Tali M. Ball

Prefrontal dysfunction during emotion regulation in generalized anxiety and panic disorders

BACKGROUND The mechanisms that contribute to emotion dysregulation in anxiety disorders are not well understood. Two common disorders, generalized anxiety disorder (GAD) and panic disorder (PD), were examined to test the hypothesis that both disorders are characterized by hypo-activation in prefrontal cortex (PFC) during emotion regulation. A competing hypothesis that GAD in particular is characterized by PFC hyper-activation during emotion regulation (reflecting overactive top-down control) was also evaluated. Method Twenty-two medication-free healthy control (HC), 23 GAD, and 18 PD participants underwent functional magnetic resonance imaging (fMRI) during a task that required them to reappraise (i.e. reduce) or maintain emotional responses to negative images. RESULTS GAD participants reported the least reappraisal use in daily life, and reappraisal use was inversely associated with anxiety severity and functional impairment in these participants. During fMRI, HCs demonstrated greater activation during both reappraisal and maintenance than either GAD or PD participants (who did not differ) in brain areas important for emotion regulation (e.g. dorsolateral and dorsomedial PFC). Furthermore, across all anxious participants, activation during reappraisal in dorsolateral and dorsomedial PFC was inversely associated with anxiety severity and functional impairment. CONCLUSIONS Emotion dysregulation in GAD and PD may be the consequence of PFC hypo-activation during emotion regulation, consistent with insufficient top-down control. The relationship between PFC hypo-activation and functional impairment suggests that the failure to engage PFC during emotion regulation may be part of the critical transition from dispositionally high anxiety to an anxiety disorder.

Single-Subject Anxiety Treatment Outcome Prediction using Functional Neuroimaging

The possibility of individualized treatment prediction has profound implications for the development of personalized interventions for patients with anxiety disorders. Here we utilize random forest classification and pre-treatment functional magnetic resonance imaging (fMRI) data from individuals with generalized anxiety disorder (GAD) and panic disorder (PD) to generate individual subject treatment outcome predictions. Before cognitive behavioral therapy (CBT), 48 adults (25 GAD and 23 PD) reduced (via cognitive reappraisal) or maintained their emotional responses to negative images during fMRI scanning. CBT responder status was predicted using activations from 70 anatomically defined regions. The final random forest model included 10 predictors contributing most to classification accuracy. A similar analysis was conducted using the clinical and demographic variables. Activations in the hippocampus during maintenance and anterior insula, superior temporal, supramarginal, and superior frontal gyri during reappraisal were among the best predictors, with greater activation in responders than non-responders. The final fMRI-based model yielded 79% accuracy, with good sensitivity (0.86), specificity (0.68), and positive and negative likelihood ratios (2.73, 0.20). Clinical and demographic variables yielded poorer accuracy (69%), sensitivity (0.79), specificity (0.53), and likelihood ratios (1.67, 0.39). This is the first use of random forest models to predict treatment outcome from pre-treatment neuroimaging data in psychiatry. Together, random forest models and fMRI can provide single-subject predictions with good test characteristics. Moreover, activation patterns are consistent with the notion that greater activation in cortico-limbic circuitry predicts better CBT response in GAD and PD.

Selective effects of social anxiety, anxiety sensitivity, and negative affectivity on the neural bases of emotional face processing.

Individuals with high anxiety show heightened neural activation in affective processing regions, including the amygdala and insula. Activations have been shown to be correlated with anxiety severity, but although anxiety is a heterogeneous state, prior studies have not systematically disentangled whether neural activity in affective processing circuitry is uniquely related to specific domains of anxiety. Forty-five young adults were tested on an emotional face processing task during functional magnetic resonance imaging. Participants completed the Social Interactional Anxiety Scale, Anxiety Sensitivity Index, and Spielberger Trait Anxiety Inventory. Using a robust multiple regression approach, we examined the effects of social anxiety, anxiety sensitivity, and trait anxiety (which overlapped with depressive symptoms, and can therefore be considered a measure of negative affectivity) on activation in insula, amygdala, and medial prefrontal cortex, in response to emotional faces. Adjusting for negative affectivity and anxiety sensitivity, social anxiety was associated with activity in left amygdala, right insula, and subgenual anterior cingulate across all emotional faces. When comparing negative and positive faces directly, greater negative affectivity was uniquely associated with less activity to positive faces in left amygdala, left anterior insula, and dorsal anterior cingulate. The current findings support the hypothesis that hyperactivity in brain areas during general emotional face processing is predominantly a function of social anxiety. In comparison, hypoactivity to positively valenced faces was predominantly associated with negative affectivity. Implications for the understanding of emotion processing in anxiety are discussed.

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse

BACKGROUND Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. METHODS 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. RESULTS 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. CONCLUSIONS These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders.

Neural Predictors of Initiating Alcohol Use During Adolescence

OBJECTIVE Underage drinking is widely recognized as a leading public health and social problem for adolescents in the United States. Being able to identify at-risk adolescents before they initiate heavy alcohol use could have important clinical and public health implications; however, few investigations have explored individual-level precursors of adolescent substance use. This prospective investigation used machine learning with demographic, neurocognitive, and neuroimaging data in substance-naive adolescents to identify predictors of alcohol use initiation by age 18. METHOD Participants (N=137) were healthy substance-naive adolescents (ages 12-14) who underwent neuropsychological testing and structural and functional magnetic resonance imaging (sMRI and fMRI), and then were followed annually. By age 18, 70 youths (51%) initiated moderate to heavy alcohol use, and 67 remained nonusers. Random forest classification models identified the most important predictors of alcohol use from a large set of demographic, neuropsychological, sMRI, and fMRI variables. RESULTS Random forest models identified 34 predictors contributing to alcohol use by age 18, including several demographic and behavioral factors (being male, higher socioeconomic status, early dating, more externalizing behaviors, positive alcohol expectancies), worse executive functioning, and thinner cortices and less brain activation in diffusely distributed regions of the brain. CONCLUSIONS Incorporating a mix of demographic, behavioral, neuropsychological, and neuroimaging data may be the best strategy for identifying youths at risk for initiating alcohol use during adolescence. The identified risk factors will be useful for alcohol prevention efforts and in research to address brain mechanisms that may contribute to early drinking.

Brain activation during fear extinction predicts exposure success

Exposure therapy, a gold‐standard treatment for anxiety disorders, is assumed to work via extinction learning, but this has never been tested. Anxious individuals demonstrate extinction learning deficits, likely related to less ventromedial prefrontal cortex (vmPFC) and more amygdala activation, but the relationship between these deficits and exposure outcome is unknown. We tested whether anxious individuals who demonstrate better extinction learning report greater anxiety reduction following brief exposure.

Quantifying person-level brain network functioning to facilitate clinical translation

Although advances in neuroimaging have yielded insights into the intrinsic organization of human brain networks and their relevance to psychiatric and neurological disorders, there has been no translation of these insights into clinical practice. One necessary step toward clinical translation is identifying a summary metric of network function that is reproducible, reliable, and has known normative data, analogous to normed neuropsychological tests. Our aim was therefore to establish the proof of principle for such a metric, focusing on the default mode network (DMN). We compared three candidate summary metrics: global clustering coefficient, characteristic path length, and average connectivity. Across three samples totaling 322 healthy, mostly Caucasian adults, average connectivity performed best, with good internal consistency (Cronbach’s α=0.69–0.70) and adequate eight-week test–retest reliability (intra-class coefficient=0.62 in a subsample N=65). We therefore present normative data for average connectivity of the DMN and its sub-networks. These proof of principle results are an important first step for the translation of neuroimaging to clinical practice. Ultimately, a normed summary metric will allow a single patient’s DMN function to be quantified and interpreted relative to normative peers.

Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers

Default mode network (DMN) dysfunction (particularly within the anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC)) has been implicated in major depressive disorder (MDD); however, its contribution to treatment outcome has not been clearly established. Here we tested the role of DMN functional connectivity as a general and differential biomarker for predicting treatment outcomes in a large, unmedicated adult sample with MDD. Seventy-five MDD outpatients completed fMRI scans before and 8 weeks after randomization to escitalopram, sertraline, or venlafaxine-XR. A whole-brain voxel-wise t-test identified profiles of pretreatment intrinsic functional connectivity that distinguished patients who were subsequently classified as remitters or non-remitters at follow-up. Connectivity was seeded in the PCC, an important node of the DMN. We further characterized differences between remitters, non-remitters, and 31 healthy controls and characterized changes pretreatment to posttreatment. Remitters were distinguished from non-remitters by relatively intact connectivity between the PCC and ACC/mPFC, not distinguishable from healthy controls, while non-remitters showed relative hypo-connectivity. In validation analyses, we demonstrate that PCC–ACC/mPFC connectivity predicts remission status with >80% cross-validated accuracy. In analyses testing whether intrinsic connectivity differentially relates to outcomes for a specific type of antidepressant, interaction models did not survive the corrected threshold. Our findings demonstrate that the overall capacity to remit on commonly used antidepressants may depend on intact organization of intrinsic functional connectivity between PCC and ACC/mPFC prior to treatment. The findings highlight the potential utility of functional scans for advancing a more precise approach to tailoring antidepressant treatment choices.

Corrigendum to “Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse” [Drug and Alcohol Dependence 152 (2015) 93–101]

Author(s): Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P