Neural correlates of predictable and unpredictable threat in internalizing psychopathology

Sleep quality impacts the link between reactivity to uncertain threat and anxiety and alcohol use in youth

Individual differences in reactivity to unpredictable threat (U-threat) have repeatedly been linked to symptoms of anxiety and drinking behavior. An emerging theory is that individuals who are hyper-reactive to U-threat experience chronic anticipatory anxiety, hyperarousal, and are vulnerable to excessive alcohol use via negative reinforcement processes. Notably, anxiety and alcohol use commonly relate to disruptions in sleep behavior and recent findings suggest that sleep quality may impact the link between reactivity to U-threat and psychiatric symptoms and behaviors. The aim of the current study was to examine the unique and interactive effects of reactivity to U-threat and sleep quality on anxiety symptoms and drinking behavior in a cohort of youth, ages 16-19 years. Participants (N = 112) completed a well-validated threat-of-shock task designed to probe individual differences in reactivity to U-threat and predictable threat (P-threat). Startle eyeblink potentiation was recorded during the task as an index of aversive reactivity. Participants also completed well-validated self-report measures of anxiety and depression symptoms, lifetime alcohol use, and current sleep quality. Results revealed significant startle reactivity to U-threat by sleep quality interactions on anxiety symptoms and lifetime drinking behavior. At high levels of sleep disturbance (only), greater reactivity to U-threat was associated with greater anxiety symptoms and total number of lifetime alcoholic beverages. These results suggest that sensitivity to uncertainty and chronic hyperarousal increases anxiety symptoms and alcohol use behavior, particularly in the context of poor sleep quality.

Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination

BACKGROUND

Recent studies have begun to examine how signals in the brain correspond to the underlying white matter structure using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we applied this framework for the first time toward a transdiagnostic cohort of individuals with internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms.

METHODS

Both diffusion-weighted and resting-state functional magnetic resonance imaging were obtained from participants at baseline (n = 60 patients, n = 19 healthy control participants). Patients were randomized to 12 weeks of treatment with either a selective serotonin reuptake inhibitor or cognitive behavioral therapy, and symptom scales were readministered posttreatment (n = 46 patients at follow-up). Using graph signal processing methodology, we quantified the alignment of functional signals to their underlying white matter structural networks.

RESULTS

We found that signal alignment within the posterior DMN was decreased in patients with internalizing psychopathologies compared with healthy control participants and was inversely (negatively) correlated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also correlated with the ratio of total within-DMN to extra-DMN functional connectivity for these regions.

CONCLUSIONS

These findings are consistent with previous literature regarding pathological promiscuity of posterior DMN connectivity and provide the first graph signal processing-based analyses in a transdiagnostic cohort of patients with internalizing psychopathologies.

Abnormal hemispheric specialization and inter-hemispheric functional cooperation in generalized anxiety disorder

Abnormal hemispheric specialization and inter-hemispheric interactions may contribute to the pathogenesis of general anxiety disorder (GAD). The current study investigated these abnormalities in GAD patients based on the two analytic approaches and examined whether such abnormalities are correlated with anxiety symptom severity. Seventy-three patients with GAD and 60 matched healthy controls were recruited. All participants completed anxiety symptoms assessment and resting-state functional magnetic resonance imaging (rs-fMRI). The autonomy index (AI) and Connectivity between Functionally Homotopic voxels (CFH) were applied to measure and compared between groups. Compared to controls, patients showed stronger AI in the right middle temporal gyrus (MTG). Seed-based analysis revealed stronger functional connectivity (FC) of the right MTG with both right precuneus and right dorsolateral prefrontal cortex (dlPFC) in patients. Patients also exhibited greater CFH in right anterior cingulate cortex (ACC) but decreased CFH in bilateral postcentral gyrus (PCG) and superior occipital gyrus (SOG). Further there were significant correlations between these regional CFH and anxiety symptoms severity. GAD patients demonstrate right hemispheric specialization and aberrant inter-hemispheric functional cooperation, and abnormal inter-hemispheric coordination is associated with anxiety symptom severity. These findings provide a clue to understanding the neuropathological mechanisms of GAD.

Transdiagnostic Fear and Anxiety: Prospective Prediction Using the No-Threat, Predictable Threat, and Unpredictable Threat Task

Background

Fear and anxiety are distinct dimensions of psychopathology that may be characterized by differences in dimensional threat reactivity. Heightened response to predictable threat is hypothesized to underlie fear symptomatology, whereas increased response to unpredictable threat may underlie anxiety. Despite widespread acceptance of this model, these purported associations have rarely been tested, and the prognostic value of predictable and unpredictable threat responding is unclear. Here we examined multilevel indicators of predictable and unpredictable threat response as cross-sectional correlates and prospective predictors of transdiagnostic fear and anxiety.

Methods

Fifty-two individuals with varying levels of internalizing psychopathology (31 female) performed the no-threat, predictable threat, and unpredictable threat task. Transdiagnostic fear and anxiety were assessed at baseline (time 1) and approximately 1.5 years later (time 2). We used event-related potential, the stimulus-preceding negativity, as a measure of threat anticipation and startle eyeblink as a measure of defensive reactivity during the no-threat, predictable threat, and unpredictable threat task. These probes were assessed as cross-sectional correlates and prospective predictors of fear and anxiety.

Results

Participants with larger time 1 stimulus-preceding negativities to predictable threat were characterized by greater time 1 fear. Larger time 1 stimulus-preceding negativities to unpredictable threat were associated with greater increases in time 2 anxiety. Heightened time 1 startle to predictable threat predicted larger increases in time 2 fear.

Conclusions

Results validate predictable and unpredictable threat responding as dimensional correlates of transdiagnostic fear versus anxiety and suggest that psychophysiological measures of predictable and unpredictable threat response hold promise as prospective predictors of trajectories of fear and anxiety.

Association between startle reactivity to uncertain threats and structural brain volume

Sensitivity to uncertain threat (U-threat) is a clinically important individual difference factor in multiple psychopathologies. Recent studies have implicated a specific frontolimbic circuit as a key network involved in the anticipation of aversive stimuli. In particular, the insula, thalamus, and dorsal anterior cingulate cortex (dACC) have recently been found to be robustly activated by anticipation of U-threat. However, no study to date has examined the association between U-threat reactivity and structural brain volume. In the present study, we utilized a pooled sample of 186 young adult volunteers who completed a structural MRI scan and the well-validated No-Predictable-Unpredictable (NPU) threat of electric shock task. Startle eyeblink potentiation was collected during the NPU task as an objective index of aversive reactivity. ROI-based analyses revealed that increased startle reactivity to U-threat was associated with reduced gray matter volume in the right insula and bilateral thalamus, but not the dACC. These results add to a growing literature implicating the insula and thalamus as core nodes involved in individual differences in U-threat reactivity.

Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies

Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.

Neural and Self-report Measures of Sensitivity to Uncertainty as Predictors of COVID-Related Negative Affect

The COVID-19 pandemic has been a period of unprecedented uncertainty. Research indicates individuals differ in their response to uncertainty and these differences are mediated by anterior insula (aINS) function. Those most sensitive to uncertainty are likely vulnerable to negative affect in the context of the pandemic. The current study was designed to directly test this question using both neural and self-reported measures of sensitivity to uncertainty. Fifty-nine volunteers completed a task designed to probe neural response to anticipation of predictable (P-) and unpredictable (U-) threat-of-electric-shock during functional magnetic resonance imaging and a self-report measure of intolerance of uncertainty (IU). Approximately two years later, during the peak of the pandemic, participants reported their emotional reactions to the COVID-19 crisis. Multilevel mixed models revealed that greater aINS activation to U-threat and greater self-reported IU were independent predictors of increased COVID-related negative affect. These findings were significant when adjusting for biological sex and depression and anxiety symptom severity. The results add to a growing literature demonstrating that individual differences in response to uncertainty have a robust impact on mood and functioning. Results also highlight that individuals highly sensitive to uncertainty may be at increased risk for poor mental health during the ongoing pandemic.

Evaluating the criterion validity of hierarchical psychopathology dimensions across models: Familial aggregation and associations with research domain criteria (sub)constructs

The Hierarchical Taxonomy of Psychopathology (HiTOP) posits that psychopathology is a hierarchy of correlated dimensions. Numerous studies have examined the validity of these dimensions using bifactor models, in which each disorder loads onto both a general and specific factor (e.g., internalizing, externalizing). Although bifactor models tend to fit better than alternative models, concerns have been raised about bifactor model selection, factor reliability, and interpretability. Therefore, we compared the reliability and validity of several higher-order HiTOP dimensions between bifactor and correlated factor models using familial aggregation and associations with Research Domain Criteria (RDoC; sub)constructs as validators. Lifetime psychopathology was assessed in a community sample (N = 504) using dimensional disorder severity scales calculated from semistructured interview data. A series of unidimensional, correlated factor, and bifactor models were fit to model several HiTOP dimensions. A bifactor model with two specific factors (internalizing and disinhibited externalizing) and a correlated two-factor model provided the best fit to the data. HiTOP dimensions had adequate reliability in the correlated factor model, but suboptimal reliability in the bifactor model. The disinhibited externalizing dimension was highly correlated across the two models and was familial, yet largely unrelated to RDoC (sub)constructs in both models. The internalizing dimension in the correlated factor model and the general factor in the bifactor model were highly correlated and had similar validity patterns, suggesting the general factor was largely redundant with the internalizing dimension in the correlated factor model. These findings support concerns about the interpretability of psychopathology dimensions in bifactor models. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Maternal depression during pregnancy alters infant subcortical and midbrain volumes

BACKGROUND

Maternal depression in pregnancy increases the risk for adverse neurodevelopmental outcomes in the offspring. The reason for this is unknown, however, one plausible mechanism may include the impact of maternal antenatal depression on infant brain. Nevertheless, relatively few studies have examined the brain anatomy of infants born to clinically diagnosed mothers.

METHODS

A legacy magnetic resonance imaging (MRI) dataset was used to compare regional brain volumes in 3-to-6-month-old infants born to women with a clinically confirmed diagnosis of major depressive disorder (MDD) during pregnancy (n = 31) and a reference sample of infants born to women without a current or past psychiatric diagnosis (n = 33). A method designed for analysis of low-resolution scans enabled examination of subcortical and midbrain regions previously found to be sensitive to the parent-child environment.

RESULTS

Compared with infants of non-depressed mothers, infants exposed to maternal antenatal depression had significantly larger subcortical grey matter volumes and smaller midbrain volumes. There was no association between gestational medication exposure and the infant regional brain volumes examined in our sample.

LIMITATIONS

Our scanning approach did not allow for an examination of fine-grained structural differences, and without repeated measures of brain volume, it is unknown whether the direction of reported associations are dependent on developmental stage.

CONCLUSIONS

Maternal antenatal depression is associated with an alteration in infant brain anatomy in early postnatal life; and that this is not accounted for by medication exposure. However, our study cannot address whether anatomical differences impact on future outcomes of the offspring.

Linking RDoC and HiTOP: A new interface for advancing psychiatric nosology and neuroscience

The Research Domain Criteria (RDoC) and the Hierarchical Taxonomy of Psychopathology (HiTOP) represent major dimensional frameworks proposing two alternative approaches to accelerate progress in the way psychopathology is studied, classified, and treated. RDoC is a research framework rooted in neuroscience aiming to further the understanding of transdiagnostic biobehavioral systems underlying psychopathology and ultimately inform future classifications. HiTOP is a dimensional classification system, derived from the observed covariation among symptoms of psychopathology and maladaptive traits, which seeks to provide more informative research and treatment targets (i.e., dimensional constructs and clinical assessments) than traditional diagnostic categories. This article argues that the complementary strengths of RDoC and HiTOP can be leveraged in order to achieve their respective goals. RDoC's biobehavioral framework may help elucidate the underpinnings of the clinical dimensions included in HiTOP, whereas HiTOP may provide psychometrically robust clinical targets for RDoC-informed research. We present a comprehensive mapping between dimensions included in RDoC (constructs and subconstructs) and HiTOP (spectra and subfactors) based on narrative review of the empirical literature. The resulting RDoC-HiTOP interface sheds light on the biobehavioral correlates of clinical dimensions and provides a broad set of dimensional clinical targets for etiological and neuroscientific research. We conclude with future directions and practical recommendations for using this interface to advance clinical neuroscience and psychiatric nosology. Ultimately, we envision that this RDoC-HiTOP interface has the potential to inform the development of a unified, dimensional, and biobehaviorally-grounded psychiatric nosology.