Two and five-factor models of negative symptoms in schizophrenia are differentially associated with trait affect, defeatist performance beliefs, and psychosocial functioning.

Recent factor analytic evidence supports both two-factor (motivation and pleasure, MAP; diminished expression, EXP) and five-factor (anhedonia, asociality, avolition, blunted affect, alogia) conceptualizations of negative symptoms. However, it is unclear whether these two conceptualizations of the latent structure of negative symptoms have differential associations with external correlates. The current study evaluated external correlates of the two- and five-factor structures by examining associations with variables known to have critical relations with negative symptoms: trait affect, defeatist performance beliefs, neurocognition, and community-based psychosocial functioning. Participants included a total of 245 outpatients diagnosed with schizophrenia who were rated on the Brief Negative Symptom Scale and completed a battery of additional measures during periods of clinical stability. These additional measures included the Positive and Negative Affect Schedule, Defeatist Performance Beliefs scale, MATRICS Consensus Cognitive Battery, and Level of Function Scale. Pearson correlations indicated differential patterns of associations between the BNSS scores and the external correlates. Support for the two-factor model was indicated by a stronger association of MAP with positive affect and psychosocial functioning, compared to EXP with neurocognition. Significance tests examining a differential magnitude of associations showed that the two-dimension negative symptom structure masked unique correlational relationships among the five negative symptom domains with neurocognition and social/vocational community functioning and captured unique patterns of correlation with trait affect. Support for the five-factor model was shown by a stronger association between Blunted Affect with Attention/Vigilance, and stronger associations between Avolition, Anhedonia, and Asociality with psychosocial functioning. Results offer support for both the two-dimension and five-domain model of negative symptoms as well as a hierarchical two-dimensions-five-domains model of negative symptoms. Findings may have implications for diagnostic criteria and descriptions of the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5), as well as possible treatment targets of negative symptoms.

Cluster analysis of negative symptoms identifies distinct negative symptom subgroups.

The heterogeneity of schizophrenia has been acknowledged for decades because of the diverse presentation of symptoms, illness course, and treatment response noted between individuals diagnosed with the disorder. Cluster analysis has been used as a statistical method to determine whether schizophrenia subgroups might be identified based on symptom heterogeneity. However, there is very limited research examining whether heterogeneity in negative symptoms might be useful in establishing schizophrenia subtypes, particularly research examining newer models of negative symptoms based on five latent constructs including anhedonia, asociality, avolition, blunted affect, and alogia. The Brief Negative Symptom Scale was used to assess the five negative symptoms domains in a sample of 220 outpatients diagnosed with schizophrenia or schizoaffective disorder. Cluster analysis supported a four-cluster solution, comprising clusters of subjects with low negative symptoms (LNS), severe negative symptoms (SNS), and two clusters with moderate negative symptoms, one with predominantly elevated blunted affect (BA) and one with elevated avolition (AV). The LNS, SNS, BA, and AV clusters significantly differed on external validators including clinical characteristics, neurocognition, and functional outcome. Findings suggest that schizophrenia heterogeneity can be parsed according to negative symptom subtypes that have distinct clinical and neuropsychological profiles. Implications for diagnosis and treatment are discussed.