diagnosis on most measures. A series of linear regressions revealed that negative symptoms (but no other clinical symptom) predicted poorer executive functioning across groups. Diagnosis was not a significant predictor of any neurocognitive variable.

Discussion

Neurocognitive deficits were pronounced in this cross-diagnostic sample of patients with psychotic disorders, and did not differ by diagnosis. Neurocognitive dysfunction may represent a symptom dimension that spans diagnostic categories, and may reflect shared pathogenic processes. As neurocognitive dysfunction is among the strongest predictors of outcome in patients, efforts to treat these deficits, which have shown promise in schizophrenia, should be extended to all patients with psychosis.

Keywords: Bipolar, Schizophrenia, Schizoaffective, neurocognitive, comparative

1. Introduction

Neurocognitive dysfunction is a core feature of schizophrenia (SZ) and related disorders, and is increasingly recognized as a prominent feature of bipolar disorder (BD). Cognitive functioning is among the strongest predictors of outcome in patients with SZ or BD (Barch, 2009, Green, 1996, Green, 2006, Martino et al., 2009, Tabares-Seisdedos et al., 2008). Despite a copious literature on neurocognition in SZ and a growing literature in BD, comparisons of neurocognition across disorders have yielded inconsistent findings, and the relationships amongst cognitive and other symptom domains across disorders remain unclear.

Comparisons of cognitive impairment between diagnoses have produced mixed results (see (Lewandowski et al., 2011) for review). Several reports indicate that patients with BD or schizoaffective disorder (SZA) exhibit a similar pattern of deficits to patients with SZ, but with levels of functioning between patients with SZ and healthy controls (Altshuler et al., 2004, Daban et al., 2006, Heinrichs et al., 2008, Krabbendam et al., 2005, Reichenberg et al., 2009, Schretlen et al., 2007, Stip et al., 2005). A cross-diagnostic comparison of patients with SZ, SZA, BD and MDD found neurocognitive deficits in all patient groups, but patients with SZ were most severely impaired (Reichenberg et al., 2008). However, other reports show no qualitative or quantitative neurocognitive differences between BD and SZ patients (Balanza-Martinez et al., 2005, McClellan et al., 2004, McGrath et al., 1997, Mojtabai et al., 2000, Morice, 1990, Simonsen et al., 2011),or amongst patients with SZ, SZA or BD with psychosis (Glahn et al., 2006, Simonsen et al., 2011, Smith et al., 2009). Lastly, patient groups may differ on some neurocognitive measures but not others. A study comparing patients with SZA, SZ, and BD reported that patients with SZA performed similarly to patients with SZ on some tasks, but that on other tasks scores fell along a continuum from SZ to SZA to BD (Szoke et al., 2008).

Cognitive functioning is most commonly associated with negative symptoms in SZ (Andreasen et al., 1990, Berman et al., 1997, Leeson et al., 2009, Nieuwenstein et al., 2001). Patients with primarily positive symptoms or paranoid subtype perform better on neurocognitive tasks relative to negative symptom or disorganized subtype patients in most (Brazo et al., 2002, Cvetic and Vukovic, 2006, Dominguez et al., 2009, Hill et al., 2001, Wang et al., 2008, Zalewski et al., 1998), although not all (Tam and Liu, 2004) studies. In BD, history of psychosis has been associated with poorer cognitive functioning (Glahn et al., 2007, Glahn et al., 2006, Martinez-Aran et al., 2008, Simonsen et al., 2011), as are factors such as earlier age of onset (Osuji and Cullum, 2005) and manic or mixed states (Dixon et al., 2004, Sweeney et al., 2000). A recent report comparing patients with SZ, BD I, BD II or SZA found that all patients with psychosis performed worse than patients with no history of psychosis or controls, but did not differ from each other (Simonsen et al., 2011). Again, these findings do not always replicate (Sanchez-Morla et al., 2009, Selva et al., 2007).

The present study aimed to examine neuropsychological functioning in a cross-diagnostic sample of patients with SZ, SZA or Bipolar I Disorder with psychosis (PBD) and the relationship of cognition to diagnosis and clinical symptoms. Based on past studies, we hypothesized that a) patients with SZ, SZA and PBD would exhibit neuropsychological deficits relative to healthy controls but would not differ from each other, and b) negative symptoms – but not mood, positive, or general symptoms – would be associated with poorer neuropsychological functioning.

2. Method

2.1 Participants

Patients with diagnoses of SZ, SZA, or PBD, and healthy controls between the ages of 18 and 55 were eligible to participate. Participants were recruited through the Schizophrenia and Bipolar Disorder Program (SBDP) at McLean Hospital, and all procedures were approved by the McLean IRB. The present sample consisted of participants with SZ (n=25), SZA (n=29) or PBD (n=31) and 20 control participants. Participants were recruited through inpatient units after stabilization or via fliers posted at the hospital. Controls were recruited from the SBDP or through online advertisements. All patients endorsed past or current psychosis. All participants with PBD were manic or hypomanic at the time of assessment; all participants with SZA had bipolar subtype. All SZA patients had a chronic psychotic illness and six had YMRS scores >20 at the time of testing. Participants had no lifetime history of substance dependence, no substance abuse within the past three months, and no history of seizure disorder or history of head injury with loss of consciousness. Diagnosis was established using the Structured Clinical Interview for DSM-IV-TR (First et al., 1996) completed through patient interview, medical record review, and – when possible – consultation with the participant's treatment provider(s).

2.2 Materials

Participants were administered clinical and neuropsychological assessments and a diagnostic interview. The neuropsychological battery included: Trails A and B (Trails; processing speed, executive functioning); Brief Visuospatial Memory Test (BVMT; visuospatial learning and memory); Stroop Color and Word Test Color and Color-Word forms (Stroop; attention, processing speed, executive functioning); Hopkins Verbal Learning Test – Revised (HVLT; verbal learning and memory); Category Fluency (verbal fluency). Raw scores were converted to standard scores using published normative data (Benedict, 1997, Benedict et al., 1998, Gladsjo et al., 1999, Golden, 1978, Selnes et al., 1991). Standardized scores were converted to z-scores for ease of comparison. A neuropsychological composite score reflecting overall neuropsychological performance was calculated by averaging the above z-scores.

The SCID was administered by trained clinicians to diagnose primary mood and psychotic disorders and comorbid substance use or anxiety disorders. The SCID was never administered by the same study staff conducting the cognitive assessments. SCID interviewers met routinely for reliability exercises and to discuss difficult cases and arrive at a consensus diagnosis. The assessment also included the Positive and Negative Syndrome Scale (PANSS; (Kay et al., 1987)), the Young Mania Rating Scale (YMRS; (Young et al., 1978)), and the Montgomery-Asberg Depression Rating Scale (MADRS; (Montgomery and Asberg, 1979)) to evaluate current psychotic and mood symptoms.

Information about medications at time of assessment was obtained from the discharge medication list (inpatients) or by patient report (outpatients). For participants with complete data, chlorpromazine (CPZ) equivalent dose was calculated; some data (e.g. dosage information) were missing in 9 subjects. For typical antipsychotics, we calculated CPZ equivalents based on the Schizophrenia Patient Outcomes Research Team (PORT) recommendations (Lehman and Steinwachs, 1998). For most atypical antipsychotics, we used the CPZ equivalent doses published by Woods (Woods, 2003). For risperidone and paliperidone, we used guidelines by Gardner et al. (2010).

2.3 Procedures

All participants completed the above assessment lasting approximately 3 hours. The SCID was administered during the first session and the neurocognitive assessment during a second session. Clinical symptoms were assessed during either the first or second session. In most cases sessions were conducted within one week of each other. If the neuropsychological assessment was conducted more than one week after the clinical assessment, symptom measures were re-administered and the more recent data used in order to ascertain state clinical symptoms at the time of neuropsychological testing. While diagnostic interviews and neuropsychological assessments were conducted by different staff members, neuropsychological and symptom assessments were often conducted by the same study staff, and staff members were not always able to remain blind to group membership.

2.4 Statistical Approach

Groups were compared on clinical and neurocognitive measures using ANOVAs, first including patient groups and controls, then including only patient groups. We examined both the neuropsychological composite score and individual domain scores to determine whether specific domains differed by diagnosis, even if the overall composite did not. When significant differences were detected amongst the three patient groups, pair-wise t-tests were conducted. This approach was taken to reduce the number of pair-wise comparisons and the risk for Type I errors. A series of linear regressions was conducted examining the effects of diagnosis on neurocognitive outcomes after controlling for demographic variables (age, sex, education), lifetime illness and state symptom severity (lifetime hospitalizations and treatment setting at testing (inpatient vs. outpatient)), and CPZ equivalents. We then examined the effects of positive, negative, and mood symptom ratings on neurocognitive outcomes after accounting for the above demographic and severity measures and diagnosis. While we ran a number of comparisons, we did not correct for multiple comparisons because we 1) anticipated modest effects which – given the sample size – are unlikely to withstand multiple comparisons corrections creating a potential for Type II errors, and 2) tested a priori hypotheses regarding specific relationships of diagnosis and clinical symptoms to neurocognitive variables.

3. Results

Table 1 presents demographic data by diagnosis. Patient groups did not differ in age, education, sex, or ethnicity. Control participants had significantly higher educational attainment than any patient group. Patients differed in treatment setting at enrollment (inpatient: PBD>SZ>SZA) and number of lifetime hospitalizations (SZA>SZ> PBD). All patients were taking at least one psychiatric medication at the time of testing. Groups differed on rates of antidepressant medication (X2 = 8.30, p = 0.016; SZ = 44%; SZA = 46%; PBD = 14%). Groups did not differ in rates of antipsychotic (typical or atypical), mood stabilizer, or benzodiazepine use; however, groups did differ in terms of CPZ equivalents (SZ, SZA>PBD; Table 1). Three participants were taking an anticholinergic medication (benztropine).

Table 1.

Demographic Variables by Diagnosis

SZ(n=25) SZA(n=29) PBD(n=31) Control(n=20)
Age 37.8 (11.4) 38.3 (9.1) 33.9 (11.6) 40.4 (8.7)
Educationa 4.2 (1.4) 4.5 (1.7) 5.0 (1.2) 6.1 (1.4)***
% Female 40% 55% 65% 45%
% Caucasian 68% 79% 71% 70%
# Lifetime
Hospitalizations 4.0 (2.2) 4.8 (1.8) 3.5 (1.8)* n/a
% Inpatient 68% 48% 87%** n/a
CPZ Equivalent 650 (435) 606 (499) 304 (271)** n/a
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***

p<.001

**

p<.01

*

p<.05

a

Education is coded based on the SCID Education and Work History scale: 1 = grade 6 or less; 2 = grade 7–12 (without graduating); 3 = high school grad or equivalent; 4 = part college; 5 = graduated 2 year college; 6 = graduated 4 year college; 7 = part graduate/professional school; 8 = completed graduate/professional school

Table 2 presents clinical symptom data by group. Patient groups differed on YMRS and PANSS N scores. T-tests revealed that the SZ and SZA groups differed from the PBD group on YMRS scores (t=−4.41, p<.001, and t=−4.44, p<.001, respectively) but did not differ from each other (t=0.05, p=.96). The SZ group scored higher on the PANSS N than the SZA (t=2.32, p<.05) or PBD (t=3.80, p<.001) groups; the SZA group scored higher than the PBD group (t=−2.01, p<.05). Groups did not differ on any other clinical measure.

Table 2.

Symptom Scales by Diagnosis

SZ(n=25) SZA(n=29) PBD(n=31) F-statistic Control(n=20)a
YMRS 13.8 (10.5) 13.7 (12.2) 29.3 (14.7) 14.52*** 0.6 (0.8)
MADRS 11.1 (7.5) 13.6 (9.4) 11.7 (7.2) 0.69 1.3 (1.9)
PANSS P 20.5 (8.1) 17.5 (8.0) 19.8 (8.2) 1.05 7.1 (0.4)
PANSS N 15.2 (5.5) 12.2 (4.5) 9.6 (5.6) 8.20** 7.4 (0.7)
PANSS G 29.2 (8.0) 28.1 (8.6) 31.6 (10.1) 1.19 16.7 (1.3)
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**

p<.01

***

p<.001

a

control participants differed significantly from all patient groups on all clinical measures (p<.001)

Control participants scored within 0.4 standard deviations of the mean on the neuropsychological composite and on all measures except the Stroop Color score (−0.86). The distribution of scores does not suggest that this finding is being driven by one or more outliers. Given that controls performed at the level that would be expected on all other neurocognitive measures and that patients also scored lower on this measure than on any other test, the normative data used for standardization appear elevated relative to our sample. Controls performed significantly better than all patient groups on all measures (Figure 1). Patient groups did not differ on the neurocognitive composite (F=2.20; p=0.12). In terms of individual tasks, groups differed only on Trails B (SZ < SZA, PBD) (t=−2.58, p<.05, and t=−2.91, p<.01, respectively). The SZA and PBD groups did not differ from each other (t=−0.27, p=.79).

Figure 1. Neurocognitive Measures by Diagnosis.

Figure 1

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* = p<.05

Note: control participants differed significantly from all patient groups on all cognitive measures: Composite: p<.001; BVMT: p<.01; HVLT: p<.001; Trails A: p<.001; Trails B: p<.001; Category Fluency: p<.01; Stroop Color: p<.001; Stroop Color-Word: p<.001

We examined the effects of diagnosis and symptoms on neurocognitive outcomes using a series of linear regressions. Several demographic and symptom severity variables were predictive of neurocognitive functioning. Increasing age and lower educational attainment predicted worse performance on Trails B (t=−3.08, p=.003 and t=4.02, p<.001, respectively) and Stroop Color-Word (t=−2.40, p=.02 and t=2.07, p=0.04, respectively), and age significantly predicted the composite (t=−2.42, p=.02). Inpatient status and more prior hospitalizations predicted poorer BVMT scores (t=−2.42, p=.02 and t=−2.23, p=.03, respectively), and treatment setting predicted the composite and Category Fluency scores (t=−2.15, p=.04 and t=−2.13, p Sex predicted Category Fluency and Stroop Color-Word scores (t=−2.29, p=.03 and t=−2.79, p=.007, respectively), with males performing better. CPZ predicted Trails A scores (t=−2.19, p=.03), but did not predict any other neuropsychological variable or the composite.

After controlling for age, sex, education, number of lifetime hospitalizations, treatment setting, and CPZ equivalents, diagnosis was not a significant predictor of any neurocognitive variable. Negative symptoms predicted Stroop Color-Word (t=−2.43, p=.018) and Trails B (t=−2.59, p=.012) – both measures of executive functioning – after controlling for demographic and severity confounders and diagnosis (Figure 2). Depression, mania, positive and general symptoms were not predictive of any neurocognitive outcome.

Figure 2.

Figure 2

Open in a new tab

Negative Symptoms and Neurocognitive Score by Diagnosis

4. Discussion

The present study examined neurocognitive functioning in patients with SZ, SZA and PBD across multiple cognitive domains including verbal and visuospatial learning and memory, verbal fluency, executive functioning and processing speed. Our sample was adequately powered to detect medium effects using well-validated measures in patients with psychotic disorders. We aimed to reduce heterogeneity within groups by selecting for symptoms that may be relevant to neurocognitive functioning: all participants had a history of psychosis; all participants with SZA had bipolar subtype; all participants with PBD were in a manic or hypomanic episode at the time of testing. All patient groups performed worse than controls on all measures, and between one and two standard deviations below the mean on all measures except Trails A. Groups did not differ by diagnosis on most neurocognitive variables. Patients with SZ performed worse than patients with PBD or SZA on one measure of executive functioning (Trails B) but not another (Stroop Color-Word). We did not find evidence of domain-specific deficits within or among diagnoses.

As hypothesized, negative symptoms, but no other clinical symptoms, predicted neurocognitive outcomes. Negative symptoms predicted measures of executive functioning and selective attention, but were not associated with visual or verbal learning and memory, verbal fluency, or processing speed. The association between negative symptoms and executive deficits remained significant even after accounting for diagnosis, suggesting that negative symptoms themselves are associated with poorer executive performance. While groups differed in terms of negative symptoms, within diagnoses negative symptoms were similarly associated with executive deficits. A post-hoc correlation analysis examining the 7 items from the PANSS Negative Symptom subscale and executive functioning scores revealed that Blunted Affect, Emotional Withdrawal, and Abstract Thinking were modestly correlated with Stroop Color-Word (r= −0.25, −0.27, and −0.32, respectively), and Spontaneity/Flow of Conversation and Abstract Thinking were modestly to moderately correlated with Trails B (r= −0.24 and −0.41, respectively). All other correlations were trivial to small, and non-significant (p>.05). No single, large correlation drove the relationship between executive functioning/selective attention and negative symptoms, although, not surprisingly, abstract thinking was correlated with both neurocognitive scores.

The association between neurocognitive deficits and negative symptoms is often reported in SZ but not in BD, perhaps due to the assumption that negative symptoms are absent or minimal in BD. Toomey et al. (1998) found that both positive and negative factors generalized to mood disorders groups. We found that negative symptoms were present in PBD, albeit at lower levels than SZ, and that they were associated with poorer executive functioning. Across psychotic disorders, negative symptoms appear to be associated with executive dysfunction.

Within psychotic disorders, neurocognitive dysfunction appears to represent an illness dimension that is detectable in cross-diagnostic samples and is associated with specific symptom dimensions (i.e. negative symptoms). These findings have implications for treatment and may inform investigations of the pathophysiology of psychotic disorders. The relationship between negative symptoms and executive deficits may reflect a shared neurobiology that is not specific to SZ, and may be indicative of maldevelopment of specific neural pathways. For instance, dopamine hypoactivity in PFC may relate to both negative symptoms and neurocognition (e.g. (Alves Fda et al., 2008, Goldman-Rakic and Selemon, 1997, Knable and Weinberger, 1997). Identification of a specific relationship between executive deficits and negative symptoms in a cross-diagnostic sample supports hypotheses regarding a shared neurobiology related to these outcomes in broader populations than just SZ.

Cognitive impairments are associated with poorer functional outcomes in SZ and BD, and longer time to recovery after a first episode in BD patients (Barch, 2009, Bowie et al., 2010, Green, 1996, Green, 2006, Gruber et al., 2008, Martino et al., 2009, Tabares-Seisdedos et al., 2008). While neurocognitive dysfunction is increasingly recognized as a key treatment target in patients with SZ, our findings indicate that this symptom dimension is an important target in patients with SZA and BD with psychotic features, as well.

The present study has several limitations. First, our sample was experiencing substantial symptoms at the time of testing; thus, it is not clear that our participants are representative of all patients with psychotic disorders or would continue to share similar levels of neurocognitive dysfunction after recovery. Our participants' symptom scores were at the level that would be expected of patients with psychosis who experience periodic symptom exacerbations, making these findings generalizable to patients experiencing an acute episode or more severe chronic illness. Follow-up studies tracking participants through both acute and remitted phases of illness will help to clarify these questions.

We examined relationships amongst neuropsychological variables and CPZ equivalents, but we did not control for all medication effects. While some findings suggest that second generation antipsychotics are associated with small improvements in neurocognitive functioning in SZ (Harvey and Keefe, 2001, Harvey et al., 1990, Keefe et al., 2007, Woodward et al., 2005), polypharmacy and high dosing appear to be associated with poorer cognitive performance (Elie et al., 2010). Similarly, antipsychotics, lithium, and anticonvulsants may be associated with poorer cognitive performance in patients with BD (Balanza-Martinez et al., 2010, Donaldson et al., 2003, Stip et al., 2000, Wingo et al., 2009). Our groups differed in CPZ equivalents; however, we found that CPZ was associated only with Trails A scores and did not affect the relationship between diagnosis and neurocognitive outcomes. While medications may have affected performance on some tasks, it does not appear that medication effects account for the relationships between neurocognitive and clinical measures.

Substantial evidence exists of shared genetic liability between SZ and BD (e.g. (Craddock et al., 2006, Kendler et al., 1998, Torrey, 1999, Tsuang et al., 1980, Valles et al., 2000)). The identification of symptom dimensions such as neurocognitive dysfunction that are shared across related disorders supports increasing skepticism of stark Kraepelinian boundaries between SZ and affective disorders (e.g. (Craddock and Owen, 2007)). Study of the expression and course of major features of illness may help clarify aspects of specific pathological processes that are shared or distinct in these disorders. Additionally, the present findings indicate that treatments targeting cognitive deficits such as cognitive remediation, which has shown promise in patients with SZ (Bell et al., 2007, Bell et al., 2008, Eack et al., 2009, Fisher et al., 2009, Kurtz et al., 2007, McGurk and Wykes, 2008, Medalia and Choi, 2009, Wykes, 2008) and SZA (Lewandowski et al 2011), should be extended to all patients with psychosis. Finally, our findings support the view that translational studies of cross-cutting domains of neural dysfunction as envisioned in the R-DoC concept (Insel et al 2010) are likely to help us better understand complex psychiatric disorders better than categorical approaches alone.

Acknowledgments

We thank Danielle Pfaff for her help with data collection and participant recruitment.

Footnotes

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