Network analysis of psychopathy in forensic patients.

Abstract

The question of which features represent the most central components of psychopathy remains unresolved and is the subject of considerable debate. Network analysis, which is a relatively new way to conceptualize mental disorders that emphasizes complex causal systems, provides a means to graphically and quantitatively describe the centrality of the various symptoms of a disorder. We applied association and adaptive LASSO networks on two samples of forensic patients. The first sample included forensic inpatients (N = 277) who were administered the Psychopathy Checklist-Revised (Hare, 2003), and the second sample included patients who previously had been civilly committed (N = 1136), who were administered the Psychopathy Checklist: Screening Version (Hart, Cox, & Hare, 1995). The models indicated the items on the affective facet are highly central across both samples and methods, and the item "lack of remorse" was especially central to the networks. Conversely, interpersonal, lifestyle, and antisocial facets generally resulted in low centrality in the models of both samples. Thus, the models lend support to the importance of affective deficits as the primary feature of psychopathy when psychopathy is assessed using the Hare Psychopathy Checklist measures. (PsycINFO Database Record

Longitudinal Network Structure and Changes of Clinical Risk and Protective Factors in a Nationwide Sample of Forensic Psychiatric Patients

“…By analyzing the network of interconnections at the time of admission and unconditional release, it can be made transparent how these factors influence each other and which of these activated factors are the most central and influenceable. In network analysis, risk and protective factors are represented as nodes and the associations between pairs of risk and protective factors are depicted as edges ( Borsboom & Cramer, 2013 ; Preszler et al, 2018 ).…”

“…To our knowledge, no studies on network associations and configuration of risk and protective factors in forensic psychiatric patients have been published so far. Some recent forensic studies have conducted research on network configurations in psychopathy, investigating the structure of core symptoms of psychopathic personality disturbance ( McCuish et al, 2019 ; Preszler et al, 2018 ; Verschuere et al, 2018 ).…”

Psychopathy in a forensic sample – the factor structure of the PCL:SV in a Danish forensic sample

“…Based on a Danish sample, this suggests that psychopathic personality disorder may best be conceptualized around the core features of Interpersonal, Affective and Lifestyle components. These findings are in accordance with studies specifically exploring the PCL:SV applying network analysis, where findings showed that it was specifically the affective facet that were central to psychopathy, and that, interpersonal, lifestyle, and antisocial facets generally showed low centrality (Preszler et al ., ). Thus, the results using the PCL:SV lend support to the importance of affective deficits as the primary feature of psychopathy.…”

“…Recently, it has been suggested to leave out the criminal behavior factor of the PCL‐R when utilizing this scale to assess psychopathy in clinical practice (Cooke & Sellbom, ; Preszler, Marcus, Edens & McDermott, ; Skeem & Cooke, 2010a). Excluding criminal behavior may support the understanding of the relationship, and particularly a possible functional link, between psychopathy and criminal behavior.…”

Psychopathic Personality Disorder

“…(Richters 1997, 206-207) Simple CFA models represent a mismatch between method and the problem to be tackled. Space precludes detailed discussion, however, there are a growing number of techniques that endeavour to model these complexities including exploratory structural equation modelling (ESEM; Cooke and Sellbom in press) and network analyses (Preszler et al 2018;Verschuere et al 2018). Within ESEM approaches the Independent Clusters Model is eschewed and symptoms are modelled so that they may be underpinned by more than latent factor.…”