Neurobiological markers predicting treatment response in anxiety disorders: A systematic review and implications for clinical application

Reward and Punishment Learning as Predictors of Cognitive Behavioral Therapy Response in Parkinson's Disease Comorbid with Clinical Depression

Depression is highly comorbid among individuals with Parkinson's Disease (PD), who often experience unique challenges to accessing and benefitting from empirically supported interventions like Cognitive Behavioral Therapy (CBT). Given the role of reward processing in both depression and PD, this study analyzed a subset (N = 25) of participants who participated in a pilot telemedicine intervention of PD-informed CBT, and also completed a Reward- and Punishment-Learning Task (RPLT) at baseline. At the conclusion of CBT, participants were categorized into treatment responders (n = 14) and non-responders (n = 11). Responders learned more optimally from negative rather than positive feedback on the RPLT, while this pattern was reversed in non-responders. Computational modeling suggested group differences in learning rate to negative feedback may drive the observed differences. Overall, the results suggest that a within-subject bias for punishment-based learning might help to predict response to CBT intervention for depression in those with PD.Plain Language Summary Performance on a Computerized Task may predict which Parkinson's Disease Patients benefit from Cognitive Behavioral Treatment of Clinical DepressionWhy was the study done? Clinical depression regularly arises in individuals with Parkinson's Disease (PD) due to the neurobiological changes with the onset and progression of the disease as well as the unique psychosocial difficulties associated with living with a chronic condition. Nonetheless, psychiatric disorders among individuals with PD are often underdiagnosed and likewise undertreated for a variety of reasons. The results of our study have implications about how to improve the accuracy and specificity of mental health treatment recommendations in the future to maximize benefits for individuals with PD, who often face additional barriers to accessing quality mental health treatment.What did the researchers do? We explored whether performance on a computerized task called the Reward- and Punishment-Learning Task (RPLT) helped to predict response to Cognitive Behavioral Therapy (CBT) for depression better than other predictors identified in previous studies. Twenty-five individuals with PD and clinical depression that completed a 10-week telehealth CBT program were assessed for: Demographics (Age, gender, etc.); Clinical information (PD duration, mental health diagnoses, levels of anxiety/depression, etc.); Neurocognitive performance (Memory, processing speed, impulse control, etc.); and RPLT performance.What did the researchers find? A total of 14 participants significantly benefitted from CBT treatment while 11 did not significantly benefit from treatment.There were no differences before treatment in the demographics, clinical information, and neurocognitive performance of those participants who ended up benefitting from the treatment versus those who did not.There were, however, differences before treatment in RPLT performance so that those individuals that benefitted from CBT seemed to learn better from negative feedback.What do the findings mean? Our results suggest that the CBT program benefitted those PD patients with clinical depression that seemed to overall learn best from avoiding punishment rather than obtaining reward which was targeted in CBT by focusing on increasing engagement in rewarding activities. The Reward- and Punishment-Learning Task hence may be a useful tool to help predict treatment response and provide more individualized recommendations on how to best maximize the benefits of psychotherapy for individuals with PD that may struggle to connect to mental health care. Caution is recommended about interpretating these results beyond this study as the overall number of participants was small and the data for this study were collected as part of a previous study so there was no opportunity to include additional measurements of interest.

Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia-a replication approach

Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

Self-focused brain predictors of cognitive behavioral therapy response in a transdiagnostic sample

BACKGROUND

Effective biomarkers of cognitive behavioral therapy (CBT) response provide information beyond available behavioral or self-report measures and may optimize treatment selection for patients based on likelihood of benefit. No single biomarker reliably predicts CBT response. In this study, we evaluated patterns of brain connectivity associated with self-focused attention (SFA) as biomarkers of CBT response for anxiety and obsessive-compulsive disorders. We hypothesized that pre-treatment as well as pre-to post-treatment changes in functional connectivity would be associated with improvement during CBT in a transdiagnostic sample.

METHODS

Twenty-seven patients with primary social anxiety disorder (n = 14) and primary body dysmorphic disorder (n = 13) were scanned before and after 12 sessions of CBT targeting their primary disorder. Eligibility was based on elevated trait SFA scores on the Public Self-Consciousness Scale. Seed-based resting state functional connectivity associated with symptom improvement was computed using a seed in the posterior cingulate cortex of the default mode network.

RESULTS

At pre-treatment, stronger positive connectivity of the seed with the cerebellum, and stronger negative connectivity with the putamen, were associated with greater clinical improvement. Between pre-to post-treatment, greater anticorrelation between the seed and postcentral gyrus, extending into the inferior parietal lobule and precuneus/superior parietal lobule was associated with clinical improvement, although this did not survive thresholding.

CONCLUSIONS

Pre-treatment functional connectivity with the default mode network was associated with CBT response. Behavioral and self-report measures of SFA did not contribute to predictions, thus highlighting the value of neuroimaging-based measures of SFA.

CLINICAL TRIALS REGISTRATION

ClinicalTrials.gov Identifier: NCT02808702 https://clinicaltrials.gov/ct2/show/NCT02808702.

Enhanced late positive potential to conditioned threat cue during delayed extinction in anxious youth

BACKGROUND

Deficits in threat learning relate to anxiety symptoms. Since several anxiety disorders arise in adolescence, impaired adolescent threat learning could contribute to adolescent changes in risk for anxiety. This study compared threat learning among anxious and non-anxious youth using self-reports, peripheral psychophysiology measures, and event-related potentials. Because exposure therapy, the first-line treatment for anxiety disorders, is largely based on principles of extinction learning, the study also examined the link between extinction learning and treatment outcomes among anxious youth.

METHODS

Clinically anxious (n = 28) and non-anxious (n = 33) youth completed differential threat acquisition and immediate extinction. They returned to the lab a week later to complete a threat generalization test and a delayed extinction task. Following these two experimental visits, anxious youth received exposure therapy for 12 weeks.

RESULTS

Anxious as compared to non-anxious youth demonstrated elevated cognitive and physiological responses across acquisition and immediate extinction learning, as well as greater threat generalization. In addition, anxious youth showed enhanced late positive potential response to the conditioned threat cue compared to the safety cue during delayed extinction. Finally, aberrant neural response during delayed extinction was associated with poorer treatment outcomes.

CONCLUSIONS

The study emphasizes differences between anxious and non-anxious youth in threat learning processes and provides preliminary support for a link between neural processing during delayed extinction and exposure-based treatment outcome in pediatric anxiety.

The definition of treatment resistance in anxiety disorders: a Delphi method-based consensus guideline

Anxiety disorders are very prevalent and often persistent mental disorders, with a considerable rate of treatment resistance which requires regulatory clinical trials of innovative therapeutic interventions. However, an explicit definition of treatment-resistant anxiety disorders (TR-AD) informing such trials is currently lacking. We used a Delphi method-based consensus approach to provide internationally agreed, consistent and clinically useful operational criteria for TR-AD in adults. Following a summary of the current state of knowledge based on international guidelines and an available systematic review, a survey of free-text responses to a 29-item questionnaire on relevant aspects of TR-AD, and an online consensus meeting, a panel of 36 multidisciplinary international experts and stakeholders voted anonymously on written statements in three survey rounds. Consensus was defined as ≥75% of the panel agreeing with a statement. The panel agreed on a set of 14 recommendations for the definition of TR-AD, providing detailed operational criteria for resistance to pharmacological and/or psychotherapeutic treatment, as well as a potential staging model. The panel also evaluated further aspects regarding epidemiological subgroups, comorbidities and biographical factors, the terminology of TR-AD vs. "difficult-to-treat" anxiety disorders, preferences and attitudes of persons with these disorders, and future research directions. This Delphi method-based consensus on operational criteria for TR-AD is expected to serve as a systematic, consistent and practical clinical guideline to aid in designing future mechanistic studies and facilitate clinical trials for regulatory purposes. This effort could ultimately lead to the development of more effective evidence-based stepped-care treatment algorithms for patients with anxiety disorders.

An exploration of neural predictors of treatment compliance in cognitive-behavioral group therapy for hoarding disorder

A persistent and influential barrier to effective cognitive-behavioral therapy (CBT) for patients with hoarding disorder (HD) is treatment retention and compliance. Recent research has suggested that HD patients have abnormal brain activity identified by functional magnetic resonance (fMRI) in regions often engaged for executive functioning (e.g., right superior frontal gyrus, anterior insula, and anterior cingulate), which raises questions about whether these abnormalities could relate to patients' ability to attend, understand, and engage in HD treatment. We examined data from 74 HD-diagnosed adults who completed fMRI-measured brain activity during a discarding task designed to elicit symptom-related brain dysfunction, exploring which regions' activity might predict treatment compliance variables, including treatment engagement (within-session compliance), homework completion (between-session compliance), and treatment attendance. Brain activity that was significantly related to within- and between-session compliance was found largely in insula, parietal, and premotor areas. No brain regions were associated with treatment attendance. The results add to findings from prior research that have found prefrontal, cingulate, and insula activity abnormalities in HD by suggesting that some aspects of HD brain dysfunction might play a role in preventing the engagement needed for therapeutic benefit.

Machine learning applied to functional magnetic resonance imaging in anxiety disorders

BACKGROUND

Brain functional abnormalities have been commonly reported in anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, and specific phobias. The role of functional abnormalities in the discrimination of these disorders can be tested with machine learning (ML) techniques. Here, we aim to provide a comprehensive overview of ML studies exploring the potential discriminating role of functional brain alterations identified by functional magnetic resonance imaging (fMRI) in anxiety disorders.

METHODS

We conducted a search on PubMed, Web of Science, and Scopus of ML investigations using fMRI as features in patients with anxiety disorders. A total of 12 studies (resting-state fMRI n = 5, task-based fMRI n = 6, resting-state and task-based fMRI n=1) met our inclusion criteria.

RESULTS

Overall, the studies showed that, regardless of the classifiers, alterations in functional connectivity and aberrant neural activation involving the amygdala, anterior cingulate cortex, hippocampus, insula, orbitofrontal cortex, temporal pole, cerebellum, default mode network, dorsal attention network, sensory network, and affective network were able to discriminate patients with anxiety from controls, with accuracies spanning from 36 % to 94 %.

LIMITATIONS

The small sample size, different ML approaches and heterogeneity in the selection of regions included in the multivariate pattern analyses limit the conclusions of the present review.

CONCLUSIONS

ML methods using fMRI as features can distinguish patients with anxiety disorders from healthy controls, indicating that these techniques could be used as a helpful tool for the diagnosis and the development of more targeted treatments for these disorders.

Neuroimaging Insights: Kava's (Piper methysticum) Effect on Dorsal Anterior Cingulate Cortex GABA in Generalized Anxiety Disorder

Generalised Anxiety Disorder (GAD) is a prevalent, chronic mental health disorder. The measurement of regional brain gamma-aminobutyric acid (GABA) offers insight into its role in anxiety and is a potential biomarker for treatment response. Research literature suggests Piper methysticum (Kava) is efficacious as an anxiety treatment, but no study has assessed its effects on central GABA levels. This study investigated dorsal anterior cingulate (dACC) GABA levels in 37 adult participants with GAD. GABA was measured using proton magnetic resonance spectroscopy (1H-MRS) at baseline and following an eight-week administration of Kava (standardised to 120 mg kavalactones twice daily) (n = 20) or placebo (n = 17). This study was part of the Kava for the Treatment of GAD (KGAD; ClinicalTrials.gov: NCT02219880), a 16-week intervention study. Compared with the placebo group, the Kava group had a significant reduction in dACC GABA (p = 0.049) at eight weeks. Baseline anxiety scores on the HAM-A were positively correlated with GABA levels but were not significantly related to treatment. Central GABA reductions following Kava treatment may signal an inhibitory effect, which, if considered efficacious, suggests that GABA levels are modulated by Kava, independent of reported anxiety symptoms. dACC GABA patterns suggest a functional role of higher levels in clinical anxiety but warrants further research for symptom benefit. Findings suggest that dACC GABA levels previously un-examined in GAD could serve as a biomarker for diagnosis and treatment response.

fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review

BACKGROUND

Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis.

METHOD

A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R?

RESULTS

In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions.

CONCLUSION

The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.

Self-Focused Brain Predictors of Cognitive Behavioral Therapy Response in a Transdiagnostic Sample

Background

Effective biomarkers of cognitive behavioral therapy (CBT) response provide information beyond available behavioral or self-report measures and may optimize treatment selection for patients based on likelihood of benefit. No single biomarker reliably predicts CBT response. In this study, we evaluated patterns of brain connectivity associated with self-focused attention (SFA) as biomarkers of CBT response for anxiety and obsessive-compulsive disorders. We hypothesized that pre-treatment as well as pre- to post-treatment changes in functional connectivity would be associated with improvement during CBT in a transdiagnostic sample.

Methods

Twenty-seven patients with primary social anxiety disorder (n=14) and primary body dysmorphic disorder (n=13) were scanned before and after 12 sessions of CBT targeting their primary disorder. Eligibility was based on elevated trait SFA scores on the Public Self-Consciousness Scale. Seed-based resting state functional connectivity associated with symptom improvement was computed using a seed in the posterior cingulate cortex/precuneus that delineated a self-other functional network.

Results

At pre-treatment, stronger positive connectivity of the seed with the cerebellum, insula, middle occipital gyrus, postcentral gyrus, and precuneus/superior parietal lobule, and stronger negative connectivity with the putamen, were associated with greater clinical improvement. Between pre- to post-treatment, greater anticorrelation between the seed and precuneus/superior parietal lobule was associated with clinical improvement, although this did not survive thresholding.

Conclusions

Pre-treatment functional connectivity between regions involved in attentional salience, self-generated thoughts, and external attention predicted greater CBT response. Behavioral and self-report measures of SFA did not contribute to predictions, thus highlighting the value of neuroimaging-based measures of SFA.

Clinical Trials Registration

ClinicalTrials.gov Identifier: NCT02808702 https://clinicaltrials.gov/ct2/show/NCT02808702.

Hypnotherapy for agoraphobia-Feasibility and efficacy investigated in a pilot study

A number of case studies describing hypnotherapy in the treatment of anxiety disorder patients have already been published. Only a few randomized controlled trials (RCTs) investigated the efficacy of hypnotherapy but focused mainly on symptoms rather than specific mental disorders. The goal of this study was to investigate whether hypnotherapy (HT) was superior to a waitlist control group (WL) in the reduction of agoraphobia-related symptoms. Further goals were to report the feasibility of hypnotherapy as well as attrition and completion rates and detect (epi-)genetic variables, which might play a role in treatment outcome. This pilot study was based on a monocentric two-armed randomized controlled rater-blind clinical trial that was conducted between 2018 and 2020 with a waitlist control group. A total of 36 patients diagnosed with agoraphobia were randomized to either HT or WL. Patients in HT received individual outpatient treatment with hypnotherapy with 8 to 12 sessions for a period of 3 months. Patients in WL received HT after 3 months. Agoraphobia-related symptoms were assessed at baseline, after the treatment, and 3 months later in both groups with a clinician rating. The primary hypothesis concerning the difference between groups in the individual percentage symptom reduction could be confirmed in the intention-to-treat, not the per-protocol sample. Additionally, we applied repeated-measures analyses of variance and found a higher symptom decrease in HT compared with WL patients in three of the five imputed datasets. The dropout rate was low, and satisfaction with the treatment was high. HT patients experienced a strong symptom reduction after receiving hypnotherapy. WL patients improved slightly during the waiting period. The COMT Val108/158Met genotype had an effect on the agoraphobia-related symptoms as well as on COMT DNA methylation levels. This is the first study to indicate that hypnotherapy performed better than a waitlist control group regarding the reduction in anxiety symptoms in an RCT. Future studies should confirm the efficacy of hypnotherapy and compare the treatment with a standard treatment for anxiety disorders in a larger trial. Future studies should also investigate whether hypnotic susceptibility is associated with COMT Val108/158Met genotype and could predict treatment success for HT.

Clinical trial registration

https://classic.clinicaltrials.gov/ct2/show/NCT03684577, identifier: NCT03684577.

Neural predictors of cognitive-behavior therapy outcome in anxiety-related disorders: a meta-analysis of task-based fMRI studies

BACKGROUND

Cognitive-behavior therapy (CBT) is a well-established first-line intervention for anxiety-related disorders, including specific phobia, social anxiety disorder, panic disorder/agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, and posttraumatic stress disorder. Several neural predictors of CBT outcome for anxiety-related disorders have been proposed, but previous results are inconsistent.

METHODS

We conducted a systematic review and meta-analysis of task-based functional magnetic resonance imaging (fMRI) studies investigating whole-brain predictors of CBT outcome in anxiety-related disorders (17 studies, n = 442).

RESULTS

Across different tasks, we observed that brain response in a network of regions involved in salience and interoception processing, encompassing fronto-insular (the right inferior frontal gyrus-anterior insular cortex) and fronto-limbic (the dorsomedial prefrontal cortex-dorsal anterior cingulate cortex) cortices was strongly associated with a positive CBT outcome.

CONCLUSIONS

Our results suggest that there are robust neural predictors of CBT outcome in anxiety-related disorders that may eventually lead (probably in combination with other data) to develop personalized approaches for the treatment of these mental disorders.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Dorsal anterior cingulate cortex activity during cognitive challenge in social anxiety disorder

BACKGROUND

Social anxiety disorder (SAD) is associated with aberrant emotional information processing while little is known about non-emotional cognitive processing biases. The dorsal anterior cingulate cortex (dACC) has been implicated in SAD neuropathology and is activated both by emotional and non-affective cognitive challenges like the Multisource Interference Task (MSIT).

METHODS

Here, we used fMRI to compare dACC activity and test performance during MSIT in 69 SAD patients and 38 healthy controls. In addition to patient-control comparisons, we examined whether neural activity in the dACC correlated with social anxiety, trait anxiety or depression levels.

RESULTS

The MSIT activated the dACC as expected but with no differences in task performance or neural reactivity between SAD patients and controls. There were no significant correlations between dACC activity and social or trait anxiety symptom severity. In patients, there was a significant negative correlation between dACC activity and depressive symptoms.

CONCLUSIONS

In absence of affective challenge, we found no disorder-related cognitive profile in SAD patients since neither MSIT task performance nor dACC neural activity deviated in patients relative to controls.

Improving treatment outcomes for borderline personality disorder: what can we learn from biomarker studies of psychotherapy?

PURPOSE OF REVIEW

Borderline personality disorder (BPD) is a severe and common psychiatric disorder and though evidence-based psychotherapies are effective, rates of treatment nonresponse are as high as 50%. Treatment studies may benefit from interdisciplinary approaches from neuroscience and genetics research that could generate novel insights into treatment mechanisms and tailoring interventions to the individual.

RECENT FINDINGS

We provide a timely update to the small but growing body of literature investigating neurobiological and epigenetic changes and using biomarkers to predict outcomes from evidence-based psychotherapies for BPD. Using a rapid review methodology, we identified eight new studies, updating our earlier 2018 systematic review. Across all studies, neuroimaging ( n  = 18) and genetics studies ( n  = 4) provide data from 735 participants diagnosed with BPD (mean sample size across studies = 33.4, range 2-115).

SUMMARY

We report further evidence for psychotherapy-related alterations of neural activation and connectivity in regions and networks relating to executive control, emotion regulation, and self/interpersonal functioning in BPD. Emerging evidence also shows epigenetic changes following treatment. Future large-scale multisite studies may help to delineate multilevel treatment targets to inform intervention design, selection, and monitoring for the individual patient via integration of knowledge generated through clinical, neuroscience, and genetics research.

Positive attention bias in high socially anxious individuals: Evidence from an ERP study

The Bivalent Fear of Evaluation (BFEO) model posits that the fear of positive evaluation (FPE) is a core feature of social anxiety. As such, high socially anxious individuals may show attention bias when faced with positive stimuli. However, most of the previous studies focused on the negative attention bias of social anxiety, and less on the attention bias of positive stimuli. Meanwhile, the effect of stimulus presentation time on the attention bias pattern was unclear. In order to investigate this question, we used a dot-probe paradigm with facial expressions (happy, fearful, angry, neutral) presented for 100 ms and 500 ms. The ERP results showed: (1) For high socially anxious group, happy faces elicited a larger N1 for valid than for invalid cued probes, whereas for healthy control group, angry faces elicited a larger N1 for valid than for invalid cued probes. (2) When valid cues following happy faces presented for 500 ms, the N1 amplitude was larger than that of invalid cues. However, when valid cues following angry and fear faces presented for 100 ms, the N1 amplitude was larger than that of invalid cues. The results showed difficulty in attention disengagement of high socially anxious individuals from positive stimuli, as reflected by N1, illustrating the positive attention bias in social anxiety. These results prove that FPE may contribute to maintaining social anxiety.

Neural correlates of fear conditioning are associated with treatment-outcomes to behavioral exposure in spider phobia - Evidence from magnetoencephalography

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Magnetoencephalographic effects of fear conditioning predict exposure outcomes.

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No associations between fear ratings of conditioned stimuli and exposure outcomes.

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Prefrontal correlates of safety processing and/or fear inhibition are treatment-relevant.

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Individual neural differences might be a promising predictor of exposure success.

Background

Models of anxiety disorders and the rationale of exposure therapy (ET) are grounded on classical fear conditioning. Yet, it is unclear whether lower fear ratings of conditioned safety versus threat cues and corresponding neural markers of safety-learning and/or fear inhibition assessed before treatment would predict better outcomes of behavioral exposure.

Methods

Sixty-six patients with spider phobia completed pre-treatment clinical and experimental fear conditioning assessments, one session of virtual reality ET, a post-treatment clinical assessment, and a 6-month follow-up assessment. Tilted Gabor gratings served as conditioned stimuli (CS) that were either paired (CS+) or remained unpaired (CS-) with an aversive phobia-related and phobia-unrelated unconditioned stimulus (UCS). CS+/CS- differences in fear ratings and magnetoencephalographic event-related fields (ERFs) were related to percentual symptom reductions from pre- to post-treatment, as assessed via spider phobia questionnaire (SPQ), behavioral avoidance test (BAT), and remission status at 6-month follow-up.

Results

We observed no associations between pre-treatment CS+/CS- differences in fear ratings and any treatment outcome. CS+/CS- differences in source estimations of ERFs revealed that higher CS- activity in bilateral dorsolateral prefrontal cortex (dlPFC) was related with SPQ- and BAT-reductions. Associations between CS+/CS- differences and treatment outcomes were also observed in left ventromedial prefrontal cortex (vmPFC) regions, which additionally revealed associations with the follow-up remission status.

Conclusions

Results provide initial evidence that neural pre-treatment CS+/CS- differences may hold predictive information regarding outcomes of behavioral exposure. Our findings highlight a key role of neural responses to safety cues with potentially inhibitory effects on affect-generating structures during fear conditioning.

How a transdiagnostic approach can improve the treatment of emotional disorders: Insights from clinical psychology and neuroimaging

Multiple psychological treatments for emotional disorders have been developed and implemented, improving the quality of life of individuals. Nevertheless, relapse and poor response to psychotherapy are common. This article argues that a greater understanding of both the psychological and neurobiological mechanisms of change in psychotherapy is essential to improve treatment for emotional disorders. It aims to demonstrate how an understanding of these mechanisms provides a basis for (i) reconceptualizing some mental disorders, (ii) refining and establishing the evidence for existing therapeutic techniques and (iii) designing new techniques that precisely target the processes that maintain these disorders. Possible future directions for researchers and practitioners working at the intersection of neuropsychology and clinical psychology are discussed.

Neurocircuitry of Treatment in Anxiety Disorders

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Moment-to-Moment Brain Signal Variability Reliably Predicts Psychiatric Treatment Outcome

BACKGROUND

Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes.

METHODS

A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes.

RESULTS

Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total r_{ACTUAL,PREDICTED} = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long.

CONCLUSIONS

Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms

BACKGROUND

About 30% of patients drop out of cognitive-behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous.

AIMS

This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables.

METHOD

The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation.

RESULTS

The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = -2.93, 95% CI (-3.95, -1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale.

CONCLUSIONS

Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.

Heart rate variability: A biomarker of selective response to mindfulness-based treatment versus fluoxetine in generalized anxiety disorder

BACKGROUND

Mindfulness-based interventions (MBIs) are effective for some, but not all patients with anxiety disorders, but no clinical features have been consistently able to differentiate which patients are more likely to respond. In this study, we tested heart rate variability (HRV), a proposed correlate of regulated emotional response, as a moderator of treatment response to an MBI compared with pharmacotherapy.

METHODS

Seventy-seven patients with GAD had HRV data collected before randomization to pharmacological treatment with fluoxetine or Body-in-Mind Training (an MBI focused on bodily movement attention). HRV was used to predict treatment response measured by the Hamilton anxiety rating scale at 0 (baseline), 5, and 8 weeks (end of the intervention).

RESULTS

The HF (nu) index of HRV was a strong moderator of treatment response between BMT and fluoxetine (estimate = 4.27 95%CI [1.19, 8.19]). Although fluoxetine was overall slightly superior to BMT in this study, no differences were found between groups in patients with high HF (nu) scores (estimate = -1.85 CI95% [-9.21, 5.52]). In contrast, patients with low HF (nu) achieved lower anxiety rating scores with fluoxetine treatment when compared with BMT (estimate = -10.29, 95% CI [-17.59, -2.99]).

LIMITATIONS

A relatively small sample of patients was included.

CONCLUSIONS

HRV was able to identify a subgroup for which MBI was less effective than pharmacotherapy and is a promising candidate as a selective biomarker for treatment response between an MBI and fluoxetine.

Pharmacogenomic Studies in Intellectual Disabilities and Autism Spectrum Disorder: A Systematic Review

BACKGROUND

Individuals with intellectual disability (ID) and autism spectrum disorder (ASD) often receive psychotropic medications such as antipsychotics and antidepressants to treat aberrant behaviors and mood symptoms, frequently resulting in polypharmacy and drug-related adverse effects. Pharmacogenomic (PGx) studies with ASD and/or ID (ASD/ID) have been scarce despite the promise of optimizing treatment outcomes. We reviewed the literature on PGx studies with antipsychotics and antidepressants (e.g., treatment response and adverse effects) in ASD/ID.

METHODS

We performed a systematic review using MEDLINE, Embase, and PsycINFO, including peer-reviewed original articles in English referring to PGx in the treatment of ASD/ID in any age groups (e.g., treatment response and adverse effects).

RESULTS

A total of 28 PGx studies using mostly candidate gene approaches were identified across age groups. Notably, only 3 studies included adults with ASD/ID while the other 25 studies focused specifically on children/adolescents with ASD/ID. Twelve studies primarily investigated treatment response, of which 5 and 6 studies included patients treated with antipsychotics and antidepressants, respectively. Most interesting results for response were reported for 2 sets of candidate gene studies, namely: (1) The DRD3 Ser9Gly (rs6280) polymorphism was examined in patients treated with risperidone in 3 studies, 2 of which reported an association with risperidone treatment response and (2) the SLC6A4 5-HTTLPR polymorphism and treatment response to antidepressants which was investigated in 4 studies, 3 of which reported significant associations. In regard to side effects, 9 of 15 studies focused on hyperprolactinemia in patients treated with risperidone. Among them, 7 and 5 studies examined the impact of CYP2D6 and DRD2 Taq1A polymorphisms, respectively, yielding mostly negative study findings.

CONCLUSIONS

There is limited data available on PGx in individuals with ASD/ID and in particular in adults. Given the potential for PGx testing in improving treatment outcomes, additional PGx studies for psychotropic treatment in ASD/ID across age groups are warranted.

Clinical predictors of treatment response towards exposure therapy in virtuo in spider phobia: A machine learning and external cross-validation approach

While being highly effective on average, exposure-based treatments are not equally effective in all patients. The a priori identification of patients with a poor prognosis may enable the application of more personalized psychotherapeutic interventions. We aimed at identifying sociodemographic and clinical pre-treatment predictors for treatment response in spider phobia (SP). N = 174 patients with SP underwent a highly standardized virtual reality exposure therapy (VRET) at two independent sites. Analyses on group-level were used to test the efficacy. We applied a state-of-the-art machine learning protocol (Random Forests) to evaluate the predictive utility of clinical and sociodemographic predictors for a priori identification of individual treatment response assessed directly after treatment and at 6-month follow-up. The reliability and generalizability of predictive models was tested via external cross-validation. Our study shows that one session of VRET is highly effective on a group-level and is among the first to reveal long-term stability of this treatment effect. Individual short-term symptom reductions could be predicted above chance, but accuracies dropped to non-significance in our between-site prediction and for predictions of long-term outcomes. With performance metrics hardly exceeding chance level and the lack of generalizability in the employed between-site replication approach, our study suggests limited clinical utility of clinical and sociodemographic predictors. Predictive models including multimodal predictors may be more promising.

Systematic transdiagnostic review of magnetic-resonance imaging results: Depression, anxiety disorders and their co-occurrence

BACKGROUND

Major depressive disorder (MDD) and anxiety disorders (ANX) share core symptoms such as negative affect and often co-exist. Magnetic-resonance imaging (MRI) research suggests shared neuroanatomical/neurofunctional underpinnings. So far, studies considering transdiagnostic and disorder-specific neural alterations in MDD and ANX as well as the comorbid condition (COM) have not been reviewed systematically.

METHODS

Following PRISMA guidelines, the literature was screened and N = 247 articles were checked according to the PICOS criteria: MRI studies investigating transdiagnostic (across MDD, ANX, COM compared to healthy controls) and/or disorder-specific (between MDD, ANX, COM) neural alterations. N = 35, thereof n = 13 structural MRI and diffusion-tensor imaging studies and n = 22 functional MRI studies investigating emotional, cognitive deficits and resting state were included and quality coded.

RESULTS

Results indicated transdiagnostic structural/functional alterations in the orbitofrontal cortex/middle frontal cortex and in limbic regions (amygdala, cingulum, hippocampus). Few and inconsistent disorder-specific alterations were reported. However, depression-specific functional alterations were reported for the inferior frontal gyrus and dorsolateral prefrontal cortex during emotional tasks, and limbic regions at rest. Preliminary results for anxiety-specific functional alterations were found in the insula and frontal regions during emotional tasks, in the inferior parietal lobule, superior frontal gyrus and superior temporal gyrus during cognitive tasks, and (para)limbic alterations at rest.

CONCLUSIONS

This review provides evidence to support existing transdiagnostic fronto-limbic neural models in MDD and ANX. On top, it expands existing knowledge taking into account comorbidity and comparing MDD with ANX. Heterogeneous evidence exists for disorder-specific alterations. Research focusing on ANX sub-types, and the consideration of COM would contribute to a better understanding of basic neural underpinnings.

The clinical characterization of the adult patient with an anxiety or related disorder aimed at personalization of management

The clinical construct of "anxiety neurosis" was broad and poorly defined, so that the delineation of specific anxiety disorders in the DSM-III was an important advance. However, anxiety and related disorders are not only frequently comorbid, but each is also quite heterogeneous; thus diagnostic manuals provide only a first step towards formulating a management plan, and the development of additional decision support tools for the treatment of anxiety conditions is needed. This paper aims to describe systematically important domains that are relevant to the personalization of management of anxiety and related disorders in adults. For each domain, we summarize the available research evidence and review the relevant assessment instruments, paying special attention to their suitability for use in routine clinical practice. We emphasize areas where the available evidence allows the clinician to personalize the management of anxiety conditions, and we point out key unmet needs. Overall, the evidence suggests that we are becoming able to move from simply recommending that anxiety and related disorders be treated with selective serotonin reuptake inhibitors, cognitive-behavioral therapy, or their combination, to a more complex approach which emphasizes that the clinician has a broadening array of management modalities available, and that the treatment of anxiety and related disorders can already be personalized in a number of important respects.

Behavioral and Magnetoencephalographic Correlates of Fear Generalization are Associated with Responses to Later Virtual Reality Exposure Therapy in Spider Phobia

BACKGROUND

As overgeneralization of fear is a pathogenic marker of anxiety disorders, we investigated whether pre-treatment levels of fear generalization in spider-phobic patients are related to their response to exposure-based treatment, in order to identify pre-treatment moderators of treatment success.

METHODS

Ninety patients with spider phobia completed pre-treatment clinical and magnetoencephalography (MEG) assessments, one session of virtual reality exposure therapy, and a post-treatment clinical assessment. Based on the primary outcome (30% symptom reduction in self-reported symptoms) they were categorized as responders or non-responders. In a pre-treatment MEG fear generalization paradigm involving fear conditioning with two unconditioned stimuli (UCS), we obtained fear ratings, UCS-expectancy ratings, and event-related fields to conditioned stimuli (CS-, CS+) and 7 different generalization stimuli (GS) on a perceptual continuum from CS- to CS+.

RESULTS

Prior to treatment, non-responders showed behavioral overgeneralization indicated by more linear generalization gradients in fear ratings. Analyses of MEG source estimations revealed that non-responders showed a decline of their (inhibitory) frontal activations to safety-signaling CS- and GS compared to CS+ over time, while responders maintained these activations at early (<300ms) and late processing stages.

CONCLUSIONS

Results provide initial evidence that pre-treatment differences of behavioral and neural markers of fear generalization may act as moderators of later responses to behavioral exposure. Stimulating further research on fear generalization as a potential predictive marker, our findings are an important first step in the attempt to identify patients who may not profit from ET, and to personalize and optimize treatment strategies for this vulnerable patient group.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Emerging Evidence for Putative Neural Networks and Antecedents of Pediatric Anxiety in the Fetal, Neonatal, and Infant Periods

Anxiety disorders are the most prevalent psychiatric disorders in youth and are associated with profound individual impairment and public health costs. Research shows that clinically significant anxiety symptoms manifest in preschool-aged children, and correlates of anxiety symptoms are observable in infancy. Yet, predicting who is at risk for developing anxiety remains an enduring challenge. Predictive biomarkers of anxiety are needed before school age when anxiety symptoms typically consolidate into diagnostic profiles. Increasing evidence indicates that early neural measures implicated in anxiety and anxious temperament may be incorporated with traditional measures of behavioral risk (i.e., behavioral inhibition) to provide more robust classification of pediatric anxiety problems. This review examines the phenomenology of anxiety disorders in early life, highlighting developmental research that interrogates the putative neurocircuitry of pediatric anxiety. First, we discuss enduring challenges in identifying and predicting risk for pediatric anxiety. Second, we summarize emerging evidence for putative neural antecedents and networks underlying risk for pediatric anxiety in the fetal, neonatal, and infant periods that represent novel potential avenues for risk identification and prediction. We focus on evidence examining the importance of early amygdala and extended amygdala circuitry development to the emergence of anxiety. Finally, we discuss the utility of integrating developmental psychopathology and neuroscience to facilitate future research and clinical work.

Therapygenetic effects of 5-HTTLPR on cognitive-behavioral therapy in anxiety disorders: A meta-analysis

There is a recurring debate on the role of the serotonin transporter gene linked polymorphic region (5-HTTLPR) in the moderation of response to cognitive behavioral therapy (CBT) in anxiety disorders. Results, however, are still inconclusive. We here aim to perform a meta-analysis on the role of 5-HTTLPR in the moderation of CBT outcome in anxiety disorders. We investigated both categorical (symptom reduction of at least 50%) and dimensional outcomes from baseline to post-treatment and follow-up. Original data were obtained from ten independent samples (including three unpublished samples) with a total of 2,195 patients with primary anxiety disorder. No significant effects of 5-HTTLPR genotype on categorical or dimensional outcomes at post and follow-up were detected. We conclude that current evidence does not support the hypothesis of 5-HTTLPR as a moderator of treatment outcome for CBT in anxiety disorders. Future research should address whether other factors such as long-term changes or epigenetic processes may explain further variance in these complex gene-environment interactions and molecular-genetic pathways that may confer behavioral change following psychotherapy.

[Psychotherapy of anxiety disorders: state of the art]

BACKGROUND

Alongside depression, anxiety disorders are the most frequent reason for consulting a psychotherapist.

OBJECTIVE

This article describes the recent progress in understanding basic learning processes in anxiety treatment, the resulting therapeutic procedures, the current state of knowledge on the efficacy of the various psychotherapeutic procedures and on the moderators of the success of treatment.

MATERIAL AND METHODS

The English and German language literature was reviewed and compiled, with an emphasis on the last 10 years.

RESULTS

Cognitive-behavioral therapy (CBT) achieves the best and broadest level of evidence across all anxiety disorders. Initial studies have also provided emerging evidence for the efficacy of manualized short-term psychodynamic treatment. The most discussed mechanism of action is that of inhibitory learning. Augmentation strategies and personalized treatment approaches are gaining in relevance.

CONCLUSION

Current models of inhibitory learning are rooted in basic research and foster a deeper understanding of the underlying neurobiological mechanisms. In order to optimize success of exposure treatment in vulnerable subgroups of patients, many procedural, device-based and pharmacological augmentation strategies are currently under investigation, whereby the latter are mostly still in the stage of (pre)clinical testing.

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.

[Prevention of anxiety disorders]

Preventive interventions, such as universal and targeted, i.e. selective and indicated, interventions can effectively reduce the incidence of anxiety disorders and thus lower the high individual and socioeconomic burden of anxiety disorders. This review article provides an overview of internationally established prevention programs for children, adolescents and adults. The efficacy and cost effectiveness of preventive interventions are discussed. Due to the large target group, universally implemented strategies are costly and organizationally complex but can prevent a large number of manifest anxiety disorders or reduce the clinical expression, despite only small effect sizes. Selective preventive measures are aimed at persons with a high risk for anxiety disorders (e.g. with high anxiety sensitivity, behavioral inhibition, harm avoidance, family history of anxiety disorders). The indicated preventive interventions in high-risk individuals with first subclinical anxiety symptoms are successful in preventing the manifestation of anxiety disorders and constitute the most cost-effective type of preventive measures. Biological, i.e. genetic, imaging or neurophysiological markers, or their combination, are discussed as promising future targets for selective or indicated prevention of anxiety disorders.

Spider Phobia: Neural Networks Informing Diagnosis and (Virtual/Augmented Reality-Based) Cognitive Behavioral Psychotherapy-A Narrative Review

Recent fMRI studies on specific animal phobias, particularly spider phobia (arachnophobia), have identified a large variety of specific brain regions involved in normal and disturbed fear processing. Both functional and structural brain abnormalities have been identified among phobic patients. Current research suggests that both conscious and subconscious fear processing play a crucial role in phobic disorders. Cognitive behavioral therapy has been identified as an effective treatment for specific phobias and has been associated with neuroplastic effects which can be evaluated using current neuroimaging techniques. Recent research suggests that new approaches using virtual (VR) or augmented reality (AR) tend to be similarly effective as traditional "in vivo" therapy methods and could expand treatment options for different medical or individual scenarios. This narrative review elaborates on neural structures and particularities of arachnophobia. Current treatment options are discussed and future research questions are highlighted.

Considerations for Pairing Cognitive Behavioral Therapies and Non-invasive Brain Stimulation: Ignore at Your Own Risk

Multimodal approaches combining cognitive behavioral therapies (CBT) with non-invasive brain stimulation (NIBS) hold promise for improving the treatment of neuropsychiatric disorders. As this is a relatively new approach, it is a critical time to identify guiding principles and methodological considerations to enhance research rigor. In the current paper, we argue for a principled approach to CBT and NIBS pairings based on synergistic activation of neural circuits and identify key considerations about CBT that may influence pairing with NIBS. Careful consideration of brain-state interactions and CBT-related nuances will increase the potential for these combinations to be positively synergistic.

Combined transcranial direct current stimulation and psychological interventions: State of the art and promising perspectives for clinical psychology

Recent literature shows great heterogeneity in the reported efficacy of transcranial direct current stimulation (tDCS) as a stand-alone psychiatric treatment. Aiming to increase its efficacy, tDCS has been combined with psychological interventions. Our state-of-the-art overview of such combined treatment trials indicates, however, that these usually do not elicit synergistic clinical effects. We therefore explored more basic mechanisms related to the brain state-dependency of tDCS. Importantly, based on our overview, the efficacy of combined interventions may depend on whether individual patients present with endophenotypes that are implicated in the development and maintenance of psychopathology, such as prefrontal-mediated cognitive dysfunction. We discuss how future studies may contribute to the development of personally-tailored dual active treatments by adhering to the Research Domain Criteria (RDoC) framework. RDoC-based mechanistic research may reveal alternative neural circuits that should be functionally targeted by both tDCS and psychological interventions, with promising avenues for clinical psychological science and practice.

Enriching CBT by Neuroscience: Novel Avenues to Achieve Personalized Treatments

Although cognitive behavioral therapy (CBT) is an established and efficient treatment for a variety of common mental disorders, a considerable number of patients do not respond to treatment or relapse after successful CBT. Recent findings and approaches from neuroscience could pave the way for clinical developments to enhance the outcome of CBT. Herein, we will present how neuroscience can offer novel perspectives to better understand (a) the biological underpinnings of CBT, (b) how we can enrich CBT with neuroscience-informed techniques (augmentation of CBT), and (c) why some patients may respond better to CBT than others (predictors of therapy outcomes), thus paving the way for more personalized and effective treatments. We will introduce some key topics and describe a selection of findings from CBT-related research using tools from neuroscience, with the hope that this will provide clinicians and clinical researchers with a brief and comprehensible overview of the field.

Ketogenic therapy in neurodegenerative and psychiatric disorders: From mice to men

Ketogenic diet is a low carbohydrate and high fat diet that has been used for over 100 years in the management of childhood refractory epilepsy. More recently, ketogenic diet has been investigated for a number of metabolic, neurodegenerative and neurodevelopmental disorders. In this comprehensive review, we critically examine the potential therapeutic benefits of ketogenic diet and ketogenic agents on neurodegenerative and psychiatric disorders in humans and translationally valid animal models. The preclinical literature provides strong support for the efficacy of ketogenic diet in a variety of diverse animal models of neuropsychiatric disorders. However, the evidence from clinical studies, while encouraging, particularly in Alzheimer's disease, psychotic and autism spectrum disorders, is limited to case studies and small pilot trials. Firm conclusion on the efficacy of ketogenic diet in psychiatric disorders cannot be drawn due to the lack of randomised, controlled clinical trials. The potential mechanisms of action of ketogenic therapy in these disorders with diverse pathophysiology may include energy metabolism, oxidative stress and immune/inflammatory processes. In conclusion, while ketogenic diet and ketogenic substances hold promise pre-clinically in a variety of neurodegenerative and psychiatric disorders, further studies, particularly randomised controlled clinical trials, are warranted to better understand their clinical efficacy and potential side effects.

Change in neural response during emotion regulation is associated with symptom reduction in cognitive behavioral therapy for anxiety disorders

BACKGROUND

Anxiety disorders are debilitating conditions that can be treated with cognitive behavioral therapy (CBT). Increased understanding of the neurobiological correlates of CBT may inform treatment improvements and personalization. Prior neuroimaging studies point to treatment-related changes in anterior cingulate, insula, and other prefrontal regions during emotional processing, yet to date the impact of CBT on neural substrates of "top down" emotion regulation remains understudied. We examined the relationship between symptom changes assessed over the course of CBT treatment sessions and pre- to post-treatment neural change during an emotion regulation task.

METHOD

In the current study, a sample of 30 participants with panic disorder or generalized anxiety disorder completed a reappraisal-based emotion regulation task while undergoing fMRI before and after completing CBT.

RESULTS

Reduced activation in the parahippocampal gyrus was observed from pre- to post-treatment during periods of reducing versus maintaining emotion. Parahippocampal activation was associated with change in symptoms over the course of treatment and post-treatment responder status. Results suggest that, from pre- to post-CBT, participants demonstrated downregulation of neural responses during effortful cognitive emotion regulation.

LIMITATIONS

Effects were not observed in frontoparietal systems as would be hypothesized based on prior literature, suggesting that treatment-related change could occur outside of fronto-parietal and limbic regions that are central to most models of neural functioning in anxiety disorders.

CONCLUSIONS

Continued work is needed to better understand how CBT affects cognitive control and memory processes that are hypothesized to support reappraisal as a strategy for emotion regulation.

Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross-validation machine learning approach

OBJECTIVES

Embedded in the Collaborative Research Center "Fear, Anxiety, Anxiety Disorders" (CRC-TRR58), this bicentric clinical study aims at identifying biobehavioral markers of treatment (non-)response by applying machine learning methodology with an external cross-validation protocol. We hypothesize that a priori prediction of treatment (non-)response is possible in a second, independent sample based on multimodal markers.

METHODS

One-session virtual reality exposure treatment (VRET) with patients with spider phobia was conducted on two sites. Clinical, neuroimaging, and genetic data were assessed at baseline, post-treatment and after 6 months. The primary and secondary outcomes defining treatment response are as follows: 30% reduction regarding the individual score in the Spider Phobia Questionnaire and 50% reduction regarding the individual distance in the behavioral avoidance test.

RESULTS

N = 204 patients have been included (n = 100 in Würzburg, n = 104 in Münster). Sample characteristics for both sites are comparable.

DISCUSSION

This study will offer cross-validated theranostic markers for predicting the individual success of exposure-based therapy. Findings will support clinical decision-making on personalized therapy, bridge the gap between basic and clinical research, and bring stratified therapy into reach. The study is registered at ClinicalTrials.gov (ID: NCT03208400).

Imaging the socially-anxious brain: recent advances and future prospects

Social anxiety disorder (SAD) is serious psychiatric condition with a genetic background. Insight into the neurobiological alterations underlying the disorder is essential to develop effective interventions that could relieve SAD-related suffering. In this expert review, we consider recent neuroimaging work on SAD. First, we focus on new results from magnetic resonance imaging studies dedicated to outlining biomarkers of SAD, including encouraging findings with respect to structural and functional brain alterations associated with the disorder. Furthermore, we highlight innovative studies in the field of neuroprediction and studies that established the effects of treatment on brain characteristics. Next, we describe novel work aimed to delineate endophenotypes of SAD, providing insight into the genetic susceptibility to develop the disorder. Finally, we outline outstanding questions and point out directions for future research.

A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers

Increased amygdala responsiveness is the hallmark of fear and a characteristic across patients with anxiety disorders. The amygdala is embedded in a complex regulatory circuit. Multiple different mechanisms may elevate amygdala responsiveness and lead to the occurrence of an anxiety disorder. While top-down control by the prefrontal cortex (PFC) downregulates amygdala responses, the locus coeruleus (LC) drives up amygdala activation via noradrenergic projections. This indicates that the same fearful phenotype may result from different neural mechanisms. We propose a mechanistic model that defines three different neural biomarkers causing amygdala hyper-responsiveness in patients with anxiety disorders: (a) inherent amygdala hypersensitivity, (b) low prefrontal control and (c) high LC drive. First-line treatment for anxiety disorders is exposure-based cognitive behavioural therapy, which strengthens PFC recruitment during emotion regulation and thus targets low-prefrontal control. A treatment response rate around 50% (Loerinc et al., 2015, Clinical Psychological Reviews, 42, 72-82) might indicate heterogeneity of underlying neurobiological mechanisms among patients, presumably leading to high variation in treatment benefit. Transforming insights from cognitive neuroscience into applicable clinical heuristics to categorise patients based on their underlying biomarker may support individualised treatment selection in psychiatry. We review literature on the three anxiety-related mechanisms and present a mechanistic model that may serve as a rational for pathology-based diagnostic and biomarker-guided treatment selection in psychiatry.

Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder

BACKGROUND

Correct prediction of treatment response is a central goal of precision psychiatry. Here, we tested the predictive accuracy of a variety of pre-treatment patient characteristics, including clinical, demographic, molecular genetic, and neuroimaging markers, for treatment response in patients with social anxiety disorder (SAD).

METHODS

Forty-seven SAD patients (mean±SD age 33.9 ± 9.4 years, 24 women) were randomized and commenced 9 weeks' Internet-delivered cognitive behavior therapy (CBT) combined either with the selective serotonin reuptake inhibitor (SSRI) escitalopram (20 mg daily [10 mg first week], SSRI+CBT, n = 24) or placebo (placebo+CBT, n = 23). Treatment responders were defined from the Clinical Global Impression-Improvement scale (CGI-I ≤ 2). Before treatment, patients underwent functional magnetic resonance imaging and the Multi-Source Interference Task taxing cognitive interference. Support vector machines (SVMs) were trained to separate responders from nonresponders based on pre-treatment neural reactivity in the dorsal anterior cingulate cortex (dACC), amygdala, and occipital cortex, as well as molecular genetic, demographic, and clinical data. SVM models were tested using leave-one-subject-out cross-validation.

RESULTS

The best model separated treatment responders (n = 24) from nonresponders based on pre-treatment dACC reactivity (83% accuracy, P = 0.001). Responders had greater pre-treatment dACC reactivity than nonresponders especially in the SSRI+CBT group. No other variable was associated with clinical response or added predictive accuracy to the dACC SVM model.

LIMITATIONS

Small sample size, especially for genetic analyses. No replication or validation samples were available.

CONCLUSIONS

The findings demonstrate that treatment outcome predictions based on neural cingulate activity, at the individual level, outperform genetic, demographic, and clinical variables for medication-assisted Internet-delivered CBT, supporting the use of neuroimaging in precision psychiatry.