Callousness, exploitativeness, and tracking of cooperation incentives in the human default network.

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Striatal dopamine supports reward expectation and learning: A simultaneous PET/fMRI study.

Converging evidence from both human neuroimaging and animal studies has supported a model of mesolimbic processing underlying reward learning behaviors, based on the computation of reward prediction errors. However, competing evidence supports human dopamine signaling in the basal ganglia as also contributing to the generation of higher order learning heuristics. Here, we present data from a large (N = 81, 18-30yo), multi-modal neuroimaging study using simultaneously acquired task fMRI, affording temporal resolution of reward system function, and PET imaging with [11C]Raclopride (RAC), assessing striatal dopamine (DA) D2/3 receptor binding, during performance of a probabilistic reward learning task. Both fMRI activation and PET DA measures showed ventral striatum involvement for signaling rewards. However, greater DA release was uniquely associated with learning strategies (i.e., learning rates) that were more task-optimal within the best fitting reinforcement learning model. This DA response was associated with BOLD activation of a network of regions including anterior cingulate cortex, medial prefrontal cortex, thalamus and posterior parietal cortex, primarily during expectation, rather than prediction error, task epochs. Together, these data provide novel, human in vivo evidence that striatal dopaminergic signaling interacts with a network of cortical regions to generate task-optimal learning strategies, rather than representing reward outcomes in isolation.

Dissociation of basolateral and central amygdala effective connectivity predicts the stability of emotion-related impulsivity in adolescents and emerging adults with borderline personality symptoms: a resting-state fMRI study.

BACKGROUND: Borderline personality disorder (BPD) is associated with altered activity in the prefrontal cortex (PFC) and amygdala, yet no studies have examined fronto-limbic circuitry in borderline adolescents and emerging adults. Here, we examined the contribution of fronto-limbic effective connectivity (EC) to the longitudinal stability of emotion-related impulsivity, a key feature of BPD, in symptomatic adolescents and young adults. METHODS: We compared resting-state EC in 82 adolescents and emerging adults with and without clinically significant borderline symptoms (n BPD = 40, ages 13-30). Group-specific directed networks were estimated amongst fronto-limbic nodes including PFC, ventral striatum (VS), central amygdala (CeN), and basolateral amygdala (BLA). We examined the association of directed centrality metrics with initial levels and rates of change in emotion-related impulsivity symptoms over a one-year follow-up using latent growth curve models (LGCMs). RESULTS: In controls, ventromedial prefrontal cortex (vmPFC) and dorsal ACC had a directed influence on CeN and VS, respectively. In the BPD group, bilateral BLA had a directed influence on CeN, whereas in the healthy group CeN influenced BLA. LGCMs indicated that emotion-related impulsivity was stable across a one-year follow-up in the BPD group. Further, higher EC of R CeN to other regions in controls was associated with stronger within-person decreases in emotion-related impulsivity. CONCLUSIONS: Functional inputs from BLA and vmPFC appear to play competing roles in influencing CeN activity. In borderline adolescents and young adults, BLA may predominate over CeN activity, while in controls the ability of CeN to influence BLA activity predicted more rapid reductions in emotion-related impulsivity.

Momentary interpersonal processes of suicidal surges in borderline personality disorder.

BACKGROUND: Suicide rates are high in borderline personality disorder (BPD) where interpersonal problems trigger intense affective dysregulation and impulses to act on suicidal thoughts. To date, however, no study has examined how interpersonal stressors contribute to momentary within-person links among affect and impulsivity with suicidal ideation (SI), and how those links vary over time in people's daily lives. METHODS: A total of 153 individuals diagnosed with BPD and 52 healthy controls completed a 21-day ecological momentary assessment protocol. Of these 153 individuals with BPD, 105 had a history of suicide attempts. Multilevel structural equation modeling was used to examine dynamic links among interpersonal perceptions, affect, state impulsivity, and suicidal intent. RESULTS: Aggregated across interactions, lower perceived warmth in others was associated with SI. This direct relationship, however, did not extend to momentary within-person associations. Instead, interpersonal conflicts were linked to SI indirectly via greater negative affect and lower positive affect. While a robust within-person link between interpersonal perceptions and impulsivity emerged, impulsivity did not account for the relationship between interpersonal perceptions and SI. CONCLUSION: This intensive longitudinal study illustrates momentary interpersonal signatures of an emerging suicidal crisis. Among people with BPD at high risk for suicide, interpersonal triggers initiate a cascade of affective dysregulation, which in turn gives rise to SI.

Borderline personality disorder: stress reactivity or stress generation? A prospective dimensional study.

BACKGROUND: Individuals diagnosed with borderline personality disorder (BPD) often describe their lives as stressful and unpredictable. However, it is unclear whether the adversity faced by those with BPD is a product of stress reactivity or stress generation. Here, we examined the dynamic, prospective associations between BPD and stressful life events over 3 years. Given the heterogeneity present in BPD, we sought to understand which empirically derived dimensions of this heterogeneous disorder explain stress reactivity v. stress generation. METHODS: Participants included 355 individuals diagnosed with BPD and followed longitudinally at three annual assessments. Auto-regressive cross-lagged panel models were used to examine prospective associations between stressful life events and three latent dimensions implicated in BPD: negative affect, disinhibition, and antagonism. RESULTS: Antagonism and disinhibition, but not negative affect, prospectively predicted dependent stressful life events (events the individual may have some role in). Evidence for decompensation under stress was more tenuous, with independent stressful life events (those presumably outside the individual's control) predicting increases in negative affect. CONCLUSIONS: Our longitudinal study of a well-characterized clinical sample found more evidence for stress generation than for stress-induced decompensation in BPD. Stress generation in BPD is driven by externalizing dimensions: antagonism and disinhibition. These results highlight the utility of empirically derived dimensions for parsing heterogeneity present in BPD, leading to improvements in diagnostic evaluation, clinical prediction, and individualized approaches to treatment planning.

Neural connectivity biotypes: associations with internalizing problems throughout adolescence.

BACKGROUND: Neurophysiological patterns may distinguish which youth are at risk for the well-documented increase in internalizing symptoms during adolescence. Adolescents with internalizing problems exhibit altered resting-state functional connectivity (RSFC) of brain regions involved in socio-affective processing. Whether connectivity-based biotypes differentiate adolescents' levels of internalizing problems remains unknown. METHOD: Sixty-eight adolescents (37 females) reported on their internalizing problems at ages 14, 16, and 18 years. A resting-state functional neuroimaging scan was collected at age 16. Time-series data of 15 internalizing-relevant brain regions were entered into the Subgroup-Group Iterative Multi-Model Estimation program to identify subgroups based on RSFC maps. Associations between internalizing problems and connectivity-based biotypes were tested with regression analyses. RESULTS: Two connectivity-based biotypes were found: a Diffusely-connected biotype (N = 46), with long-range fronto-parietal paths, and a Hyper-connected biotype (N = 22), with paths between subcortical and medial frontal areas (e.g. affective and default-mode network regions). Higher levels of past (age 14) internalizing problems predicted a greater likelihood of belonging to the Hyper-connected biotype at age 16. The Hyper-connected biotype showed higher levels of concurrent problems (age 16) and future (age 18) internalizing problems. CONCLUSIONS: Differential patterns of RSFC among socio-affective brain regions were predicted by earlier internalizing problems and predicted future internalizing problems in adolescence. Measuring connectivity-based biotypes in adolescence may offer insight into which youth face an elevated risk for internalizing disorders during this critical developmental period.

The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence.

Traditional diagnostic systems went beyond empirical evidence on the structure of mental health. Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited. The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence. It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability. We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework. The system is not yet comprehensive, and we describe the processes for improving and expanding it. We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response. We describe progress in the development of HiTOP-based measures and in clinical implementation of the system. Finally, we review outstanding challenges and the research agenda. HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology.

Disentangling cognitive processes in externalizing psychopathology using drift diffusion modeling: Antagonism, but not disinhibition, is associated with poor cognitive control.

Although externalizing psychopathology has been linked to deficits in cognitive control, the cognitive processes underlying this association are unclear. Here, we provide a theoretical account of how research on cognitive processes can help to integrate and distinguish personality and psychopathology. We then apply this account to connect the two major subcomponents of externalizing, Antagonism and Disinhibition, with specific control processes using a battery of inhibitory control tasks and corresponding computational modeling. Participants (final N = 104) completed the flanker, go/no-go, and recent probes tasks, as well as normal and maladaptive personality inventories and measures of psychological distress. We fit participants' task behavior using a hierarchical drift diffusion model (DDM) to decompose their responses into specific cognitive processes. Using multilevel structural equation models, we found that Antagonism was associated with faster RTs on the flanker task and lower accuracy on flanker and go/no-go tasks. These results were complemented by DDM parameter associations: Antagonism was linked to decreased threshold and drift rate parameter estimates in the flanker task and a decreased drift rate on no-go trials. Altogether, our findings indicate that Antagonism is associated with specific impairments in fast (sub-second) inhibitory control processes involved in withholding prepared/prepotent responses and filtering distracting information. Disinhibition and momentary distress, however, were not associated with task performance.

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory.

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.