Clinical symptoms and neuropsychological deficits are longitudinally associated with functional outcome in chronic psychiatric cohorts. The current study extended these findings to young and early-course psychiatric outpatients, with the aim of identifying cognitive markers that predict later socio-occupational functioning.
At baseline, 183 young psychiatric outpatients were assessed. Ninety-three returned for follow-up (M = 21.6 years old; SD = 4.5) with an average re-assessment interval of 21.6 months (SD = 7.0), and primary diagnoses of major depressive disorder (n = 34), bipolar disorder (n = 29), or psychosis (n = 30). The primary outcome measure was cross-validated with various other functional measures and structural equation modelling was used to map out the interrelationships between predictors and later functional outcome.
Good socio-occupational functioning at follow-up was associated with better quality of life, less disability, current employment and being in a romantic relationship. The final structural equation model explained 47.5% of the variability in functional outcome at follow-up, with baseline neuropsychological functioning (a composite of memory, working memory and attentional switching) the best independent predictor of later functional outcome. Notably, depressive and negative symptoms were only associated with functioning cross-sectionally. Diagnosis at follow-up was not associated with functional outcome.
Neuropsychological functioning was the single best predictor of later socio-occupational outcome among young psychiatric outpatients. Therefore, framing psychiatric disorders along a neuropsychological continuum is likely to be more useful in predicting functional trajectory than traditional symptom-based classification systems. The current findings also have implications for early intervention utilising cognitive remediation approaches.
Citation: Lee RSC, Hermens DF, Redoblado-Hodge MA, Naismith SL, Porter MA, et al. (2013) Neuropsychological and Socio-Occupational Functioning in Young Psychiatric Outpatients: A Longitudinal Investigation. PLoS ONE 8(3): e58176. doi:10.1371/journal.pone.0058176
Editor: James D. Clelland, The Nathan Kline Institute, United States of America
Received: October 23, 2012; Accepted: January 30, 2013; Published: March 1, 2013
Copyright: © 2013 Lee et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: IBH was funded by an NHMRC Program Grant (No. 566529) and Australian Fellowship (No. 511921). DFH was supported by a grant from NSW Health Mental Health and Drug & Alcohol Office. SLN was funded by an NHMRC Clinical Research Fellowship (No. 402864). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have read the journal’s policy and have the following conflicts. IBH was funded by an NHMRC Program Grant and Australia Fellowship. DFH was supported by a grant from NSW Health Mental Health and Drug & Alcohol Office. SLN was funded by an NHMRC Career Development Award. IBH has led projects for health professionals and the community supported by governmental, community agency, and drug industry partners (Wyeth, Eli Lily, Servier, Pfizer, Astra Zeneca) for the identification and management of depression and anxiety. He has served on advisory boards convened by the drug industry in relation to specific antidepressants, including nefazodone, duloxetine, and desvenlafaxine, and has participated in a multicenter clinical trial of agomelatine effects on sleep architecture in depression. He has participated in Servier-sponsored educational programs related to circadian-based therapies. DFH has previously received honoraria for educational seminars from Janssen-Cilag. MARH has received financial support from Eli- Lily for a multisite trial of cognitive remediation in psychosis, and is a co-investigator in an Australian Rotary funded project investigating the synergistic effects of cognitive remediation and vocational training in psychosis. She was also a co-investigator in a drug trial with Novartis pharmaceutical who sponsored overseas travel in 2011, and was also involved in NHMRC funded trials in psychosis. EMS has received honoraria for educational seminars related to the clinical management of depressive disorders supported by Servier and Eli-Lilly pharmaceuticals. She has also participated in a national advisory board for the antidepressant compound Pristiq, manufactured by Pfizer. These funding agencies had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
Traditionally in mental health, there has been a strong emphasis on defining recovery in terms of symptom resolution. However, there is increasing recognition that defining remission solely on the basis of clinical symptoms is inadequate. Frequently, these ‘remitted’ patients with diagnoses of major depression , bipolar disorder  or psychosis  remain functionally impaired. Zimmerman et al.  demonstrated that half of patients with major depression who were classified as remitted based on rating scale criteria did not consider themselves to be in remission. Critically, these individuals were significantly more impaired in their work performance and social relationships. It is likely that subsyndromal symptoms, particularly depression  and negative symptoms , contribute to these ‘residual’ functional deficits across the major psychiatric disorders. Of significance, cognitive vulnerabilities may additionally contribute to persisting disability given evidence that neuropsychological dysfunction predicts later socio-occupational functioning, over and above symptom levels, as observed in depression , bipolar disorder , and schizophrenia .
While in chronic cohorts psychotic disorders are associated with more pronounced neuropsychological and psychosocial disability than affective disorders , neuropsychological performance appears to predict functional outcome irrespective of diagnosis . Accordingly, neuropsychological deficits have been proposed as endophenotypes that cut across traditional diagnostic boundaries . This idea of intermediate traits spanning the major psychiatric disorders is not new, and is buttressed by findings of neurometabolic , neuroanatomical , and neurophysiological  factors common to major depression, bipolar disorder and psychosis. The overarching premise is that affective and psychotic disorders may exist along a continuum , and exploring shared dimensional features is likely to be more informative prognostically, and for the understanding of pathophysiology (e.g. for identifying suseptibility genes) , than categorising individuals according to their idiosyncratic symptomatology . Indeed, within-disorder heterogeneity is not uncommon in the current diagnostic classification systems. Furthermore, separating patients based on syndromes is often confounded by varying illness trajectories, as well as the finding that diagnoses can be relatively unstable, particularly in the early stages of psychiatric illness .
To date, there is strong evidence that cognitive dysfunction is shared across early-course major depression , bipolar disorder , and schizophrenia . Consistent with studies in well-established cohorts, neuropsychological deficits appear longitudinally predictive of functional outcome over and above symptomatology in early psychosis  and possibly, in first-episode mania . To our knowledge, such relationships have not been explored in first-episode major depression. Identification of cognitive markers with predictive value may ultimately help to direct early intervention strategies. For example, some deficits may be indicative of underlying structural and functional brain changes, which in turn may be amenable to novel pharmacotherapies, or cognitive remediation, with the latter already showing some promise as an early intervention strategy .
The aim of the current study was to determine whether neuropsychological functioning longitudinally predicts socio-occupational functioning over and above symptomatology in a young mixed psychiatric sample with major depression, bipolar disorder or psychosis. We hypothesised that neuropsychological functioning, as well as depressive and negative symptoms, would contribute to later functional outcome. Structural equation modelling techniques were used to estimate the interrelationships among clinical variables more accurately than past studies utilising multivariate regression approaches .
Materials and Methods
The current study was approved by the University of Sydney Human Research Ethics Committee. After complete description of the study to the subjects at each assessment time-point, those aged 16 years and above gave written informed consent to participate, whereas for those under the age of 16 years both the participant and their legal guardian gave written informed consent in accordance with Human Research Ethics Committee guidelines and Australian law.
Participants were recruited from specialised tertiary referral services (‘Treatment Centre’) for the assessment and early intervention of mental health problems in young people (Youth Mental Health Clinic, YMHC, at the Brain & Mind Research Institute, BMRI; and headspace, Campbelltown, Sydney, Australia; Inner West Area Health Service First Episode Psychosis Intervention Services) . Inclusion criteria were restricted to persons aged 12 to 35 years seeking professional help primarily for a depressive (unipolar or bipolar) and/or psychotic syndrome. Subjects were excluded if they were diagnosed with a neurological condition, had insufficient English-language skills, or intellectual disability (premorbid IQ<70). Patients were assessed by a psychiatrist according to DSM-IV TR [American Psychiatric Association, 25] criteria at two time points (and diagnoses were subsequently confirmed through case-note review), and underwent a comprehensive clinical and neuropsychological assessment (including self-report questionnaires) by clinical neuropsychologists or trained research psychologists at baseline. A repeat clinical assessment (with self-report questionnaires) was also conducted at follow-up.
The BMRI Structured Interview for Neurobiological Studies was conducted to gather key demographic and clinical information . Symptoms were rated using the expanded version of the Brief Psychiatric Rating Scale (BPRS-E) . Subscores were calculated based on a previously derived component structure (depressive, positive, negative and manic symptoms) .
Premorbid intellectual functioning (premorbid IQ) was estimated using the Wechsler Test of Adult Reading (WTAR)  or Wide Ranging Achievement Test – fourth edition (WRAT-4; for participants younger than 16 years) . Various neuropsychological tests were also administered, including Trail Making Test – Part A and B (TMT-A and TMT-B) , Logical Memory II Percentage Retention [LM-Ret; participants younger than 16 years (n = 7) were excluded from this variable as there was no equivalent delayed retention index] , Rey Auditory Verbal Learning Test 20-Minute Percentage Retention (RAVLT-Ret) , Rapid Visual Processing Hits (RVP-Hits), Spatial Span length (SSP), Paired Associates Learning adjusted errors (PAL), and Intra-/Extradimensional Shift total errors (IED) from the Cambridge Neuropsychological Test Automated Battery (CANTAB) , and Letter (FAS) and Category (animals) Fluency . Neuropsychological scores were standardised into z-scores adjusting for demographic variables based on internal (LM-Ret, CANTAB)  and external normative samples (TMT-A & TMT-B, RAVLT-Ret, Letter and Category Fluency) –.
Various socio-occupational measures were administered. The Social and Occupational Functioning Scale (SOFAS) rated by the treating psychiatrist and research psychologist  was the primary functional outcome measure used. Additional patient-rated information relating to work and study, financial and living situation, and relationship status, and structured questionnaires, including total scores from the World Health Organization Quality of Life BREF version (WHOQOL-BREF)  and the Disability Assessment Scale II (WHODAS-II) , were included to cross-validate the clinician-rated SOFAS.
Statistical analyses were conducted using Statistical Package for the Social Sciences Version 20.0 (SPSS 20.0) and Analysis of Moment Structures Version 20.0 (AMOS 20.0). Participants were initially dichotomised into two groups, as adapted from similar methodology , based on their follow-up SOFAS scores. Scores greater than or equal to 61 were classed as ‘good outcome’, whereas a lesser score was categorised as ‘poor outcome’. This dichotomy was chosen since it was more meaningful to examine outcome groups anchored to qualitative descriptors (i.e. ‘generally functioning well or better’ compared with ‘moderate impairment or worse’) than using correlation coefficients based on a continuous SOFAS score. In other words, dichotomising the SOFAS score means that the sample characteristics are immediately interpretable in reference to whether patients are functioning well or poorly. Analyses of Variance (ANOVAs; Welch’s correction where homogeneity of variance was violated according to Levene’s test) and chi-squared tests were conducted to cross-validate the clinician-rated SOFAS score with other objective and subjective functional indices. ANOVAs were also conducted on baseline variables to explore potential factors associated with follow-up SOFAS outcome. Neuropsychological indices that differed according to outcome group were reduced to latent variables by confirmatory factor analysis.
Since structural equation modelling is particularly sensitive to the full range of statistical variability, continuous SOFAS scores were used in this procedure. A two-step approach to structural equation modelling was adopted . First, the measurement model (latent neuropsychological functioning) was tested for model fit. Second, we tested the structural model (the relationships between measured variables) using a model-trimming approach through an iterative process in which non-significant paths with the smallest contribution were sequentially eliminated from a saturated model (where all variables were allowed to correlate) until a best fitting model was ascertained to explain the relationships between baseline symptoms and follow-up functioning. All baseline cross-sectional relationships were treated as correlational due to uncertainty of directionality. The model was improved through the inclusion of follow-up symptoms and compared to the original model using nested-models. Latent baseline neuropsychological functioning was then entered into the model to determine whether it explained functional outcome over and above other predictors, again using nested-models. Overall model fit was determined using the χ2 statistic, Bentler comparative fit index (CFI) , Bentler-Bonnett nonnormed fit index (NFI) , and Root Mean Square Error of Approximation (RMSEA)  with 90% confidence interval. A good-fitting model was indicated by a non-significant χ2 test (indicating non-significant difference between the covariance matrix of the data and the model), a CFI and NFI of greater than 0.90 (indicating that the current model was superior to a null model where all paths are constrained to zero), and a RMSEA of less than 0.05 with an upper confidence interval bound of less than 0.08 (indicating that the error of approximation of the model compared with the data was acceptable). Nested-models were compared using Δχ2 with significant changes representative of better model fit. In the case of non-significant Δχ2, the model with greater degrees of freedom (i.e. more parsimonious) was considered the superior model .
In total, 183 patients diagnosed with major depressive disorder (n = 66), bipolar disorder (n = 52) and psychosis (n = 65) were assessed at baseline. Of these, 93 patients (48 males), aged 12 to 34 years of age (M = 21.6, SD = 4.5), returned for follow-up (34 major depressive disorder, 29 bipolar disorder, and 30 psychosis). Psychiatric comorbidities were common, with additional diagnoses of social anxiety disorder (n = 15), generalised anxiety disorder (n = 8), obsessive-compulsive disorder (n = 8), borderline personality disorder (n = 4), attention-deficit/hyperactivity disorder (n = 3), and narcissistic personality disorder (n = 1). Age of onset of psychiatric symptoms was relatively young (M = 16.2, SD = 4.7), although this measure reflected the emergence of psychiatric symptoms and not the onset of full-threshold diagnoses according to DSM-IV TR.
Patients did not return for follow-up for various reasons, including those who were disinterested in participation (n = 23), not contactable or had transportation difficulties (n = 54), too busy with full-time work or study (n = 8), too ill (n = 4), or had deceased (n = 1). Compared with those who returned for follow-up, those who did not were not significantly different in terms of diagnoses, age, age of onset, years of education, depressive, positive, negative or manic symptoms, or on any neuropsychological measure (all p>0.14). However, returning patients had significantly higher premorbid IQ [104 vs. 100; F(1, 177) = 6.73, p = .01], although this no longer remained significant once outliers (n = 6) with premorbid IQ less than 80 or greater than 120 were excluded (p>.10). Nonetheless, even by including patients with outlying premorbid IQs the difference is unlikely to be clinically significant given the discrepancy of 4 IQ points (equates to a standard deviation difference of 0.27). Thus, these patients were not excluded from subsequent structural equation modelling analyses.
Cross-validating SOFAS at Follow-Up
At follow-up, a high inter-rater agreement was obtained between treating psychiatrists and research psychologists on the SOFAS measure (intraclass correlation coefficient = 0.778, p<.000) , . Using our qualitatively-defined dichotomy, 53 patients met criteria for ‘good outcome’, whereas 40 patients were classified as ‘poor outcome’ (see Table 1). Good outcome patients reported greater overall quality of life [F(1,62.2) = 6.65, p<.05], less disability [F(1,67.0) = 5.67, p<.05], were more often employed [56.6% vs. 15.0%, χ2(1) = 16.63, p<.001], in a romantic relationship [43.4% vs. 17.5%, χ2(1) = 7.00, p<.01], and less likely to be on a disability support pension [13.2% vs. 45.0%, χ2(1) = 11.72, p<.001]. The mean follow-up interval was 21.6 months (SD = 7.0; range 6–48 months), and this neither differed between outcome groups nor correlated with follow-up SOFAS score (p>.05).
Table 1. Characteristics of poor and good functional outcome groups at follow-up.doi:10.1371/journal.pone.0058176.t001
Characteristics of Outcome Groups
Age, age of onset, gender, years of education, and premorbid IQ did not differ between outcome groups (p>.05; see Table 2). Patients with good outcome were functioning better at baseline according to the SOFAS [F(1,91) = 18.70, p<.001]. There were more bipolar patients in the good outcome group (n = 23, 43.4%) relative to the poor outcome group [n = 6, 15.0%; χ2(1) = 8.57, p<.01], although by contrast manic symptoms were more elevated in the poor outcome group at baseline [F(1,52.3) = 6.74, p<.05]. Further inspection of the data suggested that this anomaly was driven by the bipolar patients in the poor outcome group who rated significantly higher on the BPRS mania scale than those in the good outcome group [F(1,27) = 15.68, p<.001]. There were more psychosis patients in the poor outcome group (n = 18, 45.0%) than the good outcome group [n = 12, 22.6%; χ2(1) = 5.22, p<.05]. There were neither significant differences between the proportion of major depression patients in the good (n = 18, 34%) compared with the poor outcome group [n = 16, 40%; χ2(1) = 0.36, p = .55], nor between the number of patients with comorbidities between the good (n = 20, 40%) and poor outcome groups [n = 19, 35.8%; χ2(1) = 1.88, p = .17]. Treatment centre was not associated with outcome (p>.05). Most patients were medicated (n = 71, 76.3%), although on average more patients in the poor outcome group were taking medication [87.5% vs. 67.0%; χ2(1) = 4.84, p<.05]. Specifically, more patients were treated with antipsychotics in the poor outcome group, consistent with its higher proportion of psychosis patients [54.0% vs. 43.4%, χ2(1) = 5.33, p<.05]. There were no differences between outcome groups on antidepressant or anticonvulsant/mood stabiliser usage (p>.44). At baseline, patients with poor outcome performed significant worse on tests of working memory [SSP; F(1,91) = 10.44, p<.005], verbal memory retention [LM-Ret; F(1,91) = 9.53, p<.005], visual memory [PAL; F(1,51.3) = 10.50, p<.005], and attentional switching [TMT-B; F(1,65.5) = 6.75, p<.05].
Table 2. Characteristics of poor and good outcome groups at baseline.doi:10.1371/journal.pone.0058176.t002
Structural Equation Modelling
The four significant neuropsychological measures identified in the current study were best explained by one latent factor (all other factors had eigenvalues <0.6), which accounted for 60% of the variance in neuropsychological performance among the four measures (SSP, LM-Ret, PAL, TMT-B). Model fit indices were good (χ2 = .67, df = 2, p = .72, CFI = 1.00, NFI = 0.99, RMSEA = 0.00, 90% CI = 0.00–0.15).
Entering only baseline SOFAS and symptoms into the structural model did not yield any models with uniformly adequate fit indices. The best possible model included depression and negative symptoms (mania did not predict SOFAS outcome despite being different between good and poor outcome groups). This model was somewhat inadequate and paths from symptoms to follow-up functioning, as measured by the SOFAS, were both non-significant (χ2 = 37.33, df = 31, p = .20, CFI = .97, NFI = .86, RMSEA = 0.05, 90% CI = 0.00–0.10). Modifying the model by including follow-up symptoms as predictors (which were in turn predicted by baseline symptoms) yielded a good fitting model with adequate fit indices (χ2 = 23.68, df = 29, p = .74, CFI = 1.00, NFI = 0.91, RMSEA = 0.00, 90% CI = 0.00–0.06). This was a significant improvement over the previous model and was thus adopted (Δχ2 = −13.65, Δdf = −2, p<.001). As the paths from baseline symptoms to follow-up functioning were still not statistically significant, we tested whether these paths were necessary. The subsequent model, removing direct paths from baseline symptoms to follow-up functioning, was an adequate fit and all paths were significant (χ2 = 27.32, df = 31, p = .66, CFI = 1.00, NFI = 0.90, RMSEA = 0.00, 90% CI = 0.00–0.07). In addition, it was not significantly different from the previous model (Δχ2 = 3.64, Δdf = 2, p = .162) and, thus, it was adopted because it was more parsimonious. Finally, the neuropsychological latent variable was entered into the model as a predictor of follow-up functioning. This model was a good fit with all paths statistically significant (χ2 = 19.00, df = 30, p = .94, CFI = 1.00, NFI = 0.93, RMSEA = 0.00, 90% CI = 0.00–0.02), fitting better than the previous model (Δχ2 = −8.32, Δdf = −1, p<.005) and, thus, was adopted as the final model since the significant increase in model fit justified the reduction in parsimony . The final model (Figure 1) explained 47.5% of follow-up functioning, with the latent baseline neuropsychological variable being the strongest predictor, accounting for 10.9% of the variance.
Figure 1. Final structural equation model.
Note: Rectangles denote measured variables. Ovals denote latent variables. Single-headed arrows represent regression paths. Two single-headed arrows pointing in opposite directions represent correlations. Residual error terms for all endogenous variables were omitted for ease of viewing. SSP = Spatial Span length. LM-Ret = Logical Memory percentage retention. PAL = Paired Associates Learning adjusted errors. TMT-B = Trail Making Test – Part B. Depressive and Negative Symptoms were measured by subscales from the expanded Brief Psychiatric Rating Scale, and Functioning was measured by the Social and Occupational Functioning Assessment Scale. aR2 = .50. bR2 = .54. cR2 = .37. dR2 = .39. *p≤.05. **p≤.01. ***p≤.001.doi:10.1371/journal.pone.0058176.g001
Since proportions of diagnoses differed between outcome groups when initially dichotomising the SOFAS scores, we decided to test whether being diagnosed with bipolar disorder or psychosis (the two diagnoses that were differentially represented) might have better accounted for the outcomes seen at follow-up. The results revealed that diagnoses did not improve model fit and, to the contrary, made the model less parsimonious (Δχ2 = −1.85, Δdf = −2, p = .40). In addition, neither the paths from bipolar disorder nor psychosis to follow-up functioning were significant (r = .07 for both predictors, p>.40), while baseline neuropsychological functioning remained the best predictor. Therefore, the final model in Figure 1 was retained.
In young psychiatric outpatients relatively early in their illness, moderate or greater functional impairment was associated with poorer quality of life, greater disability, unemployment and single relationship-status. Functional outcome was most strongly predicted by baseline neuropsychological functioning, controlling for initial level of functioning and concurrent symptoms. Baseline verbal and visuospatial memory, spatial working memory, and attentional switching were instrumental to socio-occupational functioning 6 to 48 months later, consistent with findings from more chronic cohorts, which have identified baseline memory , ,  and executive functioning – to be predictive of later functioning across our diagnostic groups. Neurobiologically, this may reflect underlying frontotemporal changes, which underpin later functional outcome early in the course of affective and psychotic illness. In contrast, baseline neuropsychological functioning was not associated with symptomatology at baseline or follow-up, in keeping with the literature . Thus, memory and executive functioning are likely independent of clinical state in early-course psychiatric illness, underscoring their potential as endophenotypes.
In contrast, baseline depressive and negative symptoms did not directly predict later functional outcome. These symptoms were, however, associated with functional outcome cross-sectionally, in line with previous findings . Therefore, symptoms and functioning do not appear to be causally linked, but instead may be outcomes of a common pathophysiology. Similarly, the utility of diagnosis in predicting functional outcome was questioned by the current findings. Diagnosis was unrelated to level of functioning over and above the effects of cognition and clinical symptoms, implying that categorising patients based on traditional classification systems is unlikely to be informative in regards to functional trajectory. Taken together, these results strongly suggest that a traditional, or sole, focus on symptom factors is inadequate in characterising prognosis and recovery. On the other hand, the longer-term outcome of young patients presenting to mental health services with a range of disorders is best predicted by their initial level of neuropsychological functioning.
The strong cross-sectional relationship currently found between neuropsychological and socio-occupational functioning contradicts previous reports in more chronic patient populations. For instance, Simonsen et al.  found that most neuropsychological measures were not singly predictive of functional outcome in a group of chronic bipolar disorder and schizophrenia patients in their mid-thirties. Moreover, overall neuropsychological and socio-occupational functioning were only modestly correlated. This discrepancy with the current data suggests that the influence of neuropsychological functioning on later functional outcome may be greater in the early stages of illness. That is, over time the impact of neuropsychological deficits on functioning may be diminished as the chronicity of disability is spontaneously maintained. Although this is speculative, it highlights the potential importance of early intervention, since interventions aimed at reducing disability may be particularly effective within this initial, critical period from psychiatric onset .
A major strength of the current study was its longitudinal design, providing stronger, directional evidence of the predictive utility of neuropsychological functioning over traditional cross-sectional approaches. Structural equation modelling also permitted a more sophisticated representation of how clinical symptoms, cognition and socio-occupational functioning are inter-related over time. In addition, the use of a naturalistic sample of patients suffering from a range of severe mental illnesses, and frequently with other psychiatric comorbidities, strengthens the ecological validity of the current findings.
Limitations and Future Directions
A number of limitations must be acknowledged. The current study was restricted by its modest sample size. As such, we were unable to explore whether the relationships between cognition and functional outcome differed according to traditional diagnostic categories using multiple-group modelling procedures . Future studies need to directly test whether diagnosis moderates the currently observed effects, as previously demonstrated cross-sectionally in more chronically ill samples . Moreover, studies should include mediators of the relationship between neuropsychological and socio-occupational functioning, which have been identified in cross-sectional studies of chronic schizophrenia and bipolar disorder patients. Indeed, there is evidence suggesting that social competence  and social cognition  mediate this relationship. Separately, the suggestion that memory and executive functioning may be potential endophenotypes of major psychiatric morbidity needs to be verified in future studies including cohorts of unaffected family members and healthy comparison individuals.
The current study was the first longitudinal study utilising structural equation modelling to demonstrate a strong link between neuropsychological and later socio-occupational functioning in young and early-course psychiatric outpatients. Memory and executive dysfunction were linked to later disability, highlighting the potential of reducing longer-term disability through early neuroprotective strategies targeting frontal and temporal brain regions. Thus, psychiatric illness may be better viewed along a neuropsychological continuum, with those on the impaired end more likely to follow a less favourable functional trajectory. A traditional and sole focus on symptoms or syndromes is unlikely to be useful in predicting functional recovery. Compared with more chronic stages, neuropsychological functioning may be a more robust predictor of later functioning in the early stages of psychiatric disease, suggesting that cognitive remediation strategies may be most efficacious in effecting functional change in this early ‘critical period’.
The authors would like to thank Ms Kristi Griffiths for her assistance with data collection, and the clinicians who referred their patients into the current study. We would also like to express our gratitude to all participants without whom the current study would not be possible.
Conceived and designed the experiments: RSCL DFH MARH SLN EMS IBH. Performed the experiments: RSCL DFH MARH MK DW EMS. Analyzed the data: RSCL DFH MARH MAP MK DW IBH. Contributed reagents/materials/analysis tools: DFH MARH SLN EMS IBH. Wrote the paper: RSCL DFH MARH SLN MAP MK DW EMS IBH.
- 1. Zimmerman M, Martinez JA, Attiullah N, Friedman M, Toba C, et al. (2012) Why do some depressed outpatients who are in remission according to the Hamilton Depression Rating Scale not consider themselves to be in remission? Journal of Clinical Psychiatry 17: 790–795. doi: 10.4088/jcp.11m07203
- 2. Fagiolini A, Kupfer DJ, Masalehdan A, Scott JA, Houck PR, et al. (2005) Functional impairment in the remission phase of bipolar disorder. Bipolar Disorders 7: 281–285. doi: 10.1111/j.1399-5618.2005.00207.x
- 3. Oorschot M, Lataster T, Thewissen V, Lardinois M, van Os J, et al. (2012) Symptomatic remission in psychosis and real-life functioning. British Journal of Psychiatry 201: 215–220. doi: 10.1192/bjp.bp.111.104414
- 4. Bowie CR, Depp C, McGrath JA, Wolyniec P, Mausbach BT, et al. (2010) Prediction of real-world functional disability in chronic mental disorders: A comparison of schizophrenia and bipolar disorder. The American Journal of Psychiatry 167: 1116–1124. doi: 10.1176/appi.ajp.2010.09101406
- 5. Withall A, Harris LM, Cumming SR (2009) The relationship between cognitive function and clinical and functional outcomes in major depressive disorder. Psychological Medicine 39: 393–402. doi: 10.1017/s0033291708003620
- 6. Martino DJ, Marengo E, Igoa A, Scapola M, Ais ED, et al. (2009) Neurocognitive and symptomatic predictors of functional outcome in bipolar disorders: a prospective 1 year follow-up study. Journal of Affective Disorders 116: 37–42. doi: 10.1016/j.jad.2008.10.023
- 7. Green MF, Kern RS, Heaton RK (2004) Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophrenia Research 72: 41–51. doi: 10.1016/j.schres.2004.09.009
- 8. Simonsen C, Sundet K, Vaskinn A, Ueland T, Romm KL, et al. (2010) Psychosocial function in schizophrenia and bipolar disorder: Relationship to neurocognition and clinical symptoms. Journal of the International Neuropsychological Society 16: 771–783. doi: 10.1017/s1355617710000573
- 9. Robbins TW, Gillan CM, Smith DG, de Wit S, Ersche KD (2012) Neurocognitive endophenotypes of impulsivity and compulsivity: towards dimensional psychiatry. Trends in Cognitive Sciences 16: 81–91. doi: 10.1016/j.tics.2011.11.009
- 10. Hermens DF, Lagopoulos J, Naismith SL, Tobias-Webb J, Hickie IB (2012) Distinct neurometabolic profiles are evident in the anterior cingulate of young people with major psychiatric disorders. Translational Psychiatry 2: e110. doi: 10.1038/tp.2012.35
- 11. Hatton SN, Lagopoulos J, Hermens DF, Naismith SL, Bennett MR, et al. (2012) Correlating anterior insula gray matter volume changes in young people with clinical and neurocognitive outcomes: an MRI study. BMC Psychiatry 12: 45. doi: 10.1186/1471-244x-12-45
- 12. Kaur M, Lagopoulos J, Ward PB, Watson TL, Naismith SL, et al. (2012) Mismatch negativity/P3a complex in young people with psychiatric disorders: A cluster analysis. PLOS ONE 7(12): e51871. doi: 10.1371/journal.pone.0051871
- 13. Häfner H, an der Heiden W, Maurer K (2008) Evidence for separate diseases? Stages of one disease or different combinations of symptom dimensions? European Archives of Psychiatry and Clinical Neuroscience 258: 85–96. doi: 10.1007/s00406-008-2011-4
- 14. Hallmayer JF, Jablensky A, Michie P, Woodbury M, Salmon B, et al. (2003) Linkage analysis of candidate regions using a composite neurocognitive phenotype correlated with schizophrenia. Molecular Psychiatry 8: 511–523. doi: 10.1038/sj.mp.4001273
- 15. Hermens DF, Redoblado Hodge MA, Naismith SL, Kaur M, Scott E, et al. (2011) Neuropsychological Clustering Highlights Cognitive Differences In Young People Presenting With Depressive Symptoms. Journal of the International Neuropsychological Society 17: 267–276. doi: 10.1017/s1355617710001566
- 16. Baca-Garcia E, Perez-Rodriguez MM, Basurte-Villamor I, Del Moral ALF, Jimenez-Arriero MA, et al. (2007) Diagnostic stability of psychiatric disorders in clinical practice. The British Journal of Psychiatry 190: 210–216. doi: 10.1192/bjp.bp.106.024026
- 17. Lee RSC, Hermens DF, Porter MA, Redoblado-Hodge MA (2012) A meta-analysis of cognitive deficits in first-episode Major Depressive Disorder. Journal of Affective Disorders 140: 113–124. doi: 10.1016/j.jad.2011.10.023
- 18. Torres IJ, DeFreitas VG, DeFreitas CM, Kauer-Sant’Anna M, Bond DJ, et al. (2010) Neurocognitive functioning in patients with bipolar I disorder recently recovered from a first manic episode. Journal of Clinical Psychiatry 71: 1234–1242. doi: 10.4088/jcp.08m04997yel
- 19. Mesholam-Gately RI, Giuliano AJ, Goff KP, Faraone SV, Seidman LJ (2009) Neurocognition in First-Episode Schizophrenia: A Meta-Analytic Review. Neuropsychology 23: 315–336. doi: 10.1037/a0014708
- 20. Allott K, Liu P, Proffitt T-M, Killackey E (2011) Cognition at illness onset as a predictor of later functional outcome in early psychosis: Systematic review and methodological critique. Schizophrenia Research 125: 221–235. doi: 10.1016/j.schres.2010.11.001
- 21. Torres IJ, DeFreitas CM, DeFreitas VG, Bond DJ, Kunz M, et al. (2011) Relationship between cognitive functioning and 6-month clinical and functional outcome in patients with first manic episode bipolar I disorder. Psychological Medicine 41: 971–982. doi: 10.1017/s0033291710001613
- 22. Lee RSC, Redoblado Hodge MA, Naismith SL, Hermens DF, Porter MA, et al. (In Press) Cognitive remediation improves memory and psychosocial functioning in first-episode psychiatric outpatients. Psychological Medicine. doi:10.1017/S0033291712002127.
- 23. Tomarken AJ, Waller NG (2005) Structural Equation Modeling: Strengths, Limitations, and Misconceptions. Annual Review of Clinical Psychology 1: 31–65. doi: 10.1146/annurev.clinpsy.1.102803.144239
- 24. Scott EM, Hermens DF, Glozier N, Naismith SL, Guastella AJ, et al. (2012) Targeted primary care-based mental health services for young Australians. Medical Journal of Australia 196: 136–140. doi: 10.5694/mja11.10481
- 25. American Psychiatric Association (2000) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV TR). Washington D.C.: American Psychiatric Association.
- 26. Scott EM, Hermens DF, Naismith SL, Guastella AJ, De Regt T, et al. (2013) Distinguishing young people with emerging bipolar disorders from those with unipolar depression. Journal of Affective Disorders 144: 208–215. doi: 10.1016/j.jad.2012.06.031
- 27. Ventura J, Green MF, Shaner A, Liberman RP (1993) Training and quality assurance with the Brief Psychiatric Rating Scale: “The drift busters”. International Journal of Methods in Psychiatric Research 3: 221–224.
- 28. Dingemans PMAJ, Linszen DH, Lenior ME, Smeets RMW (1995) Component Structured of the Expanded Brief Psychiatric Rating Scale (BPRS-E). Psychopharmacology 122: 263–267. doi: 10.1007/bf02246547
- 29. Wechsler DS (2001) Wechsler Test of Adult Reading. San Antonio, TX: The Psychological Corporation.
- 30. Wilkinson GS, Robertson GJ (2006) Wide Range Achievement Test–Fourth Edition. Lutz, FL: Psychological Assessment Resources.
- 31. Franzen MD, Paul DS, Price G (1990) Alternate form reliability of Trails A, B, C, and D. Ninth Annual Convention of the National Academy of Neuropsychology. Reno, NY.
- 32. Wechsler DS (1997) Wechsler Memory Scale - Third Edition (WMS-III). San Antonio, TX. : The Psychological Corporation.
- 33. Taylor EM (1959) Psychological Appraisal of Children with Cerebral Deficits. Cambridge, MA: Harvard University Press.
- 34. Sahakian BJ, Owen AM (1992) Computerized assessment in neuropsychiatry using CANTAB: discussion paper. Journal of the Royal Society of Medicine 85: 399–402.
- 35. Spreen O, Benton AL (1977) Neurosensory Center Comprehensive Examination for Aphasia. Victoria, BC: Neuropsychology Laboratory, University of Victoria.
- 36. Rickert P, Senior G (1998) WMS-III list learning test and the Rey auditory verbal learning test: Comparisons and Australian normative data. 4th Annual Conference of the College of Clinical Neuropsychologists. Loren, Victoria, Australia.
- 37. Tombaugh TN (1998) Normative Data for the Trail Making Test. In: Spreen O, Strauss E, editors. A compendium of neuropsychological tests: Administration, norms and commentary. 2nd ed. NY: Oxford University Press.
- 38. Tombaugh TN, Kozak J, Rees L (1999) Normative Data Stratified by Age and Education for Two Measures of Verbal Fluency: FAS and Animal Naming. Archives of Clinical Neuropsychology 14: 167–177. doi: 10.1093/arclin/14.2.167
- 39. Goldman HH, Skodol AE, Lave TR (1992) Revising axis V for DSM-IV: a review of measures of social functioning. American Journal of Psychiatry 149: 1148–1156.
- 40. Skevington SM, Lotfy M, O’Connell KA (2004) The World Health Organization’s WHOQOL-BREF quality of life assessment: Psychometric properties and results of the international field trial. A Report from the WHOQOL Group. Quality of Life Research 13: 299–310. doi: 10.1023/b:qure.0000018486.91360.00
- 41. Chwastiak LA, Von Korff M (2003) Disability in depression and back pain: Evaluation of the World Health Organization Disability Assessment Schedule (WHO DAS II) in a primary care setting. Journal of Clinical Epidemiology 56: 507–514. doi: 10.1016/s0895-4356(03)00051-9
- 42. Lin A, Wood SJ, Nelson B, Brewer WJ, Spiliotacopoulos D, et al. (2011) Neurocognitive predictors of functional outcome two to 13 years after identification as ultra-high risk for psychosis. Schizophrenia Research 132: 1–7. doi: 10.1016/j.schres.2011.06.014
- 43. Anderson JC, Gerbing DW (1988) Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin 103: 411–423. doi: 10.1037/0033-2909.103.3.411
- 44. Bentler PM (1990) Comparative Fit Indexes in Structural Models. Psychological Bulletin 107: 238–246. doi: 10.1037/0033-2909.107.2.238
- 45. Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin 88: 588–606. doi: 10.1037/0033-2909.88.3.588
- 46. Steiger JH (1980) Tests for comparing elements of a correlation matrix. Psychological Bulletin 87: 245–251. doi: 10.1037/0033-2909.87.2.245
- 47. Bentler PM, Mooijaart A (1989) Choice of Structural Model via Parsimony: A Rationale Based on Precision. Psychological Bulletin 106: 315–317. doi: 10.1037/0033-2909.106.2.315
- 48. Shrout PE, Fleiss JL (1979) Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin 86: 420–428. doi: 10.1037/0033-2909.86.2.420
- 49. Burdock EI, Fleiss JL, Hardesty AS (2006) A new view of inter-observer agreement. Personnel Psychology 16: 373–384. doi: 10.1111/j.1744-6570.1963.tb01283.x
- 50. Jaeger J, Berns S, Uzelac S, Davis-Conway S (2006) Neurocognitive deficits and disability in major depressive disorder. Psychiatry Research 145: 39–48. doi: 10.1016/j.psychres.2005.11.011
- 51. Lucas S, Redoblado-Hodge MA, Shores A, Brennan J, Harris A (2008) Predictors of outcome three years after diagnosis of first episode psychosis. Psychiatry Research 161: 11–18. doi: 10.1016/j.psychres.2007.10.004
- 52. McGorry PD, Killackey E, Yung A (2008) Early intervention in psychosis: concepts, evidence and future directions. World Psychiatry 7: 148–156.
- 53. Arbuckle JL (2011) IBM SPSS Amos 20 User’s Guide: IBM Corporation.
- 54. Bowie CR, Leung WW, Reichenberg A, McClure MM, Patterson TL, et al. (2008) Predicting Schizophrenia Patients’ Real-World Behavior with Specific Neuropsychological and Functional Capacity Measures. Biological Psychiatry 63: 505–511. doi: 10.1016/j.biopsych.2007.05.022
- 55. Addington J, Girard TA, Christensen BK, Addington D (2010) Social cognition mediates illness-related and cognitive influences on social function in patients with schizophrenia-spectrum disorders. Journal of Psychiatry & Neuroscience 35: 49–54. doi: 10.1503/jpn.080039