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

Behavioral Activation and Brain Network Changes in Depression

Behavioral activation (BA) is a well-established method of evidence-based treatment for depression. There are clear links between the neural mechanisms underlying reward processing and BA treatment for depressive symptoms, including anhedonia; however, integrated interpretations of these two domains are lacking. Here we examine brain imaging studies involving BA treatments to investigate how changes in brain networks, including the reward networks, mediate the therapeutic effects of BA, and whether brain circuits are predictors of BA treatment responses. Increased activation of the prefrontal and subcortical regions associated with reward processing has been reported after BA treatment. Activation of these regions improves anhedonia. Conversely, some studies have found decreased activation of prefrontal regions after BA treatment in response to cognitive control stimuli in sad contexts, which indicates that the therapeutic mechanism of BA may involve disengagement from negative or sad contexts. Furthermore, the decrease in resting-state functional connectivity of the default-mode network after BA treatment appears to facilitate the ability to counteract depressive rumination, thereby promoting enjoyable and valuable activities. Conflicting results suggest that an intact neural response to rewards or defective reward functioning is predictive of the efficacy of BA treatments. Increasing the benefits of BA treatments requires identification of the unique individual characteristics determining which of these conflicting findings are relevant for the personalized treatment of each individual with depression.

Amygdala Volume is Associated with ADHD Risk and Severity Beyond Comorbidities in Adolescents: Clinical Testing of Brain Chart Reference Standards

Understanding atypicalities in ADHD brain correlates is a step towards better understanding ADHD etiology. Efforts to map atypicalities at the level of brain structure have been hindered by the absence of normative reference standards. Recent publication of brain charts allows for assessment of individual variation relative to age- and sex-adjusted reference standards and thus estimation not only of case-control differences but also of intraindividual prediction.

METHODS

Aim was to examine, whether brain charts can be applied in a sample of adolescents (N = 140, 38% female) to determine whether atypical brain subcortical and total volumes are associated with ADHD at-risk status and severity of parent-rated symptoms, accounting for self-rated anxiety and depression, and parent-rated oppositional defiant disorder (ODD) as well as motion.

RESULTS

Smaller bilateral amygdala volume was associated with ADHD at-risk status, beyond effects of comorbidities and motion, and smaller bilateral amygdala volume was associated with inattention and hyperactivity/impulsivity, beyond effects of comorbidities except for ODD symptoms, and motion.

CONCLUSIONS

Individual differences in amygdala volume meaningfully add to estimating ADHD risk and severity. Conceptually, amygdalar involvement is consistent with behavioral and functional imaging data on atypical reinforcement sensitivity as a marker of ADHD-related risk. Methodologically, results show that brain chart reference standards can be applied to address clinically informative, focused and specific questions.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder

BACKGROUND

Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

METHODS

EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.

RESULTS

Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.

LIMITATIONS

A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.

CONCLUSIONS

The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

Reliability of reward ERPs in middle-late adolescents using a custom and a standardized preprocessing pipeline

Despite advantage of neuroimaging measures in translational research frameworks, less is known about the psychometric properties thereof, especially in middle-late adolescents. Earlier, we examined evidence of convergent and incremental validity of reward anticipation and response event-related potentials (ERPs) and here we examined, in the same sample of 43 adolescents (M_age  = 15.67 years; SD = 1.01; range: 14-18; 32.6% boys), data quality (signal-to-noise ratio [SNR]), stability (mean amplitude across trials), and internal consistency (Cronbach's α and split-half reliability) of the same ERPs. Further, because observed time course and peak amplitude of ERP grand averages and thus findings on SNR, stability, and internal consistency may depend on preprocessing method, we employed a custom and a standardized preprocessing pipeline and compared findings across those. Using our custom pipeline, reward anticipation components were stable by the 40th trial, achieved acceptable internal consistency by the 19th, and all (but the stimulus-preceding negativity [SPN]) achieved acceptable SNR by the 41st trial. Initial response to reward components were stable by the 20th trial and achieved acceptable internal consistency by the 11th and acceptable SNR by the 45th trial. Difference scores had worse psychometric properties than parent measures. Time course and peak amplitudes of ERPs and thus results on SNR, stability, and internal consistency were comparable across preprocessing pipelines. In case of reward anticipation ERPs examined here, 41 trials (+4 artifacted and removed) and, in case of reward response ERPs, 45 trials (+5 artifacted) yielded stable and internally consistent estimates with acceptable SNR. Results are robust across preprocessing methods.

Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control

BACKGROUND

Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease.

METHODS

We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions.

RESULTS

There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network).

CONCLUSIONS

In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.

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.

Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments

Modern research has proven that the "typical patient" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.

Development of Neuroimaging-Based Biomarkers in Major Depression

A leading goal in the field of biological psychiatry for depression is to find a promising diagnostic biomarker and selection of specific psychiatric treatment mode that is most likely to benefit patients with depression. Recent neuroimaging studies have characterized the pathophysiology of major depressive disorder (MDD) with functional and structural alterations in the neural circuitry involved in emotion or reward processing. Particularly, structural and functional magnetic resonance imaging (MRI) studies have reported that the brain structures deeply involved in emotion regulation or reward processing including the amygdala, prefrontal cortex (PFC), anterior cingulate cortex (ACC), ventral striatum, and hippocampus are key regions that provide useful information about diagnosis and treatment outcome prediction in MDD. For example, it has been consistently reported that elevated activity of the ACC is associated with better antidepressant response in patients with MDD. This chapter will discuss a growing body of evidence that suggests that diagnosis or prediction of outcome for specific treatment can be assisted by a neuroimaging-based biomarker in MDD.

Evidências Neurobiológicas de Viés Atencional no Transtorno Obsessivo-Compulsivo: Revisão Sistemática

Resumo O viés atencional corresponde à alocação de recursos de atenção a materiais irrelevantes à tarefa. Supõe-se que pacientes com transtorno obsessivo-compulsivo (TOC) apresentem viés atencional voltado à ameaça. Com o objetivo de descrever os achados neurobiológicos do viés atencional voltado à ameaça no TOC, foi realizada uma busca sistemática por estudos experimentais com investigação neurobiológica nas bases de dados: MEDLINE, Web of Science, Scopus e LILACS. Quatro estudos com grupo controle são descritos nos resultados, todos indicam diferenças estatisticamente significativas na atividade encefálica associada a atenção em pacientes. Os achados neurobiológicos dos estudos incluídos na revisão sugerem a alocação de recursos da atenção a estímulos irrelevantes, independente da valência emocional no TOC.

Pregenual or subgenual anterior cingulate cortex as potential effective region for brain stimulation of depression

BACKGROUND

The dorsolateral prefrontal cortex (DLPFC) is the standard stimulation target for the repetitive transcranial magnetic stimulation (rTMS) treatment of major depression disorder (MDD). A retrospective study by Fox and colleagues found that a more negative resting-state functional magnetic resonance imaging (RS-fMRI) functional connectivity (FC) between left DLPFC and the subgenual anterior cingulate cortex (sgACC) in a large group of healthy participants is associated with a better curative effects of rTMS in MDD, suggesting that the sgACC may be an effective region. However, a recent meta-analysis on RS-fMRI studies found that the pregenual ACC (pgACC), rather than the sgACC, of MDD patients showed increased local activity.

METHODS

We used the stimulation coordinates in the left DLPFC analyzed by Fox et al. to perform RS-fMRI FC between the stimulation targets obtained from previous rTMS MDD studies and the potential effective regions (sgACC and pgACC, respectively) on the RS-fMRI data from 88 heathy participants.

RESULTS

(a) Both the pgACC and the sgACC were negatively connected to the left DLPFC; (b) both FCs of sgACC-DLPFC and pgACC-DLPFC were more negative in responders than in nonresponders; and (c) the associations between DLPFC-sgACC functional connectivity and clinical efficacy were clustered around the midline sgACC.

CONCLUSIONS

Both the pgACC and the sgACC may be potential effective regions for rTMS on the left DLPFC for treatment of MDD. However, individualized ACC-DLPFC FC-based rTMS on depression should be performed in the future to test the pgACC or the sgACC as effective regions.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker

OBJECTIVE

This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated.

METHODS

EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity.

RESULTS

ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values.

CONCLUSIONS

The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients.

SIGNIFICANCE

The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy

Combining advanced neuroimaging with novel computational methods in network science and machine learning has led to increasingly meaningful descriptions of structure and function in both the normal and the abnormal brain, thereby contributing significantly to our understanding of psychiatric disorders as circuit dysfunctions. Despite its marked potential for psychiatric care, this approach has not yet extended beyond the research setting to any clinically useful applications. Here we review current developments in the study of neuroimaging data using network models and machine learning methods, with a focus on their promise in offering a framework for clinical translation. We discuss 3 potential contributions of these methods to psychiatric care: 1) a better understanding of psychopathology beyond current diagnostic boundaries; 2) individualized prediction of treatment response and prognosis; and 3) formal theories to guide the development of novel interventions. Finally, we highlight current obstacles and sketch a forward-looking perspective of how the application of machine learning and network modeling methods should proceed to accelerate their potential transformation of clinically useful tools.

Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies

Antidepressive pharmacotherapy (AD), electroconvulsive therapy (ECT) and cognitive behavioural therapy (CBT) are effective treatments for major depressive disorder. With our review, we aim to provide a systematic overview of neuroimaging studies that investigate the effects of AD, ECT and CBT on brain grey matter volume (GMV) and biomarkers associated with response. After a systematic database research on PubMed, we included 50 studies using magnetic resonance imaging and investigating (1) changes in GMV, (2) pre-treatment GMV biomarkers associated with response, or (3) the accuracy of predictions of response to AD, ECT or CBT based on baseline GMV data. The strongest evidence for brain structural changes was found for ECT, showing volume increases within the temporal lobe and subcortical structures - such as the hippocampus-amygdala complex, anterior cingulate cortex (ACC) and striatum. For AD, the evidence is heterogeneous as only 4 of 11 studies reported significant changes in GMV. The results are not sufficient in order to draw conclusions about the structural brain effects of CBT. The findings show consistently that higher pre-treatment ACC volume is associated with response to AD, ECT and CBT. An association of higher pre-treatment hippocampal volume and response has only been reported for AD. Machine learning approaches based on pre-treatment whole brain patterns reach accuracies of 64-90% for predictions of AD or ECT response on the individual patient level. The findings underline the potential of brain biomarkers for the implementation in clinical practice as an additive feature within the process of treatment selection.

Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first-line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first-episode, drug-naïve patients and 109 healthy controls from three different multicenters underwent resting-state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft-threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.

[Psychogenic pain]

Psychogenic pain is one of the urgent problems of medicine. To date, pathogenetic mechanisms of development of pain syndrome are unclear, there is no uniform classification. Pain, developing in patients with a mental disorder without an organic damage to the nervous system, and pain, which is a complication of an already existing pain syndrome of neuropathic or nociceptive nature, should be considered separately. Treatment of psychogenic pain syndromes should be integrated with the mandatory use of methods aimed at modifying the patient's lifestyle and attitude towards the illness.

Reduced Reward Responsiveness Predicts Change in Depressive Symptoms in Anxious Children and Adolescents Following Treatment

Objectives: Reduced reward responsiveness, as measured by the event-related potential (ERP) component, the reward positivity (RewP), has been shown to play a role in the development of internalizing disorders, but implications for treatment remain unclear. In adult patients with anxiety and/or depression, reduced RewP has emerged as a predictor of greater change in symptoms following cognitive behavior therapy (CBT) or selective serotonin reuptake inhibitor (SSRI) treatment. The objectives of this preliminary study were to extend these findings to children and adolescents with anxiety disorders by evaluating RewP to reward as a predictor of change in anxiety severity or depressive symptoms following treatment with CBT or SSRI and to explore whether RewP differentially predicts response to one type of treatment. Methods: Patients (7-19 years old) with social and/or generalized anxiety disorder (N = 27) completed baseline measures of anxiety severity and depressive symptoms, as well as an ERP monetary reward anticipation and feedback task. RewP was measured in response to reward and breaking even feedback. Patients were then randomly assigned to CBT or SSRI treatment, and completed measures of anxiety and depressive symptom severity at the last treatment session. Results: Reduced reward responsiveness, as measured by RewP to rewards, predicted greater change in depressive symptoms following treatment, adjusting for baseline symptoms, age, and RewP to breaking even. RewP was not a significant predictor of change in anxiety symptoms. Although preliminary, exploratory analyses suggested that among anxious youth, RewP specifically predicted change in depressive symptoms following CBT, rather than SSRI. Conclusion: Results provide preliminary support for the utility of ERP measures of reward responsiveness in predicting treatment response in youth. With further research and standardization, ERP assessments could potentially be implemented in clinical settings to inform prognosis and treatment planning for youth with internalizing disorders.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

Chronic obsessive-compulsive disorder: prognostic factors

BACKGROUND

The course of illness in obsessive-compulsive disorder (OCD) varies significantly between patients. Little is known about factors predicting a chronic course of illness. The aim of this study is to identify factors involved in inducing and in maintaining chronicity in OCD.

METHODS

The present study is embedded within the Netherlands Obsessive Compulsive Disorder Association (NOCDA) study, an ongoing multicenter naturalistic cohort study designed to identify predictors of long-term course and outcome in OCD. For this study, 270 subjects with a current diagnosis of OCD were included. Chronicity status at 2-year follow-up was regressed on a selection of baseline predictors related to OCD, to comorbidity and to stress and support.

RESULTS

Psychotrauma [odds ratio (OR) 1.98, confidence interval (CI) 1.22-3.22, p = 0.006], recent negative life events (OR 1.42, CI 1.01-2.01, p = 0.043), and presence of a partner (OR 0.28, CI 0.09-0.85, p = 0.025) influenced the risk of becoming chronic. Longer illness duration (OR 1.46, CI 1.08-1.96, p = 0.013) and higher illness severity (OR 1.09, CI 1.03-1.16, p = 0.003) increased the risk of remaining chronic.

CONCLUSIONS

External influences increase the risk of becoming chronic, whereas the factors involved in maintaining chronicity are illness-related. As the latter are potentially difficult to modify, treatment should be devoted to prevent chronicity from occurring in the first place. Therapeutic strategies aimed at alleviating stress and at boosting social support might aid in achieving this goal.

The development of the error-related negativity in large sample of adolescent females: Associations with anxiety symptoms

Anxiety is the most common form of psychopathology and tends to begin early in the course of development. Given this, there is great interest in identifying developmental changes in neural systems that may delineate healthy versus anxious trajectories. A substantial amount of work has focused on the error-related negativity as a neural marker of anxiety. The ERN is a negative deflection in the event-related potential that occurs when individuals make mistakes and is increased in anxious individuals. A separate body of work has focused on normative developmental changes in the ERN - demonstrating an age-related increase in the ERN that occurs across childhood and adolescence. In the current study, we examine the ERN in relation to specific phenotypic expressions of anxiety during a core risk period in a sample of females (N = 220) ranging from 8 to 14 years old. Results from the current study suggest that error-related brain activity is related to both parent and child report of social anxiety symptoms, even when controlling for all other symptom scales. Additionally, mediation models suggest that the normative developmental increase observed in the ERN is partially mediated by increases in social anxiety symptoms. The current results are novel insofar as they identify a specific phenotypic expression of anxiety that underlies developmental increases in this neural biomarker.

Personalized (N-of-1) Trials for Depression: A Systematic Review

PURPOSE/BACKGROUND

Personalized (N-of-1) trials are single-patient, crossover-design trials that may be useful for personalizing the selection of depression treatments. We conducted a systematic review of published N-of-1 trials for depression to determine the feasibility and suitability of this methodology for personalizing depression care.

METHODS/PROCEDURES

Electronic databases were searched from database inception through October 2016. Studies were selected if they enrolled depressed patients, included a within-subject crossover design, and systematically assessed depressive symptoms during the N-of-1 trial.

FINDINGS/RESULTS

Five eligible studies reporting on 47 depressed patients (range, 1-18 patients) were identified. Two studies were conducted among adults with treatment-resistant depression, 1 study among depressed inpatients, and 2 studies among patients from special populations (geriatric nursing home, human immunodeficiency virus-associated encephalopathy). All studies evaluated the effects of pharmacologic treatments (methylphenidate, D-amphetamine, ketamine, and sulpiride). Three studies compared an off-label treatment with placebo, 1 study compared 2 off-label treatments, and 1 study compared escalating doses of an off-label treatment with placebo. All 4 studies with more than 1 participant demonstrated heterogeneous treatment effects. All studies produced data that could personalize treatment selection for individual patients. No studies reported on recruitment challenges, compliance with self-tracking, or satisfaction with participation.

IMPLICATIONS/CONCLUSIONS

The feasibility of N-of-1 trials for depression was demonstrated for a limited number of second-line pharmacologic treatments in treatment-resistant patients or in patients with comorbidities that would have excluded them from conventional randomized controlled trials. Additional research is needed to determine whether N-of-1 trials are suitable for improving the selection of depression treatments in clinical practice.

Neural and neuroendocrine predictors of pharmacological treatment response in adolescents with depression: A preliminary study

OBJECTIVE

Typically, about 30 to 50% of adolescents with depression fail to respond to evidence-based treatments, including antidepressant medications such as selective serotonin reuptake inhibitors (SSRIs). Efforts for identifying predictors and moderators of treatment response are needed to begin to address critical questions relevant to personalized care in adolescent depression. In this pilot study, we aim to identify biological predictors of response to antidepressant treatment.

METHOD

We used a multiple levels of analysis approach to evaluate threat system functioning (fronto-limbic system and the associated hormonal cascade) to determine if key biological indexes at baseline could predict improvement in depressive symptoms after eight weeks of antidepressant treatment in adolescents with depression.

RESULTS

Neural predictors of favorable treatment response included lower amygdala connectivity with left supplementary motor area and with right precentral gyrus, and greater amygdala connectivity with right central opercular cortex and Heschl's gyrus connectivity during rest. During an emotion task, neural predictors of treatment response were greater activation of the bilateral anterior cingulate cortex and left medial frontal gyrus. Additionally, different patterns of salivary cortisol obtained in the context of a modified Trier Social Stress Test were associated with those whose depressive symptoms remitted as compared to those whose symptoms persisted.

CONCLUSIONS

This approach shows significant promise for identifying predictors of treatment response in adolescents with depression. Future work is needed that incorporates sufficiently powered, randomized control trials to provide the basis by which both predictors and moderators of treatment response are identified. The hope is that this work will inform the development of methods that can guide clinician decision-making in assigning beneficial treatments for adolescents who are suffering from depression.

Basolateral amygdala-ventromedial prefrontal cortex connectivity predicts cognitive behavioural therapy outcome in adults with obsessive-compulsive disorder

BACKGROUND

Cognitive behavioural therapy (CBT), including exposure and ritual prevention, is a first-line treatment for obsessive-compulsive disorder (OCD), but few reliable predictors of CBT outcome have been identified. Based on research in animal models, we hypothesized that individual differences in basolateral amygdala-ventromedial prefrontal cortex (BLA-vmPFC) communication would predict CBT outcome in patients with OCD.

METHODS

We investigated whether BLA-vmPFC resting-state functional connectivity (rs-fc) predicts CBT outcome in patients with OCD. We assessed BLA-vmPFC rs-fc in patients with OCD on a stable dose of a selective serotonin reuptake inhibitor who then received CBT and in healthy control participants.

RESULTS

We included 73 patients with OCD and 84 healthy controls in our study. Decreased BLA-vmPFC rs-fc predicted a better CBT outcome in patients with OCD and was also detected in those with OCD compared with healthy participants. Additional analyses revealed that decreased BLA-vmPFC rs-fc uniquely characterized the patients with OCD who responded to CBT.

LIMITATIONS

We used a sample of convenience, and all patients were receiving pharmacological treatment for OCD.

CONCLUSION

In this large sample of patients with OCD, BLA-vmPFC functional connectivity predicted CBT outcome. These results suggest that future research should investigate the potential of BLA-vmPFC pathways to inform treatment selection for CBT across patients with OCD and anxiety disorders.

Cortical Thickness and Subcortical Gray Matter Volume in Pediatric Anxiety Disorders

Perturbations in the prefrontal cortex (PFC), hippocampus, and amygdala are implicated in the development of anxiety disorders. However, most structural neuroimaging studies of patients with anxiety disorders utilize adult samples, and the few studies in youths examine small samples, primarily with volume-based measures. This study tested the hypothesis that cortical thickness of PFC regions and gray matter volume of the hippocampus and amygdala differ between pediatric anxiety disorder patients and healthy volunteers (HVs). High-resolution 3-Tesla T1-weighted MRI scans were acquired in 151 youths (75 anxious, 76 HV; ages 8-18). Analyses tested associations of brain structure with anxiety diagnosis and severity across both groups, as well as response to cognitive-behavioral therapy in a subset of 53 patients. Cortical thickness was evaluated both within an a priori PFC mask (small-volume corrected) and using an exploratory whole-brain-corrected (p<0.05) approach. Anxious relative to healthy youths exhibited thicker cortex in the left ventromedial PFC (vmPFC) and left precentral gyrus. Both anxiety diagnosis and symptom severity were associated with smaller right hippocampal volume. In patients, thinner cortex in parietal and occipital cortical regions was associated with worse treatment response. Pediatric anxiety was associated with structural differences in vmPFC and hippocampus, regions implicated in emotional processing and in developmental models of anxiety pathophysiology. Parietal and occipital cortical thickness were related to anxiety treatment response but not baseline anxiety.

A positron emission tomography study of the serotonergic system in relation to anxiety in depression

Symptoms of anxiety are highly comorbid with major depressive disorder (MDD) and are known to alter the course of the disease. To help elucidate the biological underpinnings of these prevalent disorders, we previously examined the relationship between components of anxiety (somatic, psychic and motoric) and serotonin 1A receptor (5-HT_1A) binding in MDD and found that higher psychic and lower somatic anxiety was associated with greater 5-HT_1A binding. In this work, we sought to examine the correlation between these anxiety symptom dimensions and 5-HTT binding. Positron emission tomography with [¹¹C]-3-amino-4-(3-dimethylamino-methylphenylsulfanyl)-benzonitrile ([¹¹C]DASB) and a metabolite-corrected arterial input function were used to estimate regional 5-HTT binding in 55 subjects with MDD and anxiety symptoms. Somatic anxiety was negatively correlated with 5-HTT binding in the thalamus (β=-.33, p=.025), amygdala (β=-.31, p=.007) and midbrain (β=-.72, p<.001). Psychic anxiety was positively correlated with 5-HTT binding in midbrain only (β=.46, p=.0025). To relate to our previous study, correlation between 5-HT_1A and 5-HTT binding was examined, and none was found. We also examined how much of the variance in anxiety symptom dimensions could be explained by both 5-HTT and 5-HT_1A binding. The developed model was able to explain 68% (p<.001), 38% (p=.012) and 32% (p=.038) of the total variance in somatic, psychic, and motoric anxiety, respectively. Results indicate the tight coupling between the serotonergic system and anxiety components, which may be confounded when using aggregate anxiety measures. Uncovering serotonin's role in anxiety and depression in this way may give way to a new generation of therapeutics and treatment strategies.

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7-19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post- CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment.

A trans-diagnostic perspective on obsessive-compulsive disorder

Progress in understanding the underlying neurobiology of obsessive-compulsive disorder (OCD) has stalled in part because of the considerable problem of heterogeneity within this diagnostic category, and homogeneity across other putatively discrete, diagnostic categories. As psychiatry begins to recognize the shortcomings of a purely symptom-based psychiatric nosology, new data-driven approaches have begun to be utilized with the goal of solving these problems: specifically, identifying trans-diagnostic aspects of clinical phenomenology based on their association with neurobiological processes. In this review, we describe key methodological approaches to understanding OCD from this perspective and highlight the candidate traits that have already been identified as a result of these early endeavours. We discuss how important inferences can be made from pre-existing case-control studies as well as showcasing newer methods that rely on large general population datasets to refine and validate psychiatric phenotypes. As exemplars, we take 'compulsivity' and 'anxiety', putatively trans-diagnostic symptom dimensions that are linked to well-defined neurobiological mechanisms, goal-directed learning and error-related negativity, respectively. We argue that the identification of biologically valid, more homogeneous, dimensions such as these provides renewed optimism for identifying reliable genetic contributions to OCD and other disorders, improving animal models and critically, provides a path towards a future of more targeted psychiatric treatments.

Bridging the Gaps Between Basic Science and Cognitive-Behavioral Treatments for Anxiety Disorders in Routine Care

Abstract. As a core component of cognitive-behavioral therapies (CBT), behavioral exposure is an effective treatment for anxiety disorders. Still, recent treatment studies demonstrate relatively high rates of treatment dropout, nonresponse, and relapse, indicating a substantial need for optimizing and personalizing existing treatment procedures. In the present article, we aim to address current challenges and future demands for translational research in CBT for the anxiety disorders, including (a) a better understanding of those mechanisms conferring behavioral change, (b) identifying important sources of individual variation that may act as moderators of treatment response, and (c) targeting practical barriers for dissemination of exposure therapy to routine care. Based on a recursive process model of psychotherapy research we will describe distinct steps to systematically translate basic and clinical research “from bench to bedside” to routine care, but also vice versa. Some of these aspects may stimulate the future roadmap for evidence-based psychotherapy research in order to better target the treatment of anxiety disorders as one core health challenge of our time.

Neural correlates of explicit and implicit emotion processing in relation to treatment response in pediatric anxiety

BACKGROUND

Approximately 40%-45% of youth with anxiety disorders do not achieve remission (or a substantial reduction in symptoms) following treatment, highlighting the need to identify predictors of treatment response. Given the well-established link between attentional biases and anxiety disorders in youth and adults, this study examined the neural correlates of directing attention toward and away from emotional faces in relation to pediatric anxiety treatment response.

METHOD

Prior to beginning treatment with the selective serotonin reuptake inhibitor (SSRI) sertraline or cognitive behavior therapy (CBT), 37 youth (age 7-19 years) with generalized and/or social anxiety disorder completed a task with conditions that manipulated whether participants were instructed to match emotional faces (explicit emotion processing) or match shapes in the context of emotional face distractors (implicit emotion processing) during functional magnetic resonance imaging.

RESULTS

Results revealed that reduced activation in superior frontal gyrus (SFG), encompassing the dorsal anterior cingulate cortex (ACC) and dorsomedial prefrontal cortex (PFC), during implicit processing of emotional faces predicted a greater reduction in anxiety severity pre-to-post treatment. Post hoc analyses indicated that effects were not significantly moderated by the type of treatment or anxiety type.

CONCLUSIONS

Findings suggest that less recruitment of SFG, including the dorsal ACC and dorsomedial PFC, during implicit emotion processing predicts a greater reduction in youth anxiety symptoms pre-to-post treatment. Youth who exhibit reduced activation in these areas while matching shapes in the context of emotional face distractors may have more to gain from CBT and SSRI treatment due to preexisting deficits in attentional control. These findings suggest that neuroimaging may be a useful tool for predicting which youth are most likely to benefit from anxiety treatment.

Neural correlates of graphic cigarette warning labels predict smoking cessation relapse

Exposure to graphic warning labels (GWLs) on cigarette packaging has been found to produce heightened activity in brain regions central to emotional processing and higher-order cognitive processes. The current study extends this literature by using functional magnetic resonance imaging (fMRI) to investigate neural activation in response to GWLs and use it to predict relapse in an evidence-based smoking cessation treatment program. Participants were 48 treatment-seeking nicotine-dependent smokers who completed an fMRI paradigm in which they were exposed to GWLs, text-only warning labels (TOLs), and matched control stimuli. Subsequently, they enrolled in smoking cessation treatment and their smoking behavior was monitored. Activation in bilateral amygdala, right dorsolateral prefrontal cortex, right inferior frontal gyrus, left medial temporal gyrus, bilateral occipital lobe, and bilateral fusiform gyrus was greater during GWLs than TOLs. Neural response in the ventromedial prefrontal cortex (vmPFC) during exposure to GWLs (relative to a visual control image) predicted relapse during treatment beyond baseline demographic and dependence severity, but response in the amygdala to GWLs did not. These findings suggest that neurocognitive processes in the vmPFC may be critical to understanding how GWL's induce behavior change and may be useful as a predictor of smoking cessation treatment prognosis.

Blunted neural response to anticipation, effort and consummation of reward and aversion in adolescents with depression symptomatology

Neural reward function has been proposed as a possible biomarker for depression. However, how the neural response to reward and aversion might differ in young adolescents with current symptoms of depression is as yet unclear. Thirty-three adolescents were recruited, 17 scoring low on the Mood and Feelings Questionnaire (low risk group) and 16 scoring high (high risk group). Our functional magnetic resonance imaging task measured; anticipation (pleasant/unpleasant cue), effort (achieve a pleasant taste or avoid an unpleasant taste) and consummation (pleasant/unpleasant tastes) in regions of interest; ventral medial prefrontal cortex, pregenual cingulate cortex, the insula and ventral striatum. We also examined whole brain group differences. In the regions of interest analysis we found reduced activity in the high risk group in the pregenual cingulate cortex during anticipation and reduced pregenual cingulate cortex and ventral medial prefrontal cortex during effort and consummation. In the whole brain analysis we also found reduced activity in the high risk group in the prefrontal cortex and the precuneus during anticipation. We found reduced activity in the hippocampus during the effort phase and in the anterior cingulate/frontal pole during consummation in the high risk group. Increased anhedonia measures correlated with decreased pregenual cingulate cortex activity during consummation in the high risk group only. Our results are the first to show that adolescents with depression symptoms have blunted neural responses during the anticipation, effort and consummation of rewarding and aversive stimuli. This study suggests that interventions in young people at risk of depression, that can reverse blunted responses, might be beneficial as preventative strategies.

Spontaneous alterations of regional brain activity in patients with adult generalized anxiety disorder

OBJECTIVE

We aimed to examine how spontaneous brain activity might be related to the pathophysiology of generalized anxiety disorder (GAD).

PATIENTS AND METHODS

Using resting-state functional MRI, we examined spontaneous regional brain activity in 31 GAD patients (mean age, 36.87±9.16 years) and 36 healthy control participants (mean age, 39.53±8.83 years) matched for age, education, and sex from December 2014 to October 2015. We performed a two-sample t-test on the voxel-based analysis of the regional homogeneity (ReHo) maps. We used Pearson correlation analysis to compare scores from the Hamilton Anxiety Rating Scale, Hamilton Depression Rating Scale, State-Trait Anxiety Scale-Trait Scale, and mean ReHo values.

RESULTS

We found abnormal spontaneous activity in multiple regions of brain in GAD patients, especially in the sensorimotor cortex and emotional regions. GAD patients showed decreased ReHo values in the right orbital middle frontal gyrus, left anterior cingulate cortex, right middle frontal gyrus, and bilateral supplementary motor areas, with increased ReHo values in the left middle temporal gyrus, left superior temporal gyrus, and right superior occipital gyrus. The ReHo value of the left middle temporal gyrus correlated positively with the Hamilton Anxiety Rating Scale scores.

CONCLUSION

These results suggest that altered local synchronization of spontaneous brain activity may be related to the pathophysiology of GAD.

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.

Decreased anticipated pleasure correlates with increased salience network resting state functional connectivity in adolescents with depressive symptomatology

Previous studies have found dysfunctional resting state functional connectivity (RSFC) in depressed patients. Examining RSFC might aid biomarker discovery for depression. However RSFC in young people at risk of depression has yet to be examined. 35 healthy adolescents (13-18 yrs old.) were recruited. 17 scoring high on the Mood and Feelings Questionnaire (MFQ > 27 (High Risk: HR), and 18 scoring low on the MFQ < 15 (Low Risk: LR) matched on age and gender. We selected seed regions in the salience network (SN: amygdala and pregenual anterior cingulate cortex (pgACC)) and the central executive network (CEN: dorsal medial prefrontal cortex (dmPFC)). Mood and anhedonia measures were correlated with brain connectivity. We found decreased RSFC in the HR group between the amygdala and the pgACC and hippocampus and precuneus. We also found decreased RSFC in the HR group between the pgACC and the putamen and between the dmPFC and the precuneus. The pgACC RSFC with the insula/orbitofrontal cortex correlated inversely with the anticipation of pleasure in all subjects. Increased RSFC was observed between the pgACC and the prefrontal cortex and the amygdala and the temporal pole in the HR group compared to the LR group. Our findings are the first to show that adolescents with depression symptoms have dysfunctional RSFC between seeds in the SN and CEN with nodes in the Default Mode Network. As increased connectivity between the pgACC and the insula correlated with decreased ability to anticipate pleasure, we suggest this might be mechanism underlying the risk of experiencing anhedonia, a suggested biomarker for depression.

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.

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.

Brain connectomics predict response to treatment in social anxiety disorder

We asked whether brain connectomics can predict response to treatment for a neuropsychiatric disorder better than conventional clinical measures. Pre-treatment resting-state brain functional connectivity and diffusion-weighted structural connectivity were measured in 38 patients with social anxiety disorder (SAD) to predict subsequent treatment response to cognitive behavioral therapy (CBT). We used a priori bilateral anatomical amygdala seed-driven resting connectivity and probabilistic tractography of the right inferior longitudinal fasciculus together with a data-driven multivoxel pattern analysis of whole-brain resting-state connectivity before treatment to predict improvement in social anxiety after CBT. Each connectomic measure improved the prediction of individuals' treatment outcomes significantly better than a clinical measure of initial severity, and combining the multimodal connectomics yielded a fivefold improvement in predicting treatment response. Generalization of the findings was supported by leave-one-out cross-validation. After dividing patients into better or worse responders, logistic regression of connectomic predictors and initial severity combined with leave-one-out cross-validation yielded a categorical prediction of clinical improvement with 81% accuracy, 84% sensitivity and 78% specificity. Connectomics of the human brain, measured by widely available imaging methods, may provide brain-based biomarkers (neuromarkers) supporting precision medicine that better guide patients with neuropsychiatric diseases to optimal available treatments, and thus translate basic neuroimaging into medical practice.

Neural responses to a modified Stroop paradigm in patients with complex chronic musculoskeletal pain compared to matched controls: an experimental functional magnetic resonance imaging study

Background

Chronic musculoskeletal pain (CMSKP) is attentionally demanding, complex and multi-factorial; neuroimaging research in the population seen in pain clinics is sparse. A better understanding of the neural activity underlying attentional processes to pain related information compared to healthy controls may help inform diagnosis and management in the future.

Methods

Blood oxygenation level dependent functional magnetic resonance imaging (BOLD fMRI) compared brain responses in patients with CMSKP (n = 15) and healthy controls (n = 14) while completing a modified Stroop task using pain-related, positive-emotional, and neutral control words.

Results

Response times in the Stroop task were no different for CMSKP patients compared with controls, but patients were less accurate in their responses to all word types. BOLD fMRI responses during presentation of pain-related words suggested increases in neural activation in patients compared to controls in regions previously reported as being involved in pain perception and emotion: the anterior cingulate cortex, insula and primary and secondary somatosensory cortex. No fMRI differences were seen between groups in response to positive or control words.

Conclusions

Using this modified Stroop tasks, specific differences were identified in brain activity between CMSKP patients and controls in response to pain-related information using fMRI. This provided evidence of differences in the way that pain-related information is processed in those with chronic complex musculoskeletal pain that were not detectable using the behavioural measures of speed and accuracy. The study may be helpful in gaining new insights into the impact of attention in those living with chronic pain.