What's in an item? Predicting social outcomes in schizophrenia spectrum disorders from the PANSS item "Poor Rapport"

Socio-Motor Improvisation in Schizophrenia: A Case-Control Study in a Sample of Stable Patients

Improvising is essential for human development and is one of the most important characteristics of being human. However, how mental illness affects improvisation remains largely unknown. In this study we focused on socio-motor improvisation in individuals with schizophrenia, one of the more debilitating mental disorder. This represents the ability to improvise gestures during an interaction to promote sustained communication and shared attention. Using a novel paradigm called the mirror game and recently introduced to study joint improvisation, we recorded hand motions of two people mirroring each other. Comparing Schizophrenia patients and healthy controls skills during the game, we found that improvisation was impaired in schizophrenia patients. Patients also exhibited significantly higher difficulties to being synchronized with someone they follow but not when they were leaders of the joint improvisation game. Considering the correlation between socio-motor synchronization and socio-motor improvisation, these results suggest that synchronization does not only promote affiliation but also improvisation, being therefore an interesting key factor to enhance social skills in a clinical context. Moreover, socio-motor improvisation abnormalities were not associated with executive functioning, one traditional underpinning of improvisation. Altogether, our results suggest that even if both mental illness and improvisation differ from normal thinking and behavior, they are not two sides of the same coin, providing a direct evidence that being able to improvise in individual situations is fundamentally different than being able to improvise in a social context.

The relationship between the therapeutic alliance and client variables in individual treatment for schizophrenia spectrum disorders and early psychosis: Narrative review

Given the high rates of treatment disengagement and medication nonadherence in individuals with schizophrenia spectrum disorders and early psychosis, fostering a strong alliance in treatment is critical. Moreover, the role of the therapeutic alliance extends beyond that in traditional psychotherapy because of the multifaceted nature of treatment. Thus, this review provides a comprehensive discussion of the relationship between the alliance and client variables across various provider types and individual treatments. This review summarizes existing research on (a) client correlates/predictors of the therapeutic alliance and on (b) the relationship between the alliance and client treatment outcomes in individual treatment for schizophrenia spectrum disorders and early psychosis. Parallel literature searches were conducted using PubMed and PsycINFO databases, which yielded 1202 potential studies with 84 studies meeting inclusion criteria. With regard to correlates/predictors, the existing evidence suggests that better insight, medication adherence, social support, and recovery variables were related to better client-rated alliance. Better medication adherence and recovery variables as well as less severe symptoms were related to better provider-rated alliance. In terms of alliance-outcome relationships, evidence suggests that a strong provider-rated alliance was predictive of improved functioning and medication and treatment adherence. There was some limited evidence that better client-rated alliance was related to improved recovery outcomes. Despite mixed results and heterogeneity among studies, this review suggests that a strong alliance can be beneficial in individual schizophrenia treatment. Thus, training and supervision of providers should emphasize developing a positive alliance, particularly with clients for whom developing an alliance may be difficult.

The association of psychopathology with concurrent level of functioning and subjective well-being in persons with schizophrenia spectrum disorders

The objective is to investigate the relationship between psychopathology measured by the positive and negative syndrome scale (PANSS) and concurrent global assessment of functioning (GAF) and subjective well-being under neuroleptics (SWN) in patients with schizophrenia spectrum disorder (SSD) regarding severity of illness and disease phase. We analyzed a sample of 202 SSD patients consisting of first episode psychosis (FEP) and multiple episode psychosis (MEP) patients followed up to 12 months using linear mixed models. All PANSS syndromes except excitement were associated with GAF scores (positive syndrome: p < 0.001, d = 1.21; negative syndrome: p = 0.029, d = 0.015; disorganized syndrome: p < 0.001, d = 0.37; anxiety/depression syndrome: p < 0.001, d = 0.49), and positive symptoms had an increasing impact on global functioning with higher severity of illness (mildly ill: p = 0.039, d = 0.22; moderately ill: p < 0.001, d = 0.28; severely ill: p < 0.001, d = 0.69). SWN was associated with positive (p = 0.002, d = 0.22) and anxiety/depression (p < 0.001, d = 0.38) syndromes. Subgroup analyses showed differing patterns depending on illness severity and phase. Over all our results show different patterns of associations of psychopathology and concurrent functioning and subjective well-being. These findings contribute knowledge on the possible role of specific psychopathological syndromes for the functioning and well-being of our patients and may enable tailored treatments depending on severity and phase of illness.

The specific social costs of expressive negative symptoms in schizophrenia: reduced smiling predicts interactional outcome

OBJECTIVE

We tested whether people with schizophrenia and prominent expressive negative symptoms (ENS) show reduced facial expressions in face-to-face social interactions and whether this expressive reduction explains negative social evaluations of these persons.

METHOD

We compared participants with schizophrenia with high ENS (n = 18) with participants with schizophrenia with low ENS (n = 30) and with healthy controls (n = 39). Participants engaged in an affiliative role-play that was coded for the frequency of positive and negative facial expression and rated for social performance skills and willingness for future interactions with the respective role-play partner.

RESULTS

Participants with schizophrenia with high ENS showed significantly fewer positive facial expressions than those with low ENS and controls and were also rated significantly lower on social performance skills and willingness for future interactions. Participants with schizophrenia with low ENS did not differ from controls on these measures. The group difference in willingness for future interactions was significantly and independently mediated by the reduced positive facial expressions and social performance skills.

CONCLUSION

Reduced facial expressiveness in schizophrenia is specifically related to ENS and has negative social consequences. These findings highlight the need to develop aetiological models and targeted interventions for ENS and its social consequences.

Identifying Resting-State Multifrequency Biomarkers via Tree-Guided Group Sparse Learning for Schizophrenia Classification

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional-magnetic-resonance-imaging-based schizophrenia diagnosis. How-ever, previous studies usually measure the fALFF with specific bands from 0.01 to 0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. In addition, fALFF data are intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multifrequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multifrequency bands (i.e., slow-5: 0.01-0.027 Hz, slow-4: 0.027-0.073 Hz, slow-3: 0.073-0.198 Hz, and slow-2: 0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches and select those significant patches related to schizophrenia using random forest-based important score. Moreover, we use tree-structured sparse learning method for feature selection with the above patch spatial constraint. Finally, considering biomarkers from multifrequency bands can reflect complementary information among multiple-frequency bands, we adopt the multikernel learning method to combine features of multifrequency bands for classification. Our experimental results show that these biomarkers from multifrequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating that the multifrequency bands analysis can better account for classification of schizophrenia.

Using Functional Analysis as a Framework to Guide Individualized Treatment for Negative Symptoms

Although numerous interventions are available for negative symptoms, outcomes have been unsatisfactory with pharmacological and psychological interventions producing changes of only limited clinical significance. Here, we argue that because negative symptoms occur as a complex syndrome caused and maintained by numerous factors that vary between individuals they are unlikely to be treated effectively by the present "one size fits all" approaches. Instead, a well-founded selection of those interventions relevant to each individual is needed to optimize both the efficiency and the efficacy of existing approaches. The concept of functional analysis (FA) can be used to structure existing knowledge so that it can guide individualized treatment planning. FA is based on stimulus-response learning mechanisms taking into account the characteristics of the organism that contribute to the responses, their consequences and the contingency with which consequences are tied to the response. FA can thus be flexibly applied to the level of individual patients to understand the factors causing and maintaining negative symptoms and derive suitable interventions. In this article we will briefly introduce the concept of FA and demonstrate-exemplarily-how known psychological and biological correlates of negative symptoms can be incorporated into its framework. We then outline the framework's implications for individual assessment and treatment. Following the logic of FA, we argue that a detailed assessment is needed to identify the key factors causing or maintaining negative symptoms for each individual patient. Interventions can then be selected according to their likelihood of changing these key factors and need to take interactions between different factors into account. Supplementary case vignettes exemplify the usefulness of functional analysis for individual treatment planning. Finally, we discuss and point to avenues for future research guided by this model.

Social consequences of subclinical negative symptoms: An EMG study of facial expressions within a social interaction

BACKGROUND AND OBJECTIVES

The negative symptoms of schizophrenia are related to lower social functioning even in non-clinical samples, but little is known about the distinct social consequences of motivational and expressive negative symptoms. In this study we focused on expressive negative symptoms and examined how these symptoms and varying degrees of pro-social facial expressiveness (smiling and mimicry of smiling) relate to the social evaluations by face-to-face interaction partners and to social support.

METHODS

We examined 30 dyadic interactions within a sample of non-clinical participants (N = 60) who were rated on motivational and expressive negative symptoms with the Clinical Assessment Interview for Negative Symptoms (CAINS). We collected data on both interaction partners' smiling-muscle (zygomaticus major) activation simultaneously with electromyography and assessed the general amount of smiling and the synchrony of smiling muscle activations between interaction partners (mimicry of smiling). Interaction partners rated their willingness for future interactions with each other after the interactions.

RESULTS

Interaction partners of participants scoring higher on expressive negative symptoms expressed less willingness for future interactions with these participants (r = -0.37; p = 0.01). Smiling behavior was negatively related to expressive negative symptoms but also explained by motivational negative symptoms. Mimicry of smiling and both negative symptom domains were also associated with participants' satisfaction with their social support network.

LIMITATIONS

Non-clinical sample with (relatively) low levels of symptoms.

CONCLUSIONS

Expressive negative symptoms have tangible negative interpersonal consequences and directly relate to diminished pro-social behavior and social support, even in non-clinical samples.