The Important Role of Motivation and Pleasure Deficits on Social Functioning in Patients With Schizophrenia: A Network Analysis

Abstract

Negative symptoms, particularly the motivation and pleasure (MAP) deficits, are associated with impaired social functioning in patients with schizophrenia (SCZ). However, previous studies seldom examined the role of the MAP on social functioning while accounting for the complex interplay between other psychopathology. This network analysis study examined the network structure and interrelationship between negative symptoms (at the “symptom-dimension” and “symptom-item” levels), other psychopathology and social functioning in a sample of 269 patients with SCZ. The psychopathological symptoms were assessed using the Clinical Assessment Interview for Negative Symptoms (CAINS) and the Positive and Negative Syndrome Scale (PANSS). Social functioning was evaluated using the Social and Occupational Functioning Assessment Scale (SOFAS). Centrality indices and relative importance of each node were estimated. The network structures between male and female participants were compared. Our resultant networks at both the “symptom-dimension” and the “symptom-item” levels suggested that the MAP factor/its individual items were closely related to social functioning in SCZ patients, after controlling for the complex interplay between other nodes. Relative importance analysis showed that MAP factor accounted for the largest proportion of variance of social functioning. This study is among the few which used network analysis and the CAINS to examine the interrelationship between negative symptoms and social functioning. Our findings supported the pivotal role of the MAP factor to determine SCZ patients’ social functioning, and as a potential intervention target for improving functional outcomes of SCZ.

Linking social motivation, general motivation, and social cognition to interpersonal functioning in schizophrenia: insights from exploratory graph analysis

“…The importance of social withdrawal has been supported by recent network analytic studies linking motivation or negative symptoms to social functioning. The avolition-apathy factor was strongly linked to social functioning in individuals with schizophrenia and accounted for most of the variance [44], with similar results in patients with the first episode of psychosis [45]. Farina et al [46] found that low social approach motivation has stronger impact on the functioning than heightened social avoidance.…”

Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings

“…In the last few years, interpersonal tasks, large-scale neuroimaging, and computational models have been used to investigate the mechanisms of social and interpersonal functions in healthy populations; however, a synthesis of these approaches in clinical psychiatric cohorts is lacking despite many studies on social functioning in PSZ (Schwarz et al 2020;Durand et al 2021;Hu et al 2022). Computational methods in schizophrenia have mostly been generative models based on the Bayesian predictive coding framework-for example, the hierarchical Gaussian filtering approach (Powers et al 2017;Charlton et al 2022;Sheffield et al 2022), Markov decision process (ie, reinforcement learning) (Pratt et al 2021;Geana et al 2022), and an active inference framework that combines these and other models but describes unobservable mechanistic variables based on actions taken by an agent to promote desired outcomes (Friston et al 2016).…”

“…Such multi-modal integration has been noticed and highlighted in our previous perspective (Schilbach, 2016 ). However, a synthesis of these approaches in empirical studies on clinical psychiatric cohorts remains to be conducted, despite existing studies on social functioning in PSZ (Schwarz et al ., 2020 ; Durand et al ., 2021 ; Hu et al ., 2022 ). Computational methods in schizophrenia have mostly been generative models based on the Bayesian predictive coding framework—for example, the hierarchical Gaussian filtering approach (Powers et al ., 2017 ; Henco et al ., 2020 ; Charlton et al ., 2022 ; Sheffield et al ., 2022 ), reinforcement learning (Pratt et al ., 2021 ; Geana et al ., 2022 ), and the active inference Markov decision process model that attempts to dissect unobservable mechanistic variables based on actions taken by an agent to promote desired outcomes (Friston et al ., 2016 ).…”

Patient Functioning and Life Engagement

“…As shown in a network analysis, motivation and pleasure have pivotal roles in determining social functioning and are intervention targets for improving functional outcomes. 25 The roundtable participants also cited cognition as an area of overlap affecting both life engagement and functional (eg, psychosocial, occupational) outcomes. 26 The relationship between life engagement and functional improvement was summarized as being bidirectional in nature: the more a patient engages-is alert, has energy, has interest in social activities-the more functional they become, and the more functional the patient becomes, the more opportunities they have to engage.…”