Altered neural encoding of prediction errors in assault-related posttraumatic stress disorder

Aberrant neural event segmentation during a continuous social narrative in trauma-exposed older adolescents and young adults

Post-traumatic stress and major depressive disorders are associated with "overgeneral" autobiographical memory, or impaired recall of specific life events. Interpersonal trauma exposure, a risk factor for both conditions, may influence how symptomatic trauma-exposed (TE) individuals segment everyday events. The ability to parse experience into units (event segmentation) supports memory. Neural state transitions occur within a cortical hierarchy and play a key role in event segmentation, with regions like the occipital cortex, angular gyrus, and striatum involved in parsing event structure. We examined whether interpersonal trauma exposure was associated with alterations in the cortical hierarchy and striatal activity at neural state transitions in symptomatic TE versus healthy control (HC) individuals. Fifty older adolescents and young adults (29 TE, 21 HC) viewed the film "Partly Cloudy" during functional magnetic resonance imaging. A greedy-state boundary search algorithm assessed the optimal number of events, quality, and segmentation agreement of neural state transitions in the occipital cortex and angular gyrus. Striatal (nucleus accumbens, caudate, and putamen) activity was assessed at occipital and angular gyrus-evoked state transitions. Compared to HCs, TE participants displayed less occipital and greater angular gyrus-evoked optimal number of neural state transitions. TE participants also displayed lower quality of neural state segmentation solutions in occipital and angular cortices compared to HCs. Additionally, TE participants had less putamen activity at angular gyrus-evoked state transitions than HCs. This investigation provides neurobiological insights into aberrant event segmentation in symptomatic TE individuals, shedding light on mechanisms influencing overgeneral memory in trauma-related disorders.

Can computational models help elucidate the link between complex trauma and hallucinations?

Recently, a number of predictive coding models have been proposed to account for post-traumatic stress disorder (PTSD)'s symptomatology, including intrusions, flashbacks and hallucinations. These models were usually developed to account for traditional/type-1 PTSD. We here discuss whether these models also apply or can be translated to the case of complex/type-2 PTSD and childhood trauma (cPTSD). The distinction between PTSD and cPTSD is important because the disorders differ in terms of symptomatology and potential mechanisms, how they relate to developmental stages, but also in terms of illness trajectory and treatment. Models of complex trauma could give us insights on hallucinations in physiological/pathological conditions or more generally on the development of intrusive experiences across diagnostic classes.

Latent-state and model-based learning in PTSD

Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes.

Placing Approach-Avoidance Conflict Within the Framework of Multi-objective Reinforcement Learning

Many psychiatric disorders are marked by impaired decision-making during an approach-avoidance conflict. Current experiments elicit approach-avoidance conflicts in bandit tasks by pairing an individual's actions with consequences that are simultaneously desirable (reward) and undesirable (harm). We frame approach-avoidance conflict tasks as a multi-objective multi-armed bandit. By defining a general decision-maker as a limiting sequence of actions, we disentangle the decision process from learning. Each decision maker can then be identified as a multi-dimensional point representing its long-term average expected outcomes, while different decision making models can be associated by the geometry of their 'feasible region', the set of all possible long term performances on a fixed task. We introduce three example decision-makers based on popular reinforcement learning models and characterize their feasible regions, including whether they can be Pareto optimal. From this perspective, we find that existing tasks are unable to distinguish between the three examples of decision-makers. We show how to design new tasks whose geometric structure can be used to better distinguish between decision-makers. These findings are expected to guide the design of approach-avoidance conflict tasks and the modeling of resulting decision-making behavior.

Diminished prospective mental representations of reward mediate reward learning strategies among youth with internalizing symptoms

BACKGROUND

Adolescent internalizing symptoms and trauma exposure have been linked with altered reward learning processes and decreased ventral striatal responses to rewarding cues. Recent computational work on decision-making highlights an important role for prospective representations of the imagined outcomes of different choices. This study tested whether internalizing symptoms and trauma exposure among youth impact the generation of prospective reward representations during decision-making and potentially mediate altered behavioral strategies during reward learning.

METHODS

Sixty-one adolescent females with varying exposure to interpersonal violence exposure (n = 31 with histories of physical or sexual assault) and severity of internalizing symptoms completed a social reward learning task during fMRI. Multivariate pattern analyses (MVPA) were used to decode neural reward representations at the time of choice.

RESULTS

MVPA demonstrated that rewarding outcomes could accurately be decoded within several large-scale distributed networks (e.g. frontoparietal and striatum networks), that these reward representations were reactivated prospectively at the time of choice in proportion to the expected probability of receiving reward, and that youth with behavioral strategies that favored exploiting high reward options demonstrated greater prospective generation of reward representations. Youth internalizing symptoms, but not trauma exposure characteristics, were negatively associated with both the behavioral strategy of exploiting high reward options as well as the prospective generation of reward representations in the striatum.

CONCLUSIONS

These data suggest diminished prospective mental simulation of reward as a mechanism of altered reward learning strategies among youth with internalizing symptoms.

The impact of cumulative obstetric complications and childhood trauma on brain volume in young people with psychotic experiences

Psychotic experiences (PEs) occur in 5-10% of the general population and are associated with exposure to childhood trauma and obstetric complications. However, the neurobiological mechanisms underlying these associations are unclear. Using the Avon Longitudinal Study of Parents and Children (ALSPAC), we studied 138 young people aged 20 with PEs (n = 49 suspected, n = 53 definite, n = 36 psychotic disorder) and 275 controls. Voxel-based morphometry assessed whether MRI measures of grey matter volume were associated with (i) PEs, (ii) cumulative childhood psychological trauma (weighted summary score of 6 trauma types), (iii) cumulative pre/peri-natal risk factors for psychosis (weighted summary score of 16 risk factors), and (iv) the interaction between PEs and cumulative trauma or pre/peri-natal risk. PEs were associated with smaller left posterior cingulate (pFWE < 0.001, Z = 4.19) and thalamus volumes (pFWE = 0.006, Z = 3.91). Cumulative pre/perinatal risk was associated with smaller left subgenual cingulate volume (pFWE < 0.001, Z = 4.54). A significant interaction between PEs and cumulative pre/perinatal risk found larger striatum (pFWE = 0.04, Z = 3.89) and smaller right insula volume extending into the supramarginal gyrus and superior temporal gyrus (pFWE = 0.002, Z = 4.79), specifically in those with definite PEs and psychotic disorder. Cumulative childhood trauma was associated with larger left dorsal striatum (pFWE = 0.002, Z = 3.65), right prefrontal cortex (pFWE < 0.001, Z = 4.63) and smaller left insula volume in all participants (pFWE = 0.03, Z = 3.60), and there was no interaction with PEs group. In summary, pre/peri-natal risk factors and childhood psychological trauma impact similar brain pathways, namely smaller insula and larger striatum volumes. The effect of pre/perinatal risk was greatest in those with more severe PEs, whereas effects of trauma were seen in all participants. In conclusion, environmental risk factors affect brain networks implicated in schizophrenia, which may increase an individual's propensity to develop later psychotic disorders.

Neural Correlates of Reward Processing in the Onset, Maintenance, and Treatment of Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a prevalent, debilitating, and heterogeneous psychiatric condition marked by both exaggerated threat responding and diminished positive affect. While symptom profiles of PTSD differ across individuals, symptoms also vary within individuals over the course of illness. Functional magnetic resonance imaging studies have provided crucial insights into the neurobiology of heightened threat responsivity in PTSD, which has aided in identifying neurobiological risk factors and treatment targets for this disorder. Despite this demonstrated utility, the application of functional magnetic resonance imaging to understanding deficits in reward responsivity in PTSD remains underexplored. Significantly, over 60% of individuals with PTSD experience anhedonia, or an inability to feel pleasure, which may reflect reward processing deficits. To better understand the neural underpinnings of reward deficits and their relevance to the onset, maintenance, and treatment of PTSD, we reviewed the functional magnetic resonance imaging literature through the framework of disease prognosis. Here, we provide insights on whether reward deficits are central to PTSD or are better explained by comorbid major depressive disorder, and we clarify how reward-related deficiencies in PTSD fit into the context of more intensely studied threat-related deficits.

A computational model for learning from repeated traumatic experiences under uncertainty

Traumatic events can lead to lifelong, inflexible adaptations in threat perception and behavior, which characterize posttraumatic stress disorder (PTSD). This process involves associations between sensory cues and internal states of threat and then generalization of the threat responses to previously neutral cues. However, most formulations neglect adaptations to threat that are not specific to those associations. To incorporate nonassociative responses to threat, we propose a computational theory of PTSD based on adaptation to the frequency of traumatic events by using a reinforcement learning momentum model. Recent threat prediction errors generate momentum that influences subsequent threat perception in novel contexts. This model fits primary data acquired from a mouse model of PTSD, in which unpredictable footshocks in one context accelerate threat learning in a novel context. The theory is consistent with epidemiological data that show that PTSD incidence increases with the number of traumatic events, as well as the disproportionate impact of early life trauma. Because the theory proposes that PTSD relates to the average of recent threat prediction errors rather than the strength of a specific association, it makes novel predictions for the treatment of PTSD.

Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches

Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.

Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making

Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat value into a combined total value according to an individually varying policy parameter, which was highly predictive of decisions to approach reward vs avoid threat during trials where the highest reward option was also the highest threat option (i.e., approach-avoidance conflict). ICA analyses demonstrated unique roles for salience, frontoparietal, medial prefrontal, and inferior frontal networks in differential encoding of reward vs threat prediction error and value signals. The left frontoparietal network uniquely encoded degree of conflict between reward and threat value at the time of choice. MVPA demonstrated that delivery of reward and threat could accurately be decoded within salience and inferior frontal networks, respectively, and that decisions to approach reward vs avoid threat were predicted by the relative degree to which these reward vs threat representations were active at the time of choice. This latter result suggests that navigating AAC decisions involves generating mental representations for possible decision outcomes, and relative activation of these representations may bias subsequent decision-making towards approaching reward or avoiding threat accordingly.

Reinforcement Learning in Patients With Mood and Anxiety Disorders vs Control Individuals: A Systematic Review and Meta-analysis

IMPORTANCE

Computational psychiatry studies have investigated how reinforcement learning may be different in individuals with mood and anxiety disorders compared with control individuals, but results are inconsistent.

OBJECTIVE

To assess whether there are consistent differences in reinforcement-learning parameters between patients with depression or anxiety and control individuals.

DATA SOURCES

Web of Knowledge, PubMed, Embase, and Google Scholar searches were performed between November 15, 2019, and December 6, 2019, and repeated on December 3, 2020, and February 23, 2021, with keywords (reinforcement learning) AND (computational OR model) AND (depression OR anxiety OR mood).

STUDY SELECTION

Studies were included if they fit reinforcement-learning models to human choice data from a cognitive task with rewards or punishments, had a case-control design including participants with mood and/or anxiety disorders and healthy control individuals, and included sufficient information about all parameters in the models.

DATA EXTRACTION AND SYNTHESIS

Articles were assessed for inclusion according to MOOSE guidelines. Participant-level parameters were extracted from included articles, and a conventional meta-analysis was performed using a random-effects model. Subsequently, these parameters were used to simulate choice performance for each participant on benchmarking tasks in a simulation meta-analysis. Models were fitted, parameters were extracted using bayesian model averaging, and differences between patients and control individuals were examined. Overall effect sizes across analytic strategies were inspected.

MAIN OUTCOMES AND MEASURES

The primary outcomes were estimated reinforcement-learning parameters (learning rate, inverse temperature, reward learning rate, and punishment learning rate).

RESULTS

A total of 27 articles were included (3085 participants, 1242 of whom had depression and/or anxiety). In the conventional meta-analysis, patients showed lower inverse temperature than control individuals (standardized mean difference [SMD], -0.215; 95% CI, -0.354 to -0.077), although no parameters were common across all studies, limiting the ability to infer differences. In the simulation meta-analysis, patients showed greater punishment learning rates (SMD, 0.107; 95% CI, 0.107 to 0.108) and slightly lower reward learning rates (SMD, -0.021; 95% CI, -0.022 to -0.020) relative to control individuals. The simulation meta-analysis showed no meaningful difference in inverse temperature between patients and control individuals (SMD, 0.003; 95% CI, 0.002 to 0.004).

CONCLUSIONS AND RELEVANCE

The simulation meta-analytic approach introduced in this article for inferring meta-group differences from heterogeneous computational psychiatry studies indicated elevated punishment learning rates in patients compared with control individuals. This difference may promote and uphold negative affective bias symptoms and hence constitute a potential mechanistic treatment target for mood and anxiety disorders.

Value estimation and latent-state update-related neural activity during fear conditioning predict posttraumatic stress disorder symptom severity

Learning theories of posttraumatic stress disorder (PTSD) purport that fear-learning processes, such as those that support fear acquisition and extinction, are impaired. Computational models designed to capture specific processes involved in fear learning have primarily assessed model-free, or trial-and-error, reinforcement learning (RL). Although previous studies indicated that aspects of model-free RL are disrupted among individuals with PTSD, research has yet to identify whether model-based RL, which is inferential and contextually driven, is impaired. Given empirical evidence of aberrant contextual modulation of fear in PTSD, the present study sought to identify whether model-based RL processes are altered during fear conditioning among women with interpersonal violence (IPV)-related PTSD (n = 85) using computational modeling. Model-free, hybrid, and model-based RL models were applied to skin conductance responses (SCR) collected during fear acquisition and extinction, and the model-based RL model was found to provide the best fit to the SCR data. Parameters from the model-based RL model were carried forward to neuroimaging analyses (voxel-wise and independent component analysis). Results revealed that reduced activity within visual processing regions during model-based updating uniquely predicted higher PTSD symptoms. Additionally, after controlling for model-based updating, greater value estimation encoding within the left frontoparietal network during fear acquisition and reduced value estimation encoding within the dorsomedial prefrontal cortex during fear extinction predicted greater PTSD symptoms. Results provide evidence of disrupted RL processes in women with assault-related PTSD, which may contribute to impaired fear and safety learning, and, furthermore, may relate to treatment response (e.g., poorer response to exposure therapy).

Frontoparietal network activity during model-based reinforcement learning updates is reduced among adolescents with severe sexual abuse

Trauma and trauma-related disorders are characterized by impaired learning processes, including reinforcement learning (RL). Identifying which aspects of learning are altered by trauma is critical endeavor, as this may reveal key mechanisms of impairment and potential intervention targets. There are at least two types of RL that have been delineated using computational modeling: model-free and model-based RL. Although these RL processes differentially predict decision-making behavior, most research has examined the impact of trauma on model-free RL. Currently unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships, is impaired among individuals with a history of trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n = 60; 29 without a history of assault and 31 with a history of assault with and without PTSD) completed a three-arm bandit task during fMRI acquisition. Two computational models compared the degree to which participants' task behavior fit the use of a model-free versus model-based RL strategy. Although a history of assaultive trauma did not predict poorer model-based RL, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates. Altered model-based RL, which supports goal-directed behavior, may be an important route through which clinical impairment emerges among individuals with a history of severe sexual abuse and should be examined further in future studies.

Neurobiological Alterations in Females With PTSD: A Systematic Review

Most females experience at least one traumatic event in their lives, but not all develop PTSD. Despite considerable research, our understanding of the key factors that constitute risk for PTSD among females is limited. Previous research has largely focused on sex differences, neglecting within group comparisons, thereby obviating differences between females who do and do not develop PTSD following exposure to trauma. In this systematic review, we conducted a search for the extent of existing research utilizing magnetic resonance imaging (MRI) to examine neurobiological differences among females of all ages, with and without PTSD. Only studies of females who met full diagnostic criteria for PTSD were included. Fifty-six studies were selected and reviewed. We synthesized here findings from structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and resting state functional connectivity (rs-FC MRI) studies, comparing females with and without PTSD. A range of biopsychosocial constructs that may leave females vulnerable to PTSD were discussed. First, the ways timing and type of exposure to trauma may impact PTSD risk were discussed. Second, the key role that cognitive and behavioral mechanisms may play in PTSD was described, including rumination, and deficient fear extinction. Third, the role of specific symptom patterns and common comorbidities in female-specific PTSD was described, as well as sex-specific implications on treatment and parenting outcomes. We concluded by identifying areas for future research, to address the need to better understand developmental aspects of brain alterations, the differential impact of trauma types and timing, the putative role of neuroendocrine system in neurobiology of PTSD among females, and the impact of social and cultural factors on neurobiology in females with PTSD.

A Competition of Critics in Human Decision-Making

Recent experiments and theories of human decision-making suggest positive and negative errors are processed and encoded differently by serotonin and dopamine, with serotonin possibly serving to oppose dopamine and protect against risky decisions. We introduce a temporal difference (TD) model of human decision-making to account for these features. Our model involves two critics, an optimistic learning system and a pessimistic learning system, whose predictions are integrated in time to control how potential decisions compete to be selected. Our model predicts that human decision-making can be decomposed along two dimensions: the degree to which the individual is sensitive to (1) risk and (2) uncertainty. In addition, we demonstrate that the model can learn about the mean and standard deviation of rewards, and provide information about reaction time despite not modeling these variables directly. Lastly, we simulate a recent experiment to show how updates of the two learning systems could relate to dopamine and serotonin transients, thereby providing a mathematical formalism to serotonin's hypothesized role as an opponent to dopamine. This new model should be useful for future experiments on human decision-making.

Distinct cortical thickness correlates of early life trauma exposure and posttraumatic stress disorder are shared among adolescent and adult females with interpersonal violence exposure

Early life trauma (ELT) exposure and posttraumatic stress disorder (PTSD) both affect neural structure, which predicts a variety of mental health concerns throughout the lifespan and may present differently between adolescents and adults. However, few studies have identified the relationship between ELT, PTSD, development, and brain structure using cortical thickness (CT). CT may reveal previously obscured alterations that are potentially clinically relevant and, furthermore, could identify specific structural correlates distinct to ELT from PTSD. Two hundred and fifty-three female adolescent and adult survivors of interpersonal violence and non-trauma-exposed demographically matched controls underwent structural MRI at two different sites. Images were processed and CT was estimated using FreeSurfer. Vertex-wise linear model tests were conducted across the cortical surface to investigate whether PTSD and ELT exposure uniquely affect CT, controlling for scanner site. Planned follow-up tests included second-level analyses of clinical symptoms for CT clusters that were significantly related to PTSD or ELT. CT in the middle cingulate cortex was inversely related to ELT in both age groups, such that individuals with more ELT demonstrated less CT in this region. Additionally, CT was significantly greater in the bilateral intraparietal sulcus and left angular gyrus in both adolescents and adults with PTSD. Furthermore, CT in these clusters was also significantly related to clinical symptom severity in the adult PTSD group. This study provides evidence for distinct CT correlates of ELT and PTSD that are present across adolescents and adults, suggesting consistent markers related to ELT and PTSD on gray matter structure in trauma-exposed individuals.

Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI-a method for identifying brain regions correlated with latent variables-have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct-rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI.

Differential relationships of PTSD symptom clusters with cortical thickness and grey matter volumes among women with PTSD

Structural neuroimaging studies of posttraumatic stress disorder (PTSD) have typically reported reduced cortical thickness (CT) and gray matter volume (GMV) in subcortical structures and networks involved in memory retrieval, emotional processing and regulation, and fear acquisition and extinction. Although PTSD is more common in women, and interpersonal violence (IPV) exposure is a more potent risk factor for developing PTSD relative to other forms of trauma, most of the existing literature examined combat-exposed men with PTSD. Vertex-wise CT and subcortical GMV analyses were conducted to examine potential differences in a large, well-characterized sample of women with PTSD stemming from IPV-exposure (n = 99) compared to healthy trauma-free women without a diagnosis of PTSD (n = 22). Subgroup analyses were also conducted to determine whether symptom severity within specific PTSD symptom clusters (e.g., re-experiencing, active avoidance, hyperarousal) predict CT and GMV after controlling for comorbid depression and anxiety. Results indicated that a diagnosis of PTSD in women with IPV-exposure did not significantly predict differences in CT across the cortex or GMV in the amygdala or hippocampus compared to healthy controls. However, within the PTSD group, greater re-experiencing symptom severity was associated with decreased CT in the left inferior and middle temporal gyrus, and decreased CT in the right parahippocampal and medial temporal gyrus. In contrast, greater active avoidance symptom severity was associated with greater CT in the left lateral fissure, postcentral gyrus, and middle/lateral occipital cortex, and greater CT in the right paracentral, posterior cingulate, and superior occipital gyrus. In terms of GMV, greater hyperarousal symptom severity was associated with reduced left amygdala GMV, while greater active avoidance symptom severity was associated with greater right amygdala GMV. These findings suggest that structural brain alterations among women with IPV-related PTSD may be driven by symptom severity within specific symptom clusters and that PTSD symptom clusters may have a differential (increased or decreased) association with brain structures.

Trauma Affects Prospective Relationships Between Reward-Related Ventral Striatal and Amygdala Activation and 1-Year Future Hypo/Mania Trajectories

BACKGROUND

Trauma exposure is associated with a more severe, persistent course of affective and anxiety symptoms. Markers of reward neural circuitry function, specifically activation to reward prediction error (RPE), are impacted by trauma and predict the future course of affective symptoms. This study's purpose was to determine how lifetime trauma exposure influences relationships between reward neural circuitry function and the course of future affective and anxiety symptoms in a naturalistic, transdiagnostic observational context.

METHODS

A total of 59 young adults aged 18-25 (48 female and 11 male participants, mean ± SD = 21.5 ± 2.0 years) experiencing psychological distress completed the study. Participants were evaluated at baseline, 6, and 12 months. At baseline, the participants reported lifetime trauma events and completed a monetary reward functional magnetic resonance imaging task. Affective and anxiety symptoms were reported at each visit, and trajectories were calculated using MPlus. Neural activation during RPE and other phases of reward processing were determined using SPM8. Trauma and reward neural activation were entered as predictors of symptom trajectories.

RESULTS

Trauma exposure moderated prospective relationships between left ventral striatum (β = -1.29, p = .02) and right amygdala (β = 0.58, p = .04) activation to RPE and future hypo/mania severity trajectory: the interaction between greater trauma and greater left ventral striatum activation to RPE was associated with a shallower increase in hypo/mania severity, whereas the interaction between greater trauma and greater right amygdala activation to RPE was associated with increasing hypo/mania severity.

CONCLUSIONS

Trauma exposure affects prospective relationships between markers of reward circuitry function and affective symptom trajectories. Evaluating trauma exposure is thus crucial in naturalistic and treatment studies aiming to identify neural predictors of future affective symptom course.

L-DOPA and consolidation of fear extinction learning among women with posttraumatic stress disorder

This study tested whether L-DOPA delivered during the consolidation window following fear extinction learning reduces subsequent fear responding among women with PTSD. Adult women diagnosed with PTSD completed a contextual fear acquisition and extinction task during fMRI and then immediately received either placebo (n = 34), 100/25 mg L-DOPA/carbidopa (n = 28), or 200/50 mg L-DOPA/carbidopa (n = 29). Participants completed a resting-state scan before the task and again 45 min following drug ingestion to characterize effects of L-DOPA on extinction memory neural reactivation patterns during consolidation. Twenty-four hours later, participants returned for tests of context renewal, extinction recall, and reinstatement during fMRI with concurrent skin conductance responding (SCR) assessment. Both active drug groups demonstrated increased reactivation of extinction encoding in the amygdala during the post-task resting-state scan. For SCR data, both drug groups exhibited decreased Day 2 reinstatement across all stimuli compared to placebo, and there was some evidence for decreased context renewal to the fear stimulus in the 100 mg group compared to placebo. For imaging data, both drug groups demonstrated decreased Day 2 reinstatement across stimuli in a bilateral insula network compared to placebo. There was no evidence in SCR or neural activity that L-DOPA improved extinction recall. Reactivation of extinction encodings in the amygdala during consolidation on Day 1 predicted Day 2 activation of the insula network. These results support a role for dopamine during the consolidation window in boosting reactivation of amygdala extinction encodings and reducing reinstatement, but not improving extinction recall, in women with PTSD.

Trauma or Drama: A Predictive Processing Perspective on the Continuum of Stress

The notion of psychological trauma has been liberally used both in clinical literature and general discourse. However, no consensus exists on its exact meaning and definition. Whereas traditionally trauma has been mostly associated with criterion A of acute and posttraumatic stress disorders (PTSDs) as defined in the Diagnostic and Statistical Manual of Mental Disorders, many researchers find this definition too constraining and not accounting for the complexity and many aspects of trauma. This touched off a quest for a broader more accommodating trauma concept, and a dimensional view of trauma with PTSD as its extreme manifestation has been suggested. The dimensional view also has its detractors arguing that "conceptual bracket creep" may undermine the category's utility. Both categorical and dimensional views mostly rely on trauma's clinical phenomenology and lack a unified theoretical basis. In an attempt to reconcile this contradiction, a hybrid categorical-dimensional model of trauma based on the general theory of stress has been recently proposed (Krupnik, 2019). Herein, I explore the categorical boundary of the trauma concept, as posited by the model, within the predictive processing framework (PPF). I integrate the PPF view with the theory of stress. In conclusion, I briefly discuss how the proposed model of trauma may guide clinical practice.

Altered large-scale functional brain organization in posttraumatic stress disorder: A comprehensive review of univariate and network-level neurocircuitry models of PTSD

Classical neural circuitry models of posttraumatic stress disorder (PTSD) are largely derived from univariate activation studies and implicate the fronto-limbic circuit as a main neural correlate of PTSD symptoms. Though well-supported by human neuroimaging literature, these models are limited in their ability to explain the widely distributed neural and behavioral deficits in PTSD. Emerging interest in the application of large-scale network methods to functional neuroimaging provides a new opportunity to overcome such limitations and conceptualize the neural circuitry of PTSD in the context of network patterns. This review aims to evaluate both the classical neural circuitry model and a new, network-based model of PTSD neural circuitry using a breadth of functional brain organization research in subjects with PTSD. Taken together, this literature suggests global patterns of reduced functional connectivity (FC) in PTSD groups as well as altered FC targets that reside disproportionately in canonical functional networks, especially the default mode network. This provides evidence for an integrative model that includes elements of both the classical models and network-based models to characterize the neural circuitry of PTSD.

Animal models of liability to post-traumatic stress disorder: going beyond fear memory

In this review, we advocate a dimensional approach on the basis of candidate endophenotypes to the development of animal models of post-traumatic stress disorder (PTSD) capable of including genetic liability factors, variations in symptoms profile and underlying neurobiological mechanisms, and specific comorbidities. Results from the clinical literature pointed to two candidate endophenotypes of PTSD: low sensory gating and high waiting impulsivity. Findings of comparative studies in mice of two inbred strains characterized by different expressions of the two candidate endophenotypes showed different strain-specific neural and behavioral effects of stress experiences. Thus, mice of the standard C57BL/6J strain show stress-induced helplessness, stress-learned helplessness, and stress-extinction-resistant conditioned freezing. Instead, mice of the genetically unrelated DBA/2J strain, expressing both candidate endophenotypes, show stress-induced extinction-resistant avoidance and neural and behavioral phenotypes promoted by prolonged exposure to addictive drugs. These strain differences are in line with evidence of associations between genetic variants and specific stress-promoted pathological profiles in PTSD, support a role of genotype in determining different PTSD comorbidities, and offer the means to investigate specific pathogenic processes.

Sex Differences in Remote Contextual Fear Generalization in Mice

The generalization of fear is adaptive in that it allows an animal to respond appropriately to novel threats that are not identical to previous experiences. In contrast, the overgeneralization of fear is maladaptive and is a hallmark of post-traumatic stress disorder (PTSD), a psychiatric illness that is characterized by chronic symptomatology and a higher incidence in women compared to men. Therefore, understanding the neural basis of fear generalization at remote time-points in female animals is of particular translational relevance. However, our understanding of the neurobiology of fear generalization is largely restricted to studies employing male mice and focusing on recent time-points (i.e., within 24-48 h following conditioning). To address these limitations, we examined how male and female mice generalize contextual fear at remote time intervals (i.e., 3 weeks after conditioning). In agreement with earlier studies of fear generalization at proximal time-points, we find that the test order of training and generalization contexts is a critical determinant of generalization and context discrimination, particularly for female mice. However, tactile elements that are present during fear conditioning are more salient for male mice. Our study highlights long-term sex differences in defensive behavior between male and female mice and may provide insight into sex differences in the processing and retrieval of remote fear memory observed in humans.

Diminished positive affect and traumatic stress: A biobehavioral review and commentary on trauma affective neuroscience

Post-traumatic stress manifests in disturbed affect and emotion, including exaggerated severity and frequency of negative valence emotions, e.g., fear, anxiety, anger, shame, and guilt. However, another core feature of common post-trauma psychopathologies, i.e. post-traumatic stress disorder (PTSD) and major depression, is diminished positive affect, or reduced frequency and intensity of positive emotions and affective states such as happiness, joy, love, interest, and desire/capacity for interpersonal affiliation. There remains a stark imbalance in the degree to which the neuroscience of each affective domain has been probed and characterized in PTSD, with our knowledge of post-trauma diminished positive affect remaining comparatively underdeveloped. This remains a prominent barrier to realizing the clinical breakthroughs likely to be afforded by the increasing availability of neuroscience assessment and intervention tools. In this review and commentary, the author summarizes the modest extant neuroimaging literature that has probed diminished positive affect in PTSD using reward processing behavioral paradigms, first briefly reviewing and outlining the neurocircuitry implicated in reward and positive emotion and its interrelationship with negative emotion and negative valence circuitry. Specific research guidelines are then offered to best and most efficiently develop the knowledge base in this area in a way that is clinically translatable and will exert a positive impact on routine clinical care. The author concludes with the prediction that the development of an integrated, bivalent theoretical and predictive model of how trauma impacts affective neurocircuitry to promote post-trauma psychopathology will ultimately lead to breakthroughs in how trauma treatments are conceptualized mechanistically and developed pragmatically.

Differential Roles of the Salience Network During Prediction Error Encoding and Facial Emotion Processing Among Female Adolescent Assault Victims

BACKGROUND

Early-life assaultive violence exposure is a potent risk factor for posttraumatic stress disorder (PTSD) and other mood and anxiety disorders. Neurocircuitry models posit that increased risk is mediated by heightened emotion processing in a salience network including the dorsal anterior cingulate cortex, anterior insula, and amygdala. However, the processes of reinforcement learning (RL) also engage the salience network and are implicated in responses to early-life trauma and PTSD. To define their relative roles in response to early-life trauma and PTSD symptoms, the current study compared engagement of the salience network during emotion processing and RL as a function of early-life assault exposure.

METHODS

Adolescent girls (n = 30 girls who had previously been physically or sexually assaulted; n = 30 healthy girls for comparison) 11 to 17 years of age completed two types of tasks during functional magnetic resonance imaging: a facial emotion processing task and an RL task using either social or nonsocial stimuli. Independent component analysis was used to identify a salience network and characterize its engagement in response to emotion processing and prediction error encoding during the RL tasks.

RESULTS

Assault was related to greater reactivity of the salience network during emotion processing. By contrast, we found lesser encoding of negative prediction errors in the salience network, particularly during the social RL task, in girls who had been assaulted. The dysfunction of salience network activity during emotion processing and prediction error encoding was not associated with PTSD symptoms.

CONCLUSIONS

These results suggest that hyper- versus hypoactivity of the salience network among trauma-exposed youths depends on the cognitive-affective domain.