Single-subject anxiety treatment outcome prediction using functional neuroimaging

Emotion regulation in mood and anxiety disorders: A meta-analysis of fMRI cognitive reappraisal studies

Emotion regulation by means of cognitive reappraisal has been widely studied with functional magnetic resonance imaging (fMRI). To date, several meta-analyses of studies using cognitive reappraisal tasks in healthy volunteers have been carried out, but no meta-analyses have yet been performed on the fMRI data of clinical populations with identified alterations in emotion regulation capacity. We provide a comprehensive meta-analysis of cognitive reappraisal fMRI studies in populations of patients with mood or anxiety disorders, yielding a pooled sample of 247 patients and 262 controls from thirteen independent studies. As a distinguishing feature of this meta-analysis, original statistical brain maps were obtained from six of these studies. Our primary results demonstrated that patients with mood and anxiety disorders recruited the regulatory fronto-parietal network involved in cognitive reappraisal to a lesser extent in comparison to healthy controls. Conversely, they presented increased activation in regions that may be associated with the emotional experience (i.e., insula, cerebellum, precentral and inferior occipital gyri) and in regions whose activation may be the consequence of compensatory mechanisms (i.e., supramarginal gyri and superior parietal lobule). Moreover, activations in the left ventrolateral prefrontal cortex and the left superior temporal gyrus were associated with reinterpretation emotion regulation strategies, whereas medial frontal and parietal activations were associated with the deployment of distancing strategies. The regions revealed by this meta-analysis conform to a pattern of dysfunctional brain activation during cognitive reappraisal common to mood and anxiety disorders. As such, this neural pattern may reflect a transdiagnostic feature of these disorders.

Epigenetic and Neural Circuitry Landscape of Psychotherapeutic Interventions

The science behind psychotherapy has garnered considerable interest, as objective measures are being developed to map the patient's subjective change over the course of treatment. Prenatal and early life influences have a lasting impact on how genes are expressed and the manner in which neural circuits are consolidated. Transgenerationally transmitted epigenetic markers as well as templates of enhanced thought flexibility versus evasion can be passed down from parent to child. This influences gene expression/repression (impacting neuroplasticity) and kindling of neurocircuitry which can perpetuate maladaptive cognitive processing seen in a number of psychiatric conditions. Importantly, genetic factors and the compounding effects of early life adversity do not inexorably lead to certain fated outcomes. The concepts of vulnerability and resilience are becoming more integrated into the framework of "differential susceptibility," speaking to how corrective environmental factors may promote epigenetic change and reconfigure neural templates, allowing for symptomatic improvement. Psychotherapy is one such factor, and this review will focus on our current knowledge of its epigenetic and neurocircuitry impact.

Inference in the age of big data: Future perspectives on neuroscience

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.

Predicting cognitive behavioral therapy response in social anxiety disorder with anterior cingulate cortex and amygdala during emotion regulation

Background

Cognitive Behavioral Therapy (CBT) for social anxiety disorder (SAD) and other internalizing conditions attempts to improve emotion regulation. Accumulating data indicate anterior cingulate cortex (ACC), and to a lesser extent amygdala, activation in various tasks predicts treatment outcome. However, little is known about ACC and amygdala activation to emotion regulation in predicting clinical improvement following CBT in SAD.

Methods

Before treatment, 38 SAD patients completed implicit and explicit emotion regulation paradigms during fMRI. Implicit regulation involved attentional control over negative distractors. Explicit regulation comprised cognitive reappraisal to negative images. Pre-CBT brain activity was circumscribed to anatomical-based ACC sub-regions (rostral, dorsal) and amygdala masks, which were submitted to ROC curves to examine predictive validity as well as correlational analysis to evaluate prognostic change in symptom severity.

Results

More rostral (rACC) activity in implicit regulation and less rACC activity during explicit regulation distinguished responders (34%) from non-responders. Greater amygdala response in implicit regulation also foretold responder status. Baseline rACC and amygdala activity during attentional control correlated with pre-to-post CBT change in symptom severity such that more activation was related to greater decline in symptoms. No significant correlations were observed for explicit regulation.

Conclusions

Across forms of regulation, rACC activity predicted responder status whereas amygdala as a neuromarker was limited to implicit regulation. While the direction of effects (enhanced vs. reduced) in rACC activity was task-dependent, results suggest SAD patients with deficient regulation benefited more from CBT. Findings support previous studies involving patients with depression and suggest the rACC may be a viable marker of clinical improvement in SAD.

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Anterior cingulate cortex is a replicated treatment neuromarker in depression.

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Cognitive behavioral therapy (CBT) is evidence-based psychotherapy for social phobia.

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CBT attempts to improve emotion regulation ability.

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Baseline anterior cingulate cortex activity in regulation predicted CBT response.

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Baseline amygdala activity during regulation also predicted CBT response.

Emotion regulation related neural predictors of cognitive behavioral therapy response in social anxiety disorder

Social anxiety disorder (SAD) is characterized by aberrant prefrontal activity during reappraisal, an adaptive cognitive approach aimed at downregulating the automatic response evoked by a negative event. Cognitive behavioral therapy (CBT) is first-line psychotherapy for SAD, however, many remain symptomatic after treatment indicating baseline individual differences in neurofunctional activity may factor into CBT outcome. An emotion regulation strategy practiced in CBT is cognitive restructuring, a proxy for reappraisal. Therefore, neural response during reappraisal may serve as a brain-based predictor of CBT success. Prior to 12weeks of individual CBT, 34 patients with SAD completed a validated emotion regulation task during fMRI. Task instructions included 'Reappraise,' that is, use a cognitive approach to reduce affective state to a negative image, which was contrasted with looking at a negative image ('Look'). Regression results for Reappraise (vs. Look) revealed greater reduction in symptom severity was predicted by less pre-CBT activation in the dorsolateral prefrontal cortex (DLPFC). Regarding predictive validity, DLPFC significantly classified responder status. Post-hoc analysis revealed DLPFC activity, but not demographic data, baseline clinical measures, or reappraisal-related affective state during fMRI, significantly accounted for the variance in symptom reduction. Findings indicate patients with SAD are more likely to benefit from CBT if there is less pre-treatment DLPFC recruitment, a region strongly implicated in emotion regulation. Patients with reduced baseline frontal activation when reappraising negative stimuli may be especially helped by explicit cognitive interventions. Further research is necessary to establish DLPFC as a stable brain-based marker of treatment outcome.

What good are positive emotions for treatment? Trait positive emotionality predicts response to Cognitive Behavioral Therapy for anxiety

OBJECTIVE

Cognitive behavioral therapy (CBT) is empirically supported for the treatment of anxiety disorders; however, not all individuals achieve recovery following CBT. Positive emotions serve a number of functions that theoretically should facilitate response to CBT - they promote flexible patterns of information processing and assimilation of new information, encourage approach-oriented behavior, and speed physiological recovery from negative emotions. We conducted a secondary analysis of an existing clinical trial dataset to test the a priori hypothesis that individual differences in trait positive emotions would predict CBT response for anxiety.

METHOD

Participants meeting diagnostic criteria for panic disorder (n = 28) or generalized anxiety disorder (n = 31) completed 10 weekly individual CBT sessions. Trait positive emotionality was assessed at pre-treatment, and severity of anxiety symptoms and associated impairment was assessed throughout treatment.

RESULTS

Participants who reported a greater propensity to experience positive emotions at pre-treatment displayed the largest reduction in anxiety symptoms as well as fewer symptoms following treatment. Positive emotions remained a robust predictor of change in symptoms when controlling for baseline depression severity.

CONCLUSIONS

Initial evidence supports the predictive value of trait positive emotions as a prognostic indicator for CBT outcome in a GAD and PD sample.

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.

The Neurobiology of Panic: A Chronic Stress Disorder

Panic disorder is an often chronic and impairing human anxiety syndrome, which frequently results in serious psychiatric and medical comorbidities. Although, to date, there have been many advances in the diagnosis and treatment of panic disorder, its pathophysiology still remains to be elucidated. In this review, recent evidence for a neurobiological basis of panic disorder is reviewed with particular attention to risk factors such as genetic vulnerability, chronic stress, and temperament. In addition, neuroimaging data are reviewed which provides support for the concept of panic disorder as a fear network disorder. The potential impact of the National Institute of Mental Health Research Domain Criteria constructs of acute and chronic threats responses and their implications for the neurobiology of panic disorder are also discussed.

Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia

BACKGROUND

The approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG) often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI) interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT) in PD/AG.

METHODS

This analysis is based on pretreatment fMRI data during an interoceptive challenge from a multicenter trial of the German PANIC-NET. Patients with DSM-IV PD/AG were dichotomized as responders (n = 30) or non-responders (n = 29) based on the primary outcome (Hamilton Anxiety Scale Reduction ≥50%) after 6 weeks of CBT (2 h/week). fMRI parametric maps were used as features for response classification with linear support vector machines (SVM) with or without automated feature selection. Predictive accuracies were assessed using cross validation and permutation testing. The influence of methodological parameters and the predictive ability for specific interoception-related symptom reduction were further evaluated.

RESULTS

SVM did not reach sufficient overall predictive accuracies (38.0-54.2%) for anxiety reduction in the primary outcome. In the exploratory analyses, better accuracies (66.7%) were achieved for predicting interoception-specific symptom relief as an alternative outcome domain. Subtle information regarding this alternative response criterion but not the primary outcome was revealed by post hoc univariate comparisons.

CONCLUSION

In contrast to reports on other neurofunctional probes, SVM based on an interoception paradigm was not able to reliably predict individual response to CBT. Results speak against the clinical applicability of this technique.

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding - especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) - Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0-5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars.

A Roadmap for the Development of Applied Computational Psychiatry

BACKGROUND

Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered.

METHODS

Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational-psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice.

RESULTS

Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients.

CONCLUSIONS

A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.

Biological markers for anxiety disorders, OCD and PTSD - a consensus statement. Part I: Neuroimaging and genetics

OBJECTIVES

Biomarkers are defined as anatomical, biochemical or physiological traits that are specific to certain disorders or syndromes. The objective of this paper is to summarise the current knowledge of biomarkers for anxiety disorders, obsessive-compulsive disorder (OCD) and post-traumatic stress disorder (PTSD).

METHODS

Findings in biomarker research were reviewed by a task force of international experts in the field, consisting of members of the World Federation of Societies for Biological Psychiatry Task Force on Biological Markers and of the European College of Neuropsychopharmacology Anxiety Disorders Research Network.

RESULTS

The present article (Part I) summarises findings on potential biomarkers in neuroimaging studies, including structural brain morphology, functional magnetic resonance imaging and techniques for measuring metabolic changes, including positron emission tomography and others. Furthermore, this review reports on the clinical and molecular genetic findings of family, twin, linkage, association and genome-wide association studies. Part II of the review focuses on neurochemistry, neurophysiology and neurocognition.

CONCLUSIONS

Although at present, none of the putative biomarkers is sufficient and specific as a diagnostic tool, an abundance of high-quality research has accumulated that will improve our understanding of the neurobiological causes of anxiety disorders, OCD and PTSD.

Neural activity during object perception in schizophrenia patients is associated with illness duration and affective symptoms

BACKGROUND

Abnormalities in visual processes have been observed in schizophrenia patients and have been associated with alteration of the lateral occipital complex and visual cortex. However, the relationship of these abnormalities with clinical symptomatology is largely unknown.

METHODS

We investigated the brain activity associated with object perception in schizophrenia. Pictures of common objects were presented to 26 healthy participants (age=36.9; 11 females) and 20 schizophrenia patients (age=39.9; 8 females) in an fMRI study.

RESULTS

In the healthy sample the presentation of pictures yielded significant activation (pFWE (cluster)<0.001) of the bilateral fusiform gyrus, bilateral lingual gyrus, and bilateral middle occipital gyrus. In patients, the bilateral fusiform gyrus and bilateral lingual gyrus were significantly activated (pFWE (cluster)<0.001), but not so the middle occipital gyrus. However, significant bilateral activation of the middle occipital gyrus (pFWE (cluster)<0.05) was revealed when illness duration was controlled for. Depression was significantly associated with increased activation, and anxiety with decreased activation, of the right middle occipital gyrus and several other brain areas in the patient group. No association with positive or negative symptoms was revealed.

CONCLUSIONS

Illness duration accounts for the weak activation of the middle occipital gyrus in patients during picture presentation. Affective symptoms, but not positive or negative symptoms, influence the activation of the right middle occipital gyrus and other brain areas.

A systematic review of the neural bases of psychotherapy for anxiety and related disorders

Brain imaging studies over two decades have delineated the neural circuitry of anxiety and related disorders, particularly regions involved in fear processing and in obsessive-compulsive symptoms. The neural circuitry of fear processing involves the amygdala, anterior cingulate, and insular cortex, while cortico-striatal-thalamic circuitry plays a key role in obsessive-compulsive disorder. More recently, neuroimaging studies have examined how psychotherapy for anxiety and related disorders impacts on these neural circuits. Here we conduct a systematic review of the findings of such work, which yielded 19 functional magnetic resonance imaging studies examining the neural bases of cognitive-behavioral therapy (CBT) in 509 patients with anxiety and related disorders. We conclude that, although each of these related disorders is mediated by somewhat different neural circuitry, CBT may act in a similar way to increase prefrontal control of subcortical structures. These findings are consistent with an emphasis in cognitive-affective neuroscience on the potential therapeutic value of enhancing emotional regulation in various psychiatric conditions.

Biological predictors of pharmacological therapy in anxiety disorders

At least one third of patients with anxiety disorders do not adequately respond to available pharmacological treatment. The reason that some patients with anxiety disorders respond well, but others not, to the same classes of medication is not yet fully understood. It is suggested that several biological factors may influence treatment mechanisms in anxiety and therefore could be identified as possible biomarkers predicting treatment response. In this review, we look at current evidence exploring different types of treatment predictors, including neuroimaging, genetic factors, and blood-related measures, which could open up novel perspectives in clinical management of patients with anxiety disorders.

Imaging the neural effects of cognitive bias modification training

Cognitive bias modification (CBM) was first developed as an experimental tool to examine the causal role of cognitive biases, and later developed into complementary interventions in experimental psychopathology research. CBM involves the "re-training" of implicit biases by means of multiple trials of computerized tasks, and has been demonstrated to change anxious, depressive and drug-seeking behavior, including clinically relevant effects. Recently, the field has progressed by combining CBM with neuroimaging techniques, which provides insight into neural mechanisms underlying how CBM affects implicit biases in anxiety, depression, and addiction, and potentially other pathologies. This narrative literature review summarizes the state of the art of studies on the neural effects of CBM and provides directions for future research in the field. A total of 13 published studies were found and discussed: n=9 in anxiety, n=2 in depressive behavior, and n=2 in addiction.

Prefrontal Reactivity to Social Signals of Threat as a Predictor of Treatment Response in Anxious Youth

Neuroimaging has shown promise as a tool to predict likelihood of treatment response in adult anxiety disorders, with potential implications for clinical decision-making. Despite the relatively high prevalence and emergence of anxiety disorders in youth, very little work has evaluated neural predictors of response to treatment. The goal of the current study was to examine brain function during emotional face processing as a predictor of response to treatment in children and adolescents (age 7-19 years; N=41) with generalized, social, and/or separation anxiety disorder. Prior to beginning treatment with the selective serotonin reuptake inhibitor (SSRI) sertraline or cognitive behavior therapy (CBT), participants completed an emotional faces matching task during functional magnetic resonance imaging (fMRI). Whole brain responses to threatening (ie, angry and fearful) and happy faces were examined as predictors of change in anxiety severity following treatment. Greater activation in inferior and superior frontal gyri, including dorsolateral prefrontal cortex and ventrolateral prefrontal cortex, as well as precentral/postcentral gyri during processing of threatening faces predicted greater response to CBT and SSRI treatment. For processing of happy faces, activation in postcentral gyrus was a significant predictor of treatment response. Post-hoc analyses indicated that effects were not significantly moderated by type of treatment. Findings suggest that greater activation in prefrontal regions involved in appraising and regulating responses to social signals of threat predict better response to SSRI and CBT treatment in anxious youth and that neuroimaging may be a useful tool for predicting how youth will respond to treatment.

Japan Unified Protocol Clinical Trial for Depressive and Anxiety Disorders (JUNP study): study protocol for a randomized controlled trial

BACKGROUND

The unified protocol for the transdiagnostic treatment of emotional disorders is a promising treatment approach that could be applicable to a broad range of mental disorders, including depressive, anxiety, trauma-related, and obsessive-compulsive disorders. However, no randomized controlled trial has been conducted to verify the efficacy of the unified protocol on the heterogeneous clinical population with depressive and anxiety disorders.

METHODS/DESIGN

The trial was designed as a single-center, assessor-blinded, randomized, 20-week, parallel-group superiority study in order to compare the efficacy of the combination of unified protocol and treatment-as-usual versus waiting-list with treatment-as-usual for patients with depressive and/or anxiety disorders. The primary outcome was depression at 21 weeks, assessed by the 17-item version of the GRID-Hamilton Rating Scale for Depression. Estimated minimum sample size was 27 participants in each group. We will also examine the treatment mechanisms, treatment processes, and neuropsychological correlates.

DISCUSSION

The results of this study will clarify the efficacy of the unified protocol for depressive and anxiety disorders, and the treatment mechanism, process, and neurological correlates for the effectiveness of the unified protocol. If its efficacy can be confirmed, the unified protocol may be of high clinical value for Japan, a country in which cognitive behavioral treatment has not yet been widely adopted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02003261 (registered on December 2, 2013).

Functional neuroimaging of psychotherapeutic processes in anxiety and depression: from mechanisms to predictions

PURPOSE OF REVIEW

The review provides an update of functional neuroimaging studies that identify neural processes underlying psychotherapy and predict outcomes following psychotherapeutic treatment in anxiety and depressive disorders. Following current developments in this field, studies were classified as 'mechanistic' or 'predictor' studies (i.e., informing neurobiological models about putative mechanisms versus aiming to provide predictive information).

RECENT FINDINGS

Mechanistic evidence points toward a dual-process model of psychotherapy in anxiety disorders with abnormally increased limbic activation being decreased, while prefrontal activity is increased. Partly overlapping findings are reported for depression, albeit with a stronger focus on prefrontal activation following treatment. No studies directly comparing neural pathways of psychotherapy between anxiety and depression were detected. Consensus is accumulating for an overarching role of the anterior cingulate cortex in modulating treatment response across disorders. When aiming to quantify clinical utility, the need for single-subject predictions is increasingly recognized and predictions based on machine learning approaches show high translational potential.

SUMMARY

Present findings encourage the search for predictors providing clinically meaningful information for single patients. However, independent validation as a crucial prerequisite for clinical use is still needed. Identifying nonresponders a priori creates the need for alternative treatment options that can be developed based on an improved understanding of those neural mechanisms underlying effective interventions.

Altered Statistical Learning and Decision-Making in Methamphetamine Dependence: Evidence from a Two-Armed Bandit Task

Understanding how humans weigh long-term and short-term goals is important for both basic cognitive science and clinical neuroscience, as substance users need to balance the appeal of an immediate high vs. the long-term goal of sobriety. We use a computational model to identify learning and decision-making abnormalities in methamphetamine-dependent individuals (MDI, n = 16) vs. healthy control subjects (HCS, n = 16), in a two-armed bandit task. In this task, subjects repeatedly choose between two arms with fixed but unknown reward rates. Each choice not only yields potential immediate reward but also information useful for long-term reward accumulation, thus pitting exploration against exploitation. We formalize the task as comprising a learning component, the updating of estimated reward rates based on ongoing observations, and a decision-making component, the choice among options based on current beliefs and uncertainties about reward rates. We model the learning component as iterative Bayesian inference (the Dynamic Belief Model), and the decision component using five competing decision policies: Win-stay/Lose-shift (WSLS), ε-Greedy, τ-Switch, Softmax, Knowledge Gradient. HCS and MDI significantly differ in how they learn about reward rates and use them to make decisions. HCS learn from past observations but weigh recent data more, and their decision policy is best fit as Softmax. MDI are more likely to follow the simple learning-independent policy of WSLS, and among MDI best fit by Softmax, they have more pessimistic prior beliefs about reward rates and are less likely to choose the option estimated to be most rewarding. Neurally, MDI's tendency to avoid the most rewarding option is associated with a lower gray matter volume of the thalamic dorsal lateral nucleus. More broadly, our work illustrates the ability of our computational framework to help reveal subtle learning and decision-making abnormalities in substance use.

Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use

Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction.

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.

Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience

Neuroimaging has greatly enhanced the cognitive neuroscience understanding of the human brain and its variation across individuals (neurodiversity) in both health and disease. Such progress has not yet, however, propelled changes in educational or medical practices that improve people's lives. We review neuroimaging findings in which initial brain measures (neuromarkers) are correlated with or predict future education, learning, and performance in children and adults; criminality; health-related behaviors; and responses to pharmacological or behavioral treatments. Neuromarkers often provide better predictions (neuroprognosis), alone or in combination with other measures, than traditional behavioral measures. With further advances in study designs and analyses, neuromarkers may offer opportunities to personalize educational and clinical practices that lead to better outcomes for people.

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with SAD 1 year after completion of Internet-delivered CBT (iCBT). Twenty-six participants diagnosed with SAD underwent iCBT including attention bias modification for a total of 13 weeks. Support vector machines (SVMs), a supervised pattern recognition method allowing predictions at the individual level, were trained to separate long-term treatment responders from nonresponders based on blood oxygen level-dependent (BOLD) responses to self-referential criticism. The Clinical Global Impression-Improvement scale was the main instrument to determine treatment response at the 1-year follow-up. Results showed that the proportion of long-term responders was 52% (12/23). From multivariate BOLD responses in the dorsal anterior cingulate cortex (dACC) together with the amygdala, we were able to predict long-term response rate of iCBT with an accuracy of 92% (confidence interval 95% 73.2-97.6). This activation pattern was, however, not predictive of improvement in the continuous Liebowitz Social Anxiety Scale-Self-report version. Follow-up psychophysiological interaction analyses revealed that lower dACC-amygdala coupling was associated with better long-term treatment response. Thus, BOLD response patterns in the fear-expressing dACC-amygdala regions were highly predictive of long-term treatment outcome of iCBT, and the initial coupling between these regions differentiated long-term responders from nonresponders. The SVM-neuroimaging approach could be of particular clinical value as it allows for accurate prediction of treatment outcome at the level of the individual.

Toward the application of functional neuroimaging to individualized treatment for anxiety and depression

Functional neuroimaging has led to significant gains in understanding the biological bases of anxiety and depressive disorders. However, the ability of functional neuroimaging to directly impact clinical practice is unclear. One important method by which neuroimaging could impact clinical care is to generate single patient level predictions that can guide clinical decision-making. The present review summarizes published functional neuroimaging studies of predictors of medication or psychotherapy outcome in major depressive disorder, obsessive-compulsive disorder (OCD), posttraumatic stress disorder, generalized anxiety disorder, panic disorder, and social anxiety disorder. In major depressive disorder and OCD, there is converging evidence of specific brain circuitry that has both been implicated in the disordered state itself, and where pretreatment activation levels have been predictive of treatment response. Specifically, in major depressive disorder, greater pretreatment ventral and pregenual anterior cingulate cortex (ACC) activation may predict better antidepressant medication outcome but poorer psychotherapy outcome. In OCD, activation in the ACC and orbitofrontal cortex has been inversely associated with pharmacological treatment response. In other anxiety disorders, research in this area is just beginning, with the ACC potentially implicated. However, the question of whether these results can directly translate to clinical practice remains open. In order to achieve the goal of single patient level prediction and individualized treatment, future research should strive to establish replicable models with good predictive performance and clear incremental validity.

Neural changes with attention bias modification for anxiety: a randomized trial

Attention bias modification (ABM) procedures typically reduce anxiety symptoms, yet little is known about the neural changes associated with this behavioral treatment. Healthy adults with high social anxiety symptoms (n = 53) were randomized to receive either active or placebo ABM. Unlike placebo ABM, active ABM aimed to train individuals' attention away from threat. Using the dot-probe task, threat-related attention bias was measured during magnetic resonance imaging before and after acute and extended training over 4 weeks. A subset of participants completed all procedures (n = 30, 15 per group). Group differences in neural activation were identified using standard analyses. Linear regression tested predictive factors of symptom reduction (i.e., training group, baseline indices of threat bias). The active and placebo groups exhibited different patterns of right and left amygdala activation with training. Across all participants irrespective of group, individuals with greater left amygdala activation in the threat-bias contrast prior to training exhibited greater symptom reduction. After accounting for baseline amygdala activation, greater symptom reduction was associated with assignment to the active training group. Greater left amygdala activation at baseline predicted reductions in social anxiety symptoms following ABM. Further research is needed to clarify brain-behavior mechanisms associated with ABM training.

Neural response during attentional control and emotion processing predicts improvement after cognitive behavioral therapy in generalized social anxiety disorder

BACKGROUND

Individuals with generalized social anxiety disorder (gSAD) exhibit attentional bias to salient stimuli, which is reduced in patients whose symptoms improve after treatment, indicating that mechanisms of bias mediate treatment success. Therefore, pre-treatment activity in regions implicated in attentional control over socio-emotional signals (e.g. anterior cingulate cortex, dorsolateral prefrontal cortex) may predict response to cognitive behavioral therapy (CBT), evidence-based psychotherapy for gSAD.

METHOD

During functional magnetic resonance imaging, 21 participants with gSAD viewed images comprising a trio of geometric shapes (circles, rectangles or triangles) alongside a trio of faces (angry, fearful or happy) within the same field of view. Attentional control was evaluated with the instruction to 'match shapes', directing attention away from faces, which was contrasted with 'match faces', whereby attention was directed to emotional faces.

RESULTS

Whole-brain voxel-wise analyses showed that symptom improvement was predicted by enhanced pre-treatment activity in the presence of emotional face distractors in the dorsal anterior cingulate cortex and dorsal medial prefrontal cortex. Additionally, CBT success was foretold by less activity in the amygdala and/or increased activity in the medial orbitofrontal gyrus during emotion processing.

CONCLUSIONS

CBT response was predicted by pre-treatment activity in prefrontal regions and the amygdala. The direction of activity suggests that individuals with intact attentional control in the presence of emotional distractors, regulatory capacity over emotional faces and/or less reactivity to such faces are more likely to benefit from CBT. Findings indicate that baseline neural activity in the context of attentional control and emotion processing may serve as a step towards delineating mechanisms by which CBT exerts its effects.

Serotonin transporter [corrected] methylation and response to cognitive behaviour therapy in children with anxiety disorders

Anxiety disorders that are the most commonly occurring psychiatric disorders in childhood, are associated with a range of social and educational impairments and often continue into adulthood. Cognitive behaviour therapy (CBT) is an effective treatment option for the majority of cases, although up to 35-45% of children do not achieve remission. Recent research suggests that some genetic variants may be associated with a more beneficial response to psychological therapy. Epigenetic mechanisms such as DNA methylation work at the interface between genetic and environmental influences. Furthermore, epigenetic alterations at the serotonin transporter (SERT) promoter region have been associated with environmental influences such as stressful life experiences. In this study, we measured DNA methylation upstream of SERT in 116 children with an anxiety disorder, before and after receiving CBT. Change during treatment in percentage DNA methylation was significantly different in treatment responders vs nonresponders. This effect was driven by one CpG site in particular, at which responders increased in methylation, whereas nonresponders showed a decrease in DNA methylation. This is the first study to demonstrate differences in SERT methylation change in association with response to a purely psychological therapy. These findings confirm that biological changes occur alongside changes in symptomatology following a psychological therapy such as CBT.

Serotonin transporter [corrected] methylation and response to cognitive behaviour therapy in children with anxiety disorders

Anxiety disorders that are the most commonly occurring psychiatric disorders in childhood, are associated with a range of social and educational impairments and often continue into adulthood. Cognitive behaviour therapy (CBT) is an effective treatment option for the majority of cases, although up to 35-45% of children do not achieve remission. Recent research suggests that some genetic variants may be associated with a more beneficial response to psychological therapy. Epigenetic mechanisms such as DNA methylation work at the interface between genetic and environmental influences. Furthermore, epigenetic alterations at the serotonin transporter (SERT) promoter region have been associated with environmental influences such as stressful life experiences. In this study, we measured DNA methylation upstream of SERT in 116 children with an anxiety disorder, before and after receiving CBT. Change during treatment in percentage DNA methylation was significantly different in treatment responders vs nonresponders. This effect was driven by one CpG site in particular, at which responders increased in methylation, whereas nonresponders showed a decrease in DNA methylation. This is the first study to demonstrate differences in SERT methylation change in association with response to a purely psychological therapy. These findings confirm that biological changes occur alongside changes in symptomatology following a psychological therapy such as CBT.

Predicting rapid response to cognitive-behavioural treatment for panic disorder: the role of hippocampus, insula, and dorsolateral prefrontal cortex

Although cognitive-behavioural therapy (CBT) is an effective first-line intervention for anxiety disorders, treatments remain long and cost-intensive, difficult to access, and a subgroup of patients fails to show any benefits at all. This study aimed to identify functional and structural brain markers that predict a rapid response to CBT. Such knowledge will be important to establish the mechanisms underlying successful treatment and to develop more effective, shorter interventions. Fourteen unmedicated patients with panic disorder underwent 3 T functional and structural magnetic resonance imaging (MRI) before receiving four sessions of exposure-based CBT. Symptom severity was measured before and after treatment. During functional MRI, patients performed an emotion regulation task, either viewing negative images naturally, or intentionally down-regulating negative affect by using previously taught strategies of cognitive reappraisal. Structural MRI images were analysed including left and right segmentation and volume estimation. Improved response to brief CBT was predicted by increased pre-treatment activation in bilateral insula and left dorsolateral prefrontal cortex (dlPFC) during threat processing, as well as increased right hippocampal gray matter volume. Previous work links these regions to improved threat processing and fear memory activation, suggesting that the activation of such mechanisms is crucial for exposure-based CBT to be effective.

Psychotherapy and Neuroimaging

Technological advances in neuroimaging have enabled researchers to examine, in vivo, the relationship between psychotherapeutic interventions and markers of brain activity. This review focuses on two kinds of neuroimaging studies in psychotherapy: those that examine the patterns of brain activity associated with response to treatments and those that examine the changes that occur in brain activity during treatment. A general, hypothetical neural model of psychotherapy is presented, and support for the model is evaluated across anxiety disorders and major depression. Neuroimaging studies are broadly consistent in observing associations between response to psychotherapy and baseline activity in several key regions within the prefrontal cortex, basal ganglia, and limbic areas. These regions are involved in the generation and regulation of emotion, fear responding, and response to reward. Pre-post examinations of change following psychotherapy also typically observe that psychological treatments for anxiety and depression can affect neural activity in these regions. Despite general consensus that activity in these regions is associated with psychotherapy, substantial discrepancy persists regarding the precise direction of the observed relationships. Methodological challenges of the existing literature are considered, and future directions are discussed.