Multilevel ordinal factor analysis of the positive and negative syndrome scale (PANSS)

Symptom-circuit mappings of the schizophrenia connectome

OBJECTIVE

This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology.

METHODS

T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia.

RESULTS

The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology.

CONCLUSION

We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.

Determinants of patient-reported outcome trajectories and symptomatic recovery in Improving Access to Psychological Therapies (IAPT) services

BACKGROUND

Despite evidence for the general effectiveness of psychological therapies, there exists substantial heterogeneity in patient outcomes. We aimed to identify factors associated with baseline severity of depression and anxiety symptoms, rate of symptomatic change over the course of therapy, and symptomatic recovery in a primary mental health care setting.

METHODS

Using data from a service evaluation involving 35 527 patients in England's psychological and wellbeing [Improving Access to Psychological Therapies (IAPT)] services, we applied latent growth models to explore which routinely-collected sociodemographic, clinical, and therapeutic variables were associated with baseline symptom severity and rate of symptomatic change. We used a multilevel logit model to determine variables associated with symptomatic recovery.

RESULTS

Being female, younger, more functionally impaired, and more socioeconomically disadvantaged was associated with higher baseline severity of both depression and anxiety symptoms. Being older, less functionally impaired, and having more severe baseline symptomatology was associated with more rapid improvement of both depression and anxiety symptoms (male gender and greater socioeconomic disadvantage were further associated with rate of change for depression only). Therapy intensity and appointment frequency seemed to have no correlation with rate of symptomatic improvement. Patients with lower baseline symptom severity, less functional impairment, and older age had a greater likelihood of achieving symptomatic recovery (as defined by IAPT criteria).

CONCLUSIONS

We must continue to investigate how best to tailor psychotherapeutic interventions to fit patients' needs. Patients who begin therapy with more severe depression and/or anxiety symptoms and poorer functioning merit special attention, as these characteristics may negatively impact recovery.

Dynamic interplay between life events and course of psychotic disorders: 10-year longitudinal study following first admission

BACKGROUND

Life events (LEs) are a risk factor for first onset and relapse of psychotic disorders. However, the impact of LEs on specific symptoms - namely reality distortion, disorganization, negative symptoms, depression, and mania - remains unclear. Moreover, the differential effects of negative v. positive LEs are poorly understood.

METHODS

The present study utilizes an epidemiologic cohort of patients (N = 428) ascertained at first-admission for psychosis and followed for a decade thereafter. Symptoms were assessed at 6-, 24-, 48-, and 120-month follow-ups.

RESULTS

We examined symptom change within-person and found that negative events in the previous 6 months predicted an increase in reality distortion (β = 0.07), disorganized (β = 0.07), manic (β = 0.08), and depressive symptoms (β = 0.06), and a decrease in negative symptoms (β = -0.08). Conversely, positive LEs predicted fewer reality distortion (β = -0.04), disorganized (β = -0.04), and negative (β = -0.13) symptoms, and were unrelated to mood symptoms. A between-person approach to the same hypotheses confirmed that negative LEs predicted change in all symptoms, while positive LEs predicted change only in negative symptoms. In contrast, symptoms rarely predicted future LEs.

CONCLUSIONS

These findings confirm that LEs have an effect on symptoms, and thus contribute to the burden of psychotic disorders. That LEs increase positive symptoms and decrease negative symptoms suggest at least two different mechanisms underlying the relationship between LEs and symptoms. Our findings underscore the need for increased symptom monitoring following negative LEs, as symptoms may worsen during that time.

Identifying strategies to improve PANSS based dimensional models in schizophrenia: Accounting for multilevel structure, Bayesian model and clinical staging

BACKGROUND

Dimensional approaches can decompose a construct in a set of continuous variables, improving the characterization of complex phenotypes, such as schizophrenia. However, the five-factor model of the Positive and Negative Syndrome Scale (PANSS), the most used instrument in schizophrenia research, yielded poor fits in most confirmatory factor analysis (CFA) studies, raising concerns about its applications. Thus, we aimed to identify dimensional PANSS CFA models with good psychometric properties by comparing the traditional CFA with three methodological approaches: Bayesian CFA, multilevel modeling, and Multiple Indicators Multiple Causes (MIMIC) modeling.

METHODS

Clinical data of 700 schizophrenia patients from four centers were analyzed. We first performed a traditional CFA. Next, we tested the three techniques: 1) a Bayesian CFA; 2) a multilevel analysis using the centers as level; and 3) a MIMIC modeling to evaluate the impact of clinical staging on PANSS factors and items.

RESULTS

CFA and Bayesian CFA produced poor fit models. However, when adding a multilevel structure to the CFA model, a good fit model emerged. MIMIC modeling yielded significant differences in the factor structure between the clinical stages of schizophrenia. Sex, age, age of onset, and duration of illness did not significantly affect the model fit.

CONCLUSION

Our comparison of different CFA methods highlights the need for multilevel structure to achieve a good fit model and the potential utility of staging models (rather than the duration of illness) to deal with clinical heterogeneity in schizophrenia. Large prospective samples with biological data should help to understand the interplay between psychometrics concerns and neurobiology research.

M. Staging of Schizophrenia With the Use of PANSS: An International Multi-Center Study

INTRODUCTION

A specific clinically relevant staging model for schizophrenia has not yet been developed. The aim of the current study was to evaluate the factor structure of the PANSS and develop such a staging method.

METHODS

Twenty-nine centers from 25 countries contributed 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Analysis of covariance, Exploratory Factor Analysis, Discriminant Function Analysis, and inspection of resultant plots were performed.

RESULTS

Exploratory Factor Analysis returned 5 factors explaining 59% of the variance (positive, negative, excitement/hostility, depression/anxiety, and neurocognition). The staging model included 4 main stages with substages that were predominantly characterized by a single domain of symptoms (stage 1: positive; stages 2a and 2b: excitement/hostility; stage 3a and 3b: depression/anxiety; stage 4a and 4b: neurocognition). There were no differences between sexes. The Discriminant Function Analysis developed an algorithm that correctly classified >85% of patients.

DISCUSSION

This study elaborates a 5-factor solution and a clinical staging method for patients with schizophrenia. It is the largest study to address these issues among patients who are more likely to remain affiliated with mental health services for prolonged periods of time.

Meta-analysis of the positive and Negative Syndrome Scale (PANSS) factor structure

A meta-analysis of the results of 45 factor analyses (n = 22,812) of the Positive and Negative Syndrome Scale (PANSS) was conducted. Meta-analyses of the PANSS was conducted using both a co-occurrence similarity matrix and reproduced correlations. Both methods produced similar results. Five factors (Positive Symptoms, Negative Symptoms, Disorganization, Affect and Resistance) emerged clearly across both analyses. The factors and the items defining them were Positive Symptoms (P1 Delusions, G9 Unusual thought content, P3 Hallucinatory behavior, P6 Suspiciousness and persecution, P5 Grandiosity), Negative Symptoms (N2 Emotional withdrawal, N1 Blunted affect, N4 Passive apathetic social withdrawal, N6 Lack of spontaneity, N3 Poor rapport, G7 Motor retardation, G16 Active social avoidance), Disorganization often termed Cognitive (P2 Conceptual disorganization, G11 Poor attention, N5 Difficulty in abstract thinking, G13 Disturbance of volition, N7 Stereotyped thinking, G5 Mannerisms/posturing, G15 Preoccupation, G10 Disorientation), Affect often termed Depression-Anxiety (G2 Anxiety, G6 Depression, G3 Guilt feelings, G4 Tension, G1 Somatic concern) and a small fifth factor that might be characterized as Resistance or Excitement/Activity (P7 Hostility, G14 Poor impulse control, P4 Excitement, G8 Uncooperativeness). Items G1, G4, G10, P5, G5, G15 may not be core items for the PANSS factors and G12 lack of judgment is not a core item. Results of the PANSS meta-analyses were relatively similar to those for meta-analysis of both the BPRS and BPRS-E all of which contain the original 18 BPRS items. The PANSS is distinguished by a much larger number of items to clearly define and measure Negative Symptoms as well as a sufficient number of items to much more clearly identify a Disorganization factor than the BPRS or BPRS-E.

The psychometrics of psychosis - assessing and rating perceptual and ideational disturbance in adolescents

BACKGROUND

The early recognition and management of psychosis spectrum disorders is associated with superior outcomes in affected individuals. However, this can be challenging for numerous reasons. This article provides perspectives on the effective evaluation and rating of potentially psychotic phenomena young people. We compare and contrast two widely used instruments that can support this process.

FINDINGS

The Comprehensive Assessment for At-Risk Mental States (CAARMS) is used to explore potentially psychotic experiences in young people perceived to be at risk of an emerging or imminent psychosis. There is evidence to support its reliability and, to some extent, the predictive validity of the resultant scores. However, relatively low short-medium transition rates to psychosis in 'positive' cases suggest that its use as a screening instrument should be restricted to groups who show some indication of impending risk (e.g. help-seeking, distress, declining functioning, perceptual disturbance, etc.). In contrast, the Positive and Negative Syndrome Scale (PANSS) is calibrated to rate symptoms in those with an established psychosis, especially those with a diagnosis related to the schizophrenia spectrum. Consequently, the PANSS is useful for evaluating the clinical course and outcomes of psychotic illness.

CONCLUSIONS

Although neither instrument is designed specifically for use in those under 18, with care they can be used to effectively support the management of adolescents reporting perceptual and ideational disturbance. However, it is important that any instrument ratings are placed meaningfully in the context of the overall clinical picture and all available information.

Factor analysis of symptom dimensions (psychotic, affective and obsessive compulsive symptoms) in schizophrenia

AIM

To carryout factor analysis of symptom profile of patients with schizophrenia assessed by using positive and negative syndrome scale (PANSS), Calgary depression rating scale (CDSS), Young mania rating scale (YMRS) and YBOCS checklist.

METHODOLOGY

181 patients of schizophrenia were assessed on PANSS, CDSS, YMRS and YBOCS checklist.

RESULTS

Factor analysis of PANSS yielded 3 factor structure (positive, negative, anxiety). When the items of CDSS were added to the PANSS items, total number of factors increased to 4 with depression emerging as a separate factor. Addition of YMRS to PANSS and CDSS led to emergence of 5 factor model. Further addition of YBOCS checklist led to emergence of a 7 factor model (positive, depressive, obsessive compulsive, negative, manic, anxiety and obsessive compulsive-2), which explained 49.85% variance of the data. Positive symptoms emerged as the factor-1. Depressive symptoms loaded on the factor-2, negative symptoms loaded on to factor-4, manic symptoms loaded onto factor-5 and anxiety symptoms loaded onto factor-6. OC symptoms loaded onto factor 3 and 7.

CONCLUSIONS

Present study suggests that when multiple scales are used for assessment of various symptoms of schizophrenia, the symptoms separate out into 7 factors. This finding suggests that clinical assessment of schizophrenia should not be limited to core psychotic symptoms only and structured instruments must be used to elicit other symptoms too while monitoring the clinical picture of patients with schizophrenia.

The Brief Negative Symptom Scale: Validation in a multicenter Brazilian study

BACKGROUND

Negative symptoms are a core feature of schizophrenia. The Brief Negative Symptom Scale (BNSS) is a scale developed to measure negative symptoms in schizophrenia.

METHODS

The present study aimed to examine the construct validity of BNSS, by using convergent and divergent validities as well as factor analysis, in a Brazilian sample of 111 outpatients diagnosed with schizophrenia by DSM-5. Patients were evaluated by the Brazilian version of the BNSS and positive and negative subscales of the Positive and Negative Syndrome Scale (PANSS).

RESULTS

Assessment of patients by both instruments revealed an excellent internal consistency (Cronbach's alpha = 0.938) or inter-rater reliability (ICC = 0.92), as well as a strong correlation between BNSS and Marder negative PANSS (r = 0.866) and a weak correlation of the instrument with the positive PANSS (r = 0.292), thus characterizing convergent and discriminant validities, respectively. The exploratory factor analysis identified two distinct factors, namely, motivation/pleasure and emotional expressivity, accounting for 68.63% of the total variance.

CONCLUSION

The study shows that the Brazilian version of the BNSS has adequate psychometric properties and is a reliable instrument for the assessment of negative symptoms in schizophrenia, either for clinical practice or research.

Effects of sample size and distributional assumptions on competing models of the factor structure of the PANSS and BPRS

Factor analytic work on the Positive and Negative Syndrome Scale (PANSS) and Brief Psychiatric Rating Scale (BPRS) has yielded varied and conflicting results. The current study explored potential causes of these discrepancies. Prior research has been limited by small sample sizes and an incorrect assumption that the items are normally distributed when in practice responses are highly skewed ordinal variables. Using simulation methodology, we examined the effects of sample size, (in)correctly specifying item distributions, collapsing rarely endorsed response categories, and four factor analytic models. The first is the model of Van Dorn et al., developed using a large integrated data set, specified the item distributions as multinomial, and used cross-validation. The remaining models were developed specifying item distributions as normal: the commonly used pentagonal model of White et al.; the model of Van der Gaag et al. developed using extensive cross-validation methods; and the model of Shafer developed through meta-analysis. Our simulation results indicated that incorrectly assuming normality led to biases in model fit and factor structure, especially for small sample size. Collapsing rarely used response options had negligible effects.

Could PANSS be a useful tool in the determining of the stages of schizophrenia? A clinically operational approach

Staging in schizophrenia might be an important approach for the better treatment and rehabilitation of patients. The purpose of this study was to empirically devise a staging approach in a sample of stabilized patients with schizophrenia. One hundred and seventy patients aged ≥18 years (mean = 40.7, SD = 11.6) diagnosed by DSM-5 criteria were evaluated with the Positive and Negative Syndrome Scale (PANSS). Principal components analysis (PCA) with varimax rotation was used. The model was examined in the total sample and separately across a hypothesized stage of illness based on three age groups and between the two sexes. The PCA revealed a six factor structure for the total sample: 1) Negative, 2) Positive, 3) Depression and anxiety, 4) Excitement and Hostility, 5) Neurocognition and 6) Disorganization. The separate PCAs by stage of illness and sex revealed different patterns and quality of symptomatology. The Negative and Positive factors were stable across all examined groups. The models corresponding to different stages differed mainly in terms of neurocognition and disorganization and their interplay. Catatonic features appear more prominent in males while in females neurocognition takes two forms; one with disorganization and one with stereotype thinking with delusions. This study suggests that the three arbitrary defined stages of illness (on the basis of age) seem to reflect a progress from a preserved insight and more coherent mental functioning to disorganization and eventually neurocognitive impairment. Sexes differ in terms of the relationship of psychotic features with neurocognition. These results might have significant research and clinical implications.

Effects of ignoring clustered data structure in confirmatory factor analysis of ordered polytomous items: a simulation study based on PANSS

Statistical theory indicates that hierarchical clustering by interviewers or raters needs to be considered to avoid incorrect inferences when performing any analyses including regression, factor analysis (FA) or item response theory (IRT) modelling of binary or ordinal data. We use simulated Positive and Negative Syndrome Scale (PANSS) data to show the consequences (in terms of bias, variance and mean square error) of using an analysis ignoring clustering on confirmatory factor analysis (CFA) estimates. Our investigation includes the performance of different estimators, such as maximum likelihood, weighted least squares and Markov Chain Monte Carlo (MCMC). Our simulation results suggest that ignoring clustering may lead to serious bias of the estimated factor loadings, item thresholds, and corresponding standard errors in CFAs for ordinal item response data typical of that commonly encountered in psychiatric research. In addition, fit indices tend to show a poor fit for the hypothesized structural model. MCMC estimation may be more robust against clustering than maximum likelihood and weighted least squares approaches but further investigation of these issues is warranted in future simulation studies of other datasets. Copyright © 2015 John Wiley & Sons, Ltd.