Impairment in acquisition of conditioned fear in schizophrenia

Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework

Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.

Changes in responses of the amygdala and hippocampus during fear conditioning are associated with persecutory beliefs

The persecutory delusion is the most common symptom of psychosis, yet its underlying neurobiological mechanisms are poorly understood. Prior studies have suggested that abnormalities in medial temporal lobe-dependent associative learning may contribute to this symptom. In the current study, this hypothesis was tested in a non-clinical sample of young adults without histories of psychiatric treatment (n = 64), who underwent classical Pavlovian fear conditioning while fMRI data were collected. During the fear conditioning procedure, participants viewed images of faces which were paired (the CS+) or not paired (the CS-) with an aversive stimulus (a mild electrical shock). Fear conditioning-related neural responses were measured in two medial temporal lobe regions, the amygdala and hippocampus, and in other closely connected brain regions of the salience and default networks. The participants without persecutory beliefs (n = 43) showed greater responses to the CS- compared to the CS+ in the right amygdala and hippocampus, while the participants with persecutory beliefs (n = 21) failed to exhibit this response. These between-group differences were not accounted for by symptoms of depression, anxiety or a psychosis risk syndrome. However, the severity of subclinical psychotic symptoms overall was correlated with the level of this aberrant response in the amygdala (p = .013) and hippocampus (p = .033). Thus, these findings provide evidence for a disruption of medial temporal lobe-dependent associative learning in young people with subclinical psychotic symptoms, specifically persecutory thinking.

Data-driven, connectome-wide analysis identifies psychosis-specific brain correlates of fear and anxiety

Decades of psychosis research highlight the prevalence and the clinical significance of negative emotions, such as fear and anxiety. Translational evidence demonstrates the pivotal role of the amygdala in fear and anxiety. However, most of these approaches have used hypothesis-driven analyses with predefined regions of interest. A data-driven analysis may provide a complimentary, unbiased approach to identifying brain correlates of fear and anxiety. The aim of the current study was to identify the brain basis of fear and anxiety in early psychosis and controls using a data-driven approach. We analyzed data from the Human Connectome Project for Early Psychosis, a multi-site study of 125 people with psychosis and 58 controls with resting-state fMRI and clinical characterization. Multivariate pattern analysis of whole-connectome data was used to identify shared and psychosis-specific brain correlates of fear and anxiety using the NIH Toolbox Fear-Affect and Fear-Somatic Arousal scales. We then examined clinical correlations of Fear-Affect scores and connectivity patterns. Individuals with psychosis had higher levels of Fear-Affect scores than controls (p < 0.05). The data-driven analysis identified a cluster encompassing the amygdala and hippocampus where connectivity was correlated with Fear-Affect score (p < 0.005) in the entire sample. The strongest correlate of Fear-Affect was between this cluster and the anterior insula and stronger connectivity was associated with higher Fear-Affect scores (r = 0.31, p = 0.0003). The multivariate pattern analysis also identified a psychosis-specific correlate of Fear-Affect score between the amygdala/hippocampus cluster and a cluster in the ventromedial prefrontal cortex (VMPFC). Higher Fear-Affect scores were correlated with stronger amygdala/hippocampal-VMPFC connectivity in the early psychosis group (r = 0.33, p = 0.002), but not in controls (r = -0.15, p = 0.28). The current study provides evidence for the transdiagnostic role of the amygdala, hippocampus, and anterior insula in the neural basis of fear and anxiety and suggests a psychosis-specific relationship between fear and anxiety symptoms and amygdala/hippocampal-VMPFC connectivity. Our novel data-driven approach identifies novel, psychosis-specific treatment targets for fear and anxiety symptoms and provides complimentary evidence to decades of hypothesis-driven approaches examining the brain basis of threat processing.

Revisiting delusion subtypes in schizophrenia based on their underlying structures

A clear understanding of the pathophysiology of schizophrenia and related spectrum disorders has been limited by clinical heterogeneity. We investigated whether relative severity and predominance of one or more delusion subtypes might yield clinically differentiable patient profiles. Patients (N = 286) with schizophrenia spectrum disorders (SSD) completed the 21-item Peters et al. Delusions Inventory (PDI-21). We performed factor analysis followed by k-means clustering to identify delusion factors and patient subtypes. Patients were further assessed via the Brief Psychiatric Rating Scale, Brief Negative Symptom Scale, Digit Symbol and Digit Substitution tasks, use of cannabis and tobacco, and stressful life events. The overall patient sample clustered into subtypes corresponding to Low-Delusion, Grandiose-Predominant, Paranoid-Predominant, and Pan-Delusion patients. Paranoid-Predominant and Pan-Delusion patients showed significantly higher burden of positive symptoms, while Low-Delusion patients showed the highest burden of negative symptoms. The Paranoia delusion factor score showed a positive association with Digit Symbol and Digit Substitution tasks in the overall sample, and the Paranoid-Predominant subtype exhibited the best performance on both tasks. Grandiose-Predominant patients showed significantly higher tobacco smoking severity than other subtypes, while Paranoid-Predominant patients were significantly more likely to have a lifetime diagnosis of Cannabis Use Disorder. We suggest that delusion self-report inventories such as the PDI-21 may be of utility in identifying sub-syndromes in SSD. From the current study, a Paranoid-Predominant form may be most distinctive, with features including less cognitive impairment and a stronger association with cannabis use.

65 years of research on dopamine's role in classical fear conditioning and extinction: A systematic review

Dopamine, a catecholamine neurotransmitter, has historically been associated with the encoding of reward, whereas its role in aversion has received less attention. Here, we systematically gathered the vast evidence of the role of dopamine in the simplest forms of aversive learning: classical fear conditioning and extinction. In the past, crude methods were used to augment or inhibit dopamine to study its relationship with fear conditioning and extinction. More advanced techniques such as conditional genetic, chemogenic and optogenetic approaches now provide causal evidence for dopamine's role in these learning processes. Dopamine neurons encode conditioned stimuli during fear conditioning and extinction and convey the signal via activation of D1-4 receptor sites particularly in the amygdala, prefrontal cortex and striatum. The coordinated activation of dopamine receptors allows for the continuous formation, consolidation, retrieval and updating of fear and extinction memory in a dynamic and reciprocal manner. Based on the reviewed literature, we conclude that dopamine is crucial for the encoding of classical fear conditioning and extinction and contributes in a way that is comparable to its role in encoding reward.

Threat Responses in Schizophrenia: A Negative Valence Systems Framework

PURPOSE OF REVIEW

Emotions are prominent in theories and accounts of schizophrenia but are largely understudied compared to cognition. Utilizing the Research Domain Criteria (RDoC) Negative Valence Systems framework, we review the current knowledge of emotions in schizophrenia. Given the pivotal role of threat responses in theories of schizophrenia and the substantial evidence of altered threat responses, we focus on three components of Negative Valence Systems tied to threat responses: responses to acute threat, responses to potential threat, and sustained threat.

RECENT FINDINGS

Individuals with schizophrenia show altered responses to neutral stimuli during acute threat, bed nucleus of the stria terminalis connectivity in response to potential threat, and threat responses associated with sustained threat. Our review concludes that Negative Valence Systems are altered in schizophrenia; however, the level and evidence of alterations vary across the types of threat responses. We suggest avenues for future research to further understand and intervene on threat responses in schizophrenia.

Resolving the Delusion Paradox

BACKGROUND AND HYPOTHESIS

The neurocomputational framework of predictive processing (PP) provides a promising approach to explaining delusions, a key symptom of psychotic disorders. According to PP, the brain makes inferences about the world by weighing prior beliefs against the available sensory data. Mismatches between prior beliefs and sensory data result in prediction errors that may update the brain's model of the world. Psychosis has been associated with reduced weighting of priors relative to the sensory data. However, delusional beliefs are highly resistant to change, suggesting increased rather than decreased weighting of priors. We propose that this "delusion paradox" can be resolved within a hierarchical PP model: Reduced weighting of prior beliefs at low hierarchical levels may be compensated by an increased influence of higher-order beliefs represented at high hierarchical levels, including delusional beliefs. This may sculpt perceptual processing into conformity with delusions and foster their resistance to contradictory evidence.

STUDY DESIGN

We review several lines of experimental evidence on low- and high-level processes, and their neurocognitive underpinnings in delusion-related phenotypes and link them to predicted processing.

STUDY RESULTS

The reviewed evidence supports the notion of decreased weighting of low-level priors and increased weighting of high-level priors, in both delusional and delusion-prone individuals. Moreover, we highlight the role of prefrontal cortex as a neural basis for the increased weighting of high-level prior beliefs and discuss possible clinical implications of the proposed hierarchical predictive-processing model.

CONCLUSIONS

Our review suggests the delusion paradox can be resolved within a hierarchical PP model.

From safety to frustration: The neural substrates of inhibitory learning in aversive and appetitive conditioning procedures

Inhibitory associative learning counters the effects of excitatory learning, whether appetitively or aversively motivated. Moreover, the affective responses accompanying the inhibitory associations are of opponent valence to the excitatory conditioned responses. Inhibitors for negative aversive outcomes (e.g. shock) signal safety, while inhibitors for appetitive outcomes (e.g. food reward) elicit frustration and/or disappointment. This raises the question as to whether studies using appetitive and aversive conditioning procedures should demonstrate the same neural substrates for inhibitory learning. We review the neural substrates of appetitive and aversive inhibitory learning as measured in different procedural variants and in the context of the underpinning excitatory conditioning on which it depends. The mesocorticolimbic dopamine pathways, retrosplenial cortex and hippocampus are consistently implicated in inhibitory learning. Further neural substrates identified in some procedural variants may be related to the specific motivation of the learning task and modalities of the learning cues. Finally, we consider the translational implications of our understanding of the neural substrates of inhibitory learning, for obesity and addictions as well as for anxiety disorders.

The neurobiology of Pavlovian safety learning: Towards an acquisition-expression framework

Safety learning creates associations between conditional stimuli and the absence of threat. Studies of human fear conditioning have accumulated evidence for the neural signatures of safety over various paradigms, aligning on several common brain systems. While these systems are often interpreted as underlying safety learning in a generic sense, they may instead reflect the expression of learned safety, pertaining to processes of fear inhibition, positive affect, and memory. Animal models strongly suggest these can be separable from neural circuits implicated in the conditioning process itself (or safety acquisition). While acquisition-expression distinctions are ubiquitous in behavioural science, this lens has not been applied to safety learning, which remains a novel area in the field. In this mini-review, we overview findings from prevalent safety paradigms in humans, and synthesise these with insights from animal models to propose that the neurobiology of safety learning be conceptualised along an acquisition-expression model, with the aim of stimulating richer brain-based characterisations of this important process.

Enhanced Instructed Fear Learning in Delusion-Proneness

Psychosis is associated with distorted perceptions and deficient bottom-up learning such as classical fear conditioning. This has been interpreted as reflecting imprecise priors in low-level predictive coding systems. Paradoxically, overly strong beliefs, such as overvalued beliefs and delusions, are also present in psychosis-associated states. In line with this, research has suggested that patients with psychosis and associated phenotypes rely more on high-order priors to interpret perceptual input. In this behavioural and fMRI study we studied two types of fear learning, i.e., instructed fear learning mediated by verbal suggestions about fear contingencies and classical fear conditioning mediated by low level associative learning, in delusion proneness—a trait in healthy individuals linked to psychotic disorders. Subjects were shown four faces out of which two were coupled with an aversive stimulation (CS+) while two were not (CS-) in a fear conditioning procedure. Before the conditioning, subjects were informed about the contingencies for two of the faces of each type, while no information was given for the two other faces. We could thereby study the effect of both classical fear conditioning and instructed fear learning. Our main outcome variable was evaluative rating of the faces. Simultaneously, fMRI-measurements were performed to study underlying mechanisms. We postulated that instructed fear learning, measured with evaluative ratings, is stronger in psychosis-related phenotypes, in contrast to classical fear conditioning that has repeatedly been shown to be weaker in these groups. In line with our hypothesis, we observed significantly larger instructed fear learning on a behavioural level in delusion-prone individuals (n = 20) compared to non-delusion-prone subjects (n = 23; n = 20 in fMRI study). Instructed fear learning was associated with a bilateral activation of lateral orbitofrontal cortex that did not differ significantly between groups. However, delusion-prone subjects showed a stronger functional connectivity between right lateral orbitofrontal cortex and regions processing fear and pain. Our results suggest that psychosis-related states are associated with a strong instructed fear learning in addition to previously reported weak classical fear conditioning. Given the similarity between nocebo paradigms and instructed fear learning, our results also have an impact on understanding why nocebo effects differ between individuals.

Whole brain correlates of individual differences in skin conductance responses during discriminative fear conditioning to social cues

Understanding the neural basis for individual differences in the skin conductance response (SCR) during discriminative fear conditioning may inform on our understanding of autonomic regulation in fear-related psychopathology. Previous region-of-interest (ROI) analyses have implicated the amygdala in regulating conditioned SCR, but whole brain analyses are lacking. This study examined correlations between individual differences in SCR during discriminative fear conditioning to social stimuli and neural activity throughout the brain, by using data from a large functional magnetic resonance imaging study of twins (N = 285 individuals). Results show that conditioned SCR correlates with activity in the dorsal anterior cingulate cortex/anterior midcingulate cortex, anterior insula, bilateral temporoparietal junction, right frontal operculum, bilateral dorsal premotor cortex, right superior parietal lobe, and midbrain. A ROI analysis additionally showed a positive correlation between amygdala activity and conditioned SCR in line with previous reports. We suggest that the observed whole brain correlates of SCR belong to a large-scale midcingulo-insular network related to salience detection and autonomic-interoceptive processing. Altered activity within this network may underlie individual differences in conditioned SCR and autonomic aspects of psychopathology.