Heterogeneity matters: implications for Poeppl et al.'s (2019) meta-analysis and future neuroimaging research on psychopathy

The amygdala volume moderates the relationship between childhood maltreatment and callous-unemotional traits in adolescents with conduct disorder

CU traits, characterized by shallow affect, lack of fear, and absence of remorse, have been moderately associated with childhood maltreatment in a recent meta-analysis. However, the potential impact of brain structures remains undetermined. This paper examines the relationship between callous-unemotional (CU) traits, childhood maltreatment, and amygdala volumes. In this study, we used a region-of-interest (ROI) analysis to explore the interaction between the volumes of the amygdala, childhood maltreatment, and the manifestation of CU traits in adolescents diagnosed with conduct disorder (CD, N = 67), along with a comparison group of healthy-control youths (HCs, N = 89). The ROI analysis revealed no significant group differences in the bilateral amygdalar volumes. Significant positive correlation was discovered between all forms of child maltreatment (except for physical neglect) and CU traits across subjects. But the interaction of physical abuse and amygdala volumes was only significant within CD patients. Notably, a sensitivity analysis suggested that gender significantly influences these findings. These results contribute critical insights into the etiology of CU traits, emphasizing the need for customized clinical assessment tools and intervention strategies.

Focusing Narrowly on Model Fit in Factor Analysis Can Mask Construct Heterogeneity and Model Misspecification: Applied Demonstrations across Sample and Assessment Types

This study builds upon research indicating that focusing narrowly on model fit when evaluating factor analytic models can lead to problematic inferences regarding the nature of item sets, as well as how models should be applied to inform measure development and validation. To advance research in this area, we present concrete examples relevant to researchers in clinical, personality, and related subfields highlighting two specific scenarios when an overreliance on model fit may be problematic. Specifically, we present data analytic examples showing that focusing narrowly on model fit may lead to (a) incorrect conclusions that heterogeneous item sets reflect narrower homogeneous constructs and (b) the retention of potentially problematic items when developing assessment measures. We use both interview data from adult outpatients (N = 2,149) and self-report data from adults recruited online (N = 547) to demonstrate the importance of these issues across sample types and assessment methods. Following demonstrations with these data, we make recommendations focusing on how other model characteristics (e.g., factor loading patterns; carefully considering the content and nature of factor indicators) should be considered in addition to information provided by model fit indices when evaluating factor analytic models.

Psychopathic and autistic traits differentially influence the neural mechanisms of social cognition from communication signals

Psychopathy is associated with severe deviations in social behavior and cognition. While previous research described such cognitive and neural alterations in the processing of rather specific social information from human expressions, some open questions remain concerning central and differential neurocognitive deficits underlying psychopathic behavior. Here we investigated three rather unexplored factors to explain these deficits, first, by assessing psychopathy subtypes in social cognition, second, by investigating the discrimination of social communication sounds (speech, non-speech) from other non-social sounds, and third, by determining the neural overlap in social cognition impairments with autistic traits, given potential common deficits in the processing of communicative voice signals. The study was exploratory with a focus on how psychopathic and autistic traits differentially influence the function of social cognitive and affective brain networks in response to social voice stimuli. We used a parametric data analysis approach from a sample of 113 participants (47 male, 66 female) with ages ranging between 18 and 40 years (mean 25.59, SD 4.79). Our data revealed four important findings. First, we found a phenotypical overlap between secondary but not primary psychopathy with autistic traits. Second, primary psychopathy showed various neural deficits in neural voice processing nodes (speech, non-speech voices) and in brain systems for social cognition (mirroring, mentalizing, empathy, emotional contagion). Primary psychopathy also showed deficits in the basal ganglia (BG) system that seems specific to the social decoding of communicative voice signals. Third, neural deviations in secondary psychopathy were restricted to social mirroring and mentalizing impairments, but with additional and so far undescribed deficits at the level of auditory sensory processing, potentially concerning deficits in ventral auditory stream mechanisms (auditory object identification). Fourth, high autistic traits also revealed neural deviations in sensory cortices, but rather in the dorsal auditory processing streams (communicative context encoding). Taken together, social cognition of voice signals shows considerable deviations in psychopathy, with differential and newly described deficits in the BG system in primary psychopathy and at the neural level of sensory processing in secondary psychopathy. These deficits seem especially triggered during the social cognition from vocal communication signals.

Reduced Multivoxel Pattern Similarity of Vicarious Neural Pain Responses in Psychopathy

Psychopathy is a personality construct characterized by interpersonal callousness, boldness, and disinhibition, traits that vary continuously across the population and are linked to impaired empathic responding to others' distress and suffering. Following suggestions that empathy reflects neural self-other mapping-for example, the similarity of neural responses to experienced and observed pain, measurable at the voxel level-we used a multivoxel approach to assess associations between psychopathy and empathic neural responses to pain. During fMRI scanning, 21 community-recruited participants varying in psychopathy experienced painful pressure stimulation and watched a live video of a stranger undergoing the same stimulation. As total psychopathy, coldheartedness, and self-centered impulsivity increased, multivoxel similarity of vicarious and experienced pain in the left anterior insula decreased, effects that were not observed following an empathy prompt. Our data provide preliminary evidence that psychopathy is characterized by disrupted spontaneous empathic representations of others' pain that may be reduced by instructions to empathize.

Connections that characterize callousness: Affective features of psychopathy are associated with personalized patterns of resting-state network connectivity

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There was significant heterogeneity in participants’ neural networks.

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Psychopathy associated with default mode-central executive network connectivity.

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Associations were specific to affective psychopathic traits.

Background

Psychopathic traits are hypothesized to be associated with dysfunction across three resting-state networks: the default mode (DMN), salience (SN), and central executive (CEN). Past work has not considered heterogeneity in the neural networks of individuals who display psychopathic traits, which is likely critical in understanding the etiology of psychopathy and could underlie different symptom presentations. Thus, this study maps person-specific resting state networks and links connectivity patterns to features of psychopathy.

Methods

We examined resting-state functional connectivity among eight regions of interest in the DMN, SN, and CEN using a person-specific, sparse network mapping approach (Group Iterative Multiple Model Estimation) in a community sample of 22-year-old men from low-income, urban families (N = 123). Associations were examined between a dimensional measure of psychopathic traits and network density (i.e., number of connections within and between networks).

Results

There was significant heterogeneity in neural networks of participants, which were characterized by person-specific connections and no common connections across the sample. Psychopathic traits, particularly affective traits, were associated with connection density between the DMN and CEN, such that greater density was associated with elevated psychopathic traits.

Discussion

Findings emphasize that neural networks underlying psychopathy are highly individualized. However, individuals with high levels of psychopathic traits had increased density in connections between the DMN and CEN, networks that have been linked with self-referential thinking and executive functioning. Taken together, the results highlight the utility of person-specific approaches in modeling neural networks underlying psychopathic traits, which could ultimately inform personalized prevention and intervention strategies.