Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Neural and Cognitive Predictors of Stimulant Treatment Efficacy in Medication-Naïve ADHD Adults: A Pilot Diffusion Tensor Imaging Study

OBJECTIVE

Stimulant medications are the main treatment for Attention Deficit Hyperactivity Disorder (ADHD), but overall treatment efficacy in adults has less than a 60% response rate. This study aimed to identify neural and cognitive markers predictive of longitudinal improvement in response to stimulant treatment in drug-naïve adults with ADHD.

METHOD

We used diffusion tensor imaging (DTI) and executive function measures with 36 drug-naïve adult ADHD patients in a prospective study design.

RESULTS

Structural connectivity (measured by fractional anisotropy, FA) in striatal regions correlated with ADHD clinical symptom improvement following stimulant treatment (amphetamine or methylphenidate) in better medication responders. A significant positive correlation was also found between working memory performance and stimulant-related symptom improvement. Higher pre-treatment working memory scores correlated with greater response.

CONCLUSION

These findings provide evidence of pre-treatment neural and behavioral markers predictive of longitudinal treatment response to stimulant medications in adults with ADHD.

Individual cognitive therapy reduces frontal-thalamic resting-state functional connectivity in social anxiety disorder

Introduction

Previous neuroimaging studies in social anxiety disorders (SAD) have reported potential neural predictors of cognitive behavioral therapy (CBT)-related brain changes. However, several meta-analyses have demonstrated that cognitive therapy (CT) was superior to traditional exposure-based CBT for SAD.

Objective

To explore resting-state functional connectivity (rsFC) to evaluate the response to individual CT for SAD patients.

Methods

Twenty SAD patients who attended 16-week individual CT were scanned pre- and post-therapy along with twenty healthy controls (HCs). The severity of social anxiety was assessed with the Liebowitz Social Anxiety Scale (LSAS). Multi-voxel pattern analysis (MVPA) was performed on the pre-CT data to extract regions associated with a change in LSAS (∆LSAS). Group comparisons of the seed-based rsFC analysis were performed between the HCs and pre-CT patients and between the pre-and post-CT patients.

Results

MVPA-based regression analysis revealed that rsFC between the left thalamus and the frontal pole/inferior frontal gyrus was significantly correlated with ∆LSAS (adjusted R2 = 0.65; p = 0.00002). Compared with HCs, the pre-CT patients had higher rsFCs between the thalamus and temporal pole and between the thalamus and superior/middle temporal gyrus/planum temporale (p < 0.05). The rsFC between the thalamus and the frontal pole decreased post-CT (p < 0.05).

Conclusion

SAD patients had significant rsFC between the thalamus and temporal pole, superior/middle temporal gyrus, and planum temporale, which may be indicators of extreme anxiety in social situations. In addition, rsFC between the thalamus and the frontal pole may be a neuromarker for the effectiveness of individual CT.

Predicting Dimensional Antidepressant Response to Repetitive Transcranial Magnetic Stimulation using Pretreatment Resting-state Functional Connectivity

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.

A biomarker discovery framework for childhood anxiety

Introduction

Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety.

Methods

We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls.

Results

We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders.

Conclusion

This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.

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.

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.

Social phobia of Ethiopian students: meta-analysis and systematic review

BACKGROUND

Social anxiety disorder is defined as the fear of social situations, incorporating situations that involve contact with strangers. People highly fear embarrassing themselves which includes situations like social gatherings, oral presentations, and meeting new people. People with social phobia have nonspecific fears of practicing vague or, performing specific tasks like eating or speaking in front of others. In people with social anxiety disorder, worry can arise from both the circumstance itself and embarrassment from others, for students, social phobia is an overwhelming fear of speaking in front of others or giving presentations in class. The prevalence of social phobia among different studies in Ethiopia was inconsistent and inconclusive therefore, this study showed the cumulative burden of social phobia among students in Ethiopia.

METHOD

Observational studies published on social phobia and associated factors among students in Ethiopia were included in this study based on the criteria after independent selection by two authors. Data were extracted by Microsoft Excel spreadsheet to be exported to Stata version 11 for further analysis. The random-effect model was used to estimate the pooled effect size of social phobia and its effect on the previous studies with 95% confidence intervals. Funnel plots analysis and Egger regression tests were conducted to detect the presence of publication bias. Sub-group analysis and sensitivity analysis were done.

RESULT

A total of 2878 study participants from seven studies were included in this meta-analysis and systematic review. The pooled prevalence of social phobia among students in Ethiopia was 26.81% with a 95% CI (22.31-31.30). The pooled effect size of social phobia in Oromia, Amhara, and SNNPs regions was 24.76%, 24.76%, and 29.47%, respectively. According to the subgroup analysis, university, and college/high school students were 28.05% and 25.34% respectively. Being female [AOR = 2.11 (95% CI 1.72-2.60)], having poor social support [AOR = 2.38 (95% CI 1.54-3.70)], substance use [AOR = 2.25 (95% CI 1.54-3.30)], single parent [AOR = 5.18 (95% CI 3.30-8.12)], and rural residence [AOR = 2.29 (95% CI 1.91-2.75)] were significantly associated in this meta-analysis in Ethiopia.

CONCLUSION

The pooled prevalence of social phobia in this meta-analysis and systematic review was high (26.81%) among students therefore, the educational bureau needs to work on decreasing the burden of social phobia to raise the academic achievement and creativity of the students. In therapeutic advice like exposure to presentations, family members take the responsibility for the students' therapy and expose them to various social interactions.

Neuroscience-based psychotherapy: A position paper

In the recent years, discoveries in neuroscience have greatly impacted upon the need to modify therapeutic practice starting from the evidence showing some cerebral mechanisms capable of coping with mental health crisis and traumatic events of the individual's life history by redesigning the narrative plot and the person's sense of the Self. The emerging dialogue between neuroscience and psychotherapy is increasingly intense and modern psychotherapy cannot ignore the heritage deriving from studies about neuropsychological modification of memory traces, neurobiology of attachment theory, cognitive mechanisms involved in psychopathology, neurophysiology of human empathy, neuroimaging evidence about psychotherapeutic treatment, and somatoform disorders connecting the brain and the body. In the present article, we critically examined sectorial literature and claimed that psychotherapy has to referred to a neuroscience-based approach in order to adopt the most tailored interventions for specific groups of patients or therapy settings. We also provided recommendations for care implementation in clinical practice and illustrated challenges of future research.

Time course of attentional bias in social anxiety: Evidence from visuocortical dynamics

Threat-related attentional bias is thought to have a causal influence on the etiology of social anxiety. However, there is uncertainty on whether attention dwells on or diverts away from threats, and the measurements typically utilized to explore attentional bias cannot continuously quantify changes in attention. Here, we used steady-state visual evoked potentials (ssVEPs) as a continuous neurophysiological measure of visual attentional processing to examine the time course of attentional bias in social anxiety. Participants with high (n = 18) and low (n = 18) social anxiety passively viewed two faces flickering at 15 and 20 Hz frequency to evoke ssVEPs, and completed Attentional Control Scale. The results showed that angry faces, as compared to happy and neutral faces, elicited larger ssVEP amplitudes for the time window of 180-500 ms after facial stimuli onset only in the high socially anxious individuals, and the effect extended to the next two periods of 500-1000 ms and 1000-1500 ms. The ssVEP amplitudes differed most when individuals with high social anxiety viewed angry-neutral expression combinations. Additionally, attentional control was negatively correlated with social anxiety and threat-related attentional bias. The results suggested that individuals with social anxiety initially oriented attention toward the threat and subsequently exhibited difficulty in disengaging attention from it, possibly due to impaired attentional control.

Self-compassion training for individuals with social anxiety disorder: a preliminary randomized controlled trial

Self-compassion is the ability to offer oneself kindness and compassion in response to failure, suffering, or insecurity. Learning how to be self-compassionate through self-compassion training appears effective for improving psychological well-being in community samples and promising for clinical populations. The current randomized controlled trial was designed to (a) examine the effectiveness of a self-guided self-compassion training program; and (b) determine whether self-compassion training can help mitigate social anxiety disorder (SAD) symptoms. Adults with SAD (n = 63; Mage = 34.3, SD = 11.4; 67.8% female; 84.7% Caucasian) were randomized to a waitlist control condition, a self-guided self-compassion training condition, or a self-guided applied relaxation training condition for six weeks. Outcome measures of SAD symptoms and self-compassion were completed pre-, mid-, and post-treatment, as well as at 3-months follow-up. Multilevel linear modelling results suggested the self-compassion training program was statistically superior at improving outcome measures relative to the waitlist control condition (ps < .05; η²_ps = .12-.33), but not relative to the applied relaxation training condition (ps > .05; η²_ps = .01-.05). Self-compassion training produced greater clinically significant gains in self-compassion and reductions in fear of self-compassion compared to both the waitlist condition and applied relaxation training. The current trial provides preliminary evidence for the effectiveness of a self-help self-compassion training program and provides evidence that self-compassion training may be beneficial for managing clinically significant SAD symptoms.

Brain response to emotional faces in anxiety and depression: neural predictors of cognitive behavioral therapy outcome and predictor-based subgroups following therapy

BACKGROUND

Neuroimaging studies have shown variance in brain response to emotional faces predicts cognitive behavioral therapy (CBT) outcome. An important next step is to determine if individual differences in neural predictors of CBT response represent distinct patient groups.

METHODS

In total, 90 patients with internalizing disorders completed a face-matching task during functional magnetic resonance imaging before and after 12 weeks of CBT and 45 healthy controls completed the task before and after 12 weeks. Patients exhibiting a pre-to-post CBT >50% reduction in symptom severity on two measures were considered treatment responders. Regions of interest (ROIs) for angry, fearful, and happy faces were submitted to receiver operating characteristic (ROC) curve analysis. Significant ROIs were then submitted to decision tree analysis to classify responder/non-responder subgroups. Psychophysiological interactions (PPI) were used to explore functional connectivity in the region(s) that delineated subgroups.

RESULTS

A total of 51 patients were treatment responders and ROC curve results were significant for all face types though specific regions varied. Decision tree results revealed superior occipital response to angry faces identified patient subgroups such that the subgroup with 'high' occipital activity had more responders than the 'low' occipital subgroup. Following CBT, the high, relative to low, occipital subgroup was less symptomatic. Controls exhibited stable superior occipital activation over time. Whole-brain PPI showed reduced baseline superior occipital-postcentral gyrus functional connectivity in responders compared to non-responders.

CONCLUSIONS

Preliminary findings indicate patients characterized by relatively more pre-treatment superior occipital gyrus engagement to angry faces and reduced superior occipital-postcentral gyrus connectivity, relative to non-responders, may represent a phenotype likely to benefit from CBT.

Pain-related reorganization in the primary somatosensory cortex of patients with postherpetic neuralgia

Studies on functional and structural changes in the primary somatosensory cortex (S1) have provided important insights into neural mechanisms underlying several chronic pain conditions. However, the role of S1 plasticity in postherpetic neuralgia (PHN) remains elusive. Combining psychophysics and magnetic resonance imaging (MRI), we investigated whether pain in PHN patients is linked to S1 reorganization as compared with healthy controls. Results from voxel-based morphometry showed no structural differences between groups. To characterize functional plasticity, we compared S1 responses to noxious laser stimuli of a fixed intensity between both groups and assessed the relationship between S1 activation and spontaneous pain in PHN patients. Although the intensity of evoked pain was comparable in both groups, PHN patients exhibited greater activation in S1 ipsilateral to the stimulated hand. Pain-related activity was identified in contralateral superior S1 (SS1) in controls as expected, but in bilateral inferior S1 (IS1) in PHN patients with no overlap between SS1 and IS1. Contralateral SS1 engaged during evoked pain in controls encoded spontaneous pain in patients, suggesting functional S1 reorganization in PHN. Resting-state fMRI data showed decreased functional connectivity between left and right SS1 in PHN patients, which scaled with the intensity of spontaneous pain. Finally, multivariate pattern analyses (MVPA) demonstrated that BOLD activity and resting-state functional connectivity of S1 predicted within-subject variations of evoked and spontaneous pain intensities across groups. In summary, functional reorganization in S1 might play a key role in chronic pain related to PHN and could be a potential treatment target in this patient group.

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.

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.

Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach

BACKGROUND

Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.

METHODS

In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).

RESULTS

At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.

CONCLUSIONS

The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.

Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.

Evaluation of Cognitive Behavioral Therapy vs Mindfulness Meditation in Brain Changes During Reappraisal and Acceptance Among Patients With Social Anxiety Disorder: A Randomized Clinical Trial

IMPORTANCE

Cognitive behavioral group therapy (CBGT) and mindfulness-based stress reduction (MBSR) are thought to help patients with social anxiety disorder (SAD) via distinct emotion-regulation mechanisms. However, no study has compared the effects of CBGT and MBSR on brain and negative emotion indicators of cognitive reappraisal and acceptance in patients with SAD.

OBJECTIVE

To investigate the effects of CBGT and MBSR on reappraisal and acceptance in patients with SAD and to test whether treatment-associated brain changes are associated with social anxiety symptoms 1 year posttreatment.

DESIGN, SETTING, AND PARTICIPANTS

In this randomized clinical trial, a total of 108 unmedicated adults diagnosed with generalized SAD were randomly assigned to 12 weeks of CBGT, MBSR, or waitlist. The final sample included 31 patients receiving CBGT, 32 patients receiving MBSR, and 32 waitlist patients. Data were collected at the psychology department at Stanford University from September 2012 to December 2014. Data were analyzed from February 2019 to December 2020.

INTERVENTIONS

CBGT and MBSR.

MAIN OUTCOMES AND MEASURES

Changes in self-reported negative emotion and functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) signal within an a priori-defined brain search region mask derived from a meta-analysis of cognitive reappraisal and attention regulation 1 year posttreatment.

RESULTS

Of 108 participants, 60 (56%) were female. The mean (SD) age was 32.7 (8.0) years. Self-reported race and ethnicity data were collected to inform the generalizability of the study to the wider population and to satisfy the requirements of the National Institutes of Health. From the categories provided by the National Institutes of Health, 47 participants selected White (43.5%), 42 selected Asian (38.9%) 10 selected Latinx (9.3%), 1 selected Black (1%), 1 selected Native American (1%), and 7 selected more than 1 race (6.5%). CBGT and MBSR were associated with a significant decrease in negative emotion (partial η2 range, 0.38 to 0.53) with no significant between-group differences when reacting (β, -0.04; SE, 0.09; 95% CI, -0.11 to 0.08; t92 = -0.37; P = .71), reappraising (β, -0.15; SE, 0.09; 95% CI, -0.32 to 0.03; t92 = -1.67; P = .10), or accepting (β, -0.05; SE, 0.08; 95% CI, -0.20 to 0.11; t92 = -0.59; P = .56). There was a significant increase in BOLD percentage signal change in cognitive and attention-regulation regions when reappraising (CBGT = 0.031; MBSR = 0.037) and accepting (CBGT = 0.012; MBSR = 0.077) negative self-beliefs. CBGT and MBSR did not differ in decreased negative emotion and increased reappraisal and acceptance BOLD responses. Reappraisal-associated MBSR (vs CBGT) negative emotions and CBGT (vs MBSR) brain responses were associated with social anxiety symptoms 1 year posttreatment.

CONCLUSIONS AND RELEVANCE

The results of this study suggest that CBGT and MBSR may be effective treatments with long-term benefits for patients with SAD that recruit cognitive and attention-regulation brain networks. Despite contrasting models of therapeutic change, CBT and MBSR may both enhance reappraisal and acceptance emotion regulation strategies.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02036658.

Leveraging big data to map neurodevelopmental trajectories in pediatric anxiety

Anxiety disorders are the most prevalent psychiatric condition among youth, with symptoms commonly emerging prior to or during adolescence. Delineating neurodevelopmental trajectories associated with anxiety disorders is important for understanding the pathophysiology of pediatric anxiety and for early risk identification. While a growing literature has yielded valuable insights into the nature of brain structure and function in pediatric anxiety, progress has been limited by inconsistent findings and challenges common to neuroimaging research. In this review, we first discuss these challenges and the promise of ‘big data’ to map neurodevelopmental trajectories in pediatric anxiety. Next, we review evidence of age-related differences in neural structure and function among anxious youth, with a focus on anxiety-relevant processes such as threat and safety learning. We then highlight large-scale cross-sectional and longitudinal studies that assess anxiety and are well positioned to inform our understanding of neurodevelopment in pediatric anxiety. Finally, we detail relevant challenges of ‘big data’ and propose future directions through which large publicly available datasets can advance knowledge of deviations from normative brain development in anxiety. Leveraging ‘big data’ will be essential for continued progress in understanding the neurobiology of pediatric anxiety, with implications for identifying markers of risk and novel treatment targets.

The Daily Activity Study of Health (DASH): A pilot randomized controlled trial to enhance physical activity in sedentary older adults

Sedentary behavior increases the risk for multiple chronic diseases, early mortality, and accelerated cognitive decline in older adults. Interventions to reduce sedentary behavior among older adults are needed to improve health outcomes and reduce the burden on healthcare systems. We designed a randomized controlled trial that uses a self-affirmation manipulation and gain-framed health messaging to effectively reduce sedentary behavior in older adults. This message-based intervention lasts 6 weeks, recruiting 80 healthy but sedentary older adults from the community, between the ages of 60 and 95 years. Participants are randomly assigned to one of two groups: 1) an intervention group, which receives self-affirmation followed by gain-framed health messages daily or 2) a control group, which receives daily loss-framed health messages only. Objective physical activity engagement is measured by accelerometers. Accelerometers are deployed a week before, during, and the last week of intervention to examine potential changes in sedentary time and physical activity engagement. Participants undertake structural and functional (resting and task-based) MRI scans, neuropsychological tests, computerized behavioral measures, and neurobehavioral inventories at baseline and after the intervention. A 3-month follow-up assesses the long-term maintenance of any engendered behaviors from the intervention period. This study will assess the effectiveness of a novel behavioral intervention at reducing sedentarism in older adults and examine the neurobehavioral mechanisms underlying any such changes.

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.

Neural correlates of cognitive behavioral therapy response in youth with negative valence disorders: A systematic review of the literature

BACKGROUND

Cognitive-behavioral therapy (CBT) is the gold-standard psychotherapeutic treatment for pediatric negative valence disorders. However, some youths do not respond optimally to treatment, which may be due to variations in neural functioning.

METHODS

We systematically reviewed functional magnetic resonance imaging studies in youths with negative valence disorders to identify pre- and post-treatment neural correlates of CBT response.

RESULTS

A total of 21 studies were identified, of overall weak to moderate quality. The most consistent findings across negative valence disorders consisted of associations of treatment response with pre- and post-treatment task-based activation and/or functional connectivity within and between the prefrontal cortex, the medial temporal lobe, and other limbic regions. Associations of CBT response with baseline and/or post-treatment activity in the striatum, precentral and postcentral gyri, medial and posterior cingulate cortices, and parietal cortex, connectivity within and between the default-mode, cognitive control, salience, and frontoparietal networks, and metrics of large-scale brain network organization, were also reported, although less consistently.

LIMITATIONS

The poor quality and limited number of studies and the important heterogeneity of study designs and results considerably limit the conclusions that can be drawn from this literature.

CONCLUSIONS

Despite these limitations, these findings provide preliminary evidence suggesting youths presenting certain patterns of brain function may respond better to CBT, whereas others may benefit from alternative or augmented forms of treatment.

A process-based approach to psychological diagnosis and treatment:The conceptual and treatment utility of an extended evolutionary meta model

For half a century, the dominant paradigm in psychotherapy research has been to develop syndrome-specific treatment protocols for hypothesized but unproved latent disease entities, as defined by psychiatric nosological systems. While this approach provided a common language for mental health problems, it failed to achieve its ultimate goal of conceptual and treatment utility. Process-based therapy (PBT) offers an alternative approach to understanding and treating psychological problems, and promoting human prosperity. PBT targets empirically established biopsychosocial processes of change that researchers have shown are functionally important to long terms goals and outcomes. By building on concepts of known clinical utility, and organizing them into coherent theoretical models, an idiographic, functional-analytic approach to diagnosis is within our grasp. We argue that a multi-dimensional, multi-level extended evolutionary meta-model (EEMM) provides consilience and a common language for process-based diagnosis. The EEMM applies the evolutionary concepts of context-appropriate variation, selection, and retention to key biopsychosocial dimensions and levels related to human suffering, problems, and positive functioning. The EEMM is a meta-model of diagnostic and intervention approaches that can accommodate any set of evidence-based change processes, regardless of the specific therapy orientation. In a preliminary way, it offers an idiographic, functional analytic, and clinically useful alternative to contemporary psychiatric nosological systems.

Treatment Response Biomarkers in Anxiety Disorders: From Neuroimaging to Neuronally-Derived Extracellular Vesicles and Beyond

Multiple and diverse psychotherapeutic or psychopharmacologic treatments effectively reduce symptoms for many patients with anxiety disorders, but the trajectory and magnitude of response vary considerably. This heterogeneity of treatment response has invigorated the search for biomarkers of treatment response in anxiety disorders, across the lifespan. In this review, we summarize evidence for biomarkers of treatment response in children, adolescents and adults with generalized, separation and social anxiety disorders as well as panic disorder. We then discuss the relationship between these biomarkers of treatment response and the pathophysiology of anxiety disorders. Finally, we provide context for treatment response biomarkers of the future, including neuronally-derived extracellular vesicles in anxiety disorders and discuss challenges that must be overcome prior to the debut of treatment response biomarkers in the clinic. A number of promising treatment response biomarkers have been identified, although there is an urgent need to replicate findings and to identify which biomarkers might guide clinicians in selecting from available treatments rather than just simply identifying patients who may be less likely to respond to a given intervention.

Predicting Response to Group Cognitive Behavioral Therapy in Asthma by a Small Number of Abnormal Resting-State Functional Connections

Group cognitive behavioral therapy (GCBT) is a successful psychotherapy for asthma. However, response varies considerably among individuals, and identifying biomarkers of GCBT has been challenging. Thus, the aim of this study was to predict an individual's potential response by using machine learning algorithms and functional connectivity (FC) and to improve the personalized treatment of GCBT. We use the lasso method to make the feature selection in the functional connections between brain regions, and we utilize t-test method to test the significant difference of these selected features. The feature selections are performed between controls (size = 20) and pre-GCBT patients (size = 20), pre-GCBT patients (size = 10) and post-GCBT patients (size = 10), and post-GCBT patients (size = 10) and controls (size = 10). Depending on these features, support vector classification was used to classify controls and pre- and post-GCBT patients. Pearson correlation analysis was employed to analyze the associations between clinical symptoms and the selected discriminated FCs in post-GCBT patients. At last, linear support vector regression was applied to predict the therapeutic effect of GCBT. After feature selection and significant analysis, five discriminated FC regarding neuroimaging biomarkers of GCBT were discovered, which are also correlated with clinical symptoms. Using these discriminated functional connections, we could accurately classify the patients before and after GCBT (classification accuracy, 80%) and predict the therapeutic effect of GCBT in asthma (predicted accuracy, 67.8%). The findings in this study would provide a novel sight toward GCBT response prediction and further confirm neural underpinnings of asthma. Moreover, our findings had clinical implications for personalized treatment by identifying asthmatic patients who will be appropriate for GCBT.

CLINICAL TRIAL REGISTRATION

The brain mechanisms of group cognitive behavioral therapy to improve the symptoms of asthma (Registration number: Chi-CTR-15007442, http://www.chictr.org.cn/index.aspx).

A Synthetic Literature Review on the Management of Emerging Treatment Resistance in First Episode Psychosis: Can We Move towards Precision Intervention and Individualised Care?

Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a ‘one-size fits all’ approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards ‘big data’ and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one’s illness may also be key to understanding the factors that drive illness course.

Defining data-driven subgroups of obsessive-compulsive disorder with different treatment responses based on resting-state functional connectivity

Characterization of obsessive-compulsive disorder (OCD), like other psychiatric disorders, suffers from heterogeneities in its symptoms and therapeutic responses, and identification of more homogeneous subgroups may help to resolve the heterogeneity. We aimed to identify the OCD subgroups based on resting-state functional connectivity (rsFC) and to explore their differences in treatment responses via a multivariate approach. From the resting-state functional MRI data of 107 medication-free OCD patients and 110 healthy controls (HCs), we selected rsFC features, which discriminated OCD patients from HCs via support vector machine (SVM) analyses. With the selected brain features, we subdivided OCD patients into subgroups using hierarchical clustering analyses. We identified 35 rsFC features that achieved a high sensitivity (82.74%) and specificity (76.29%) in SVM analyses. The OCD patients were subdivided into two subgroups, which did not show significant differences in their demographic and clinical backgrounds. However, one of the OCD subgroups demonstrated more impaired rsFC that was involved either within the default mode network (DMN) or between DMN brain regions and other network regions. This subgroup also showed both lower improvements in symptom severity in the 16-week follow-up visit and lower responder percentage than the other subgroup. Our results highlight that not only abnormalities within the DMN but also aberrant rsFC between the DMN and other networks may contribute to the treatment response and support the importance of these neurobiological alterations in OCD patients. We suggest that abnormalities in these connectivity may play predictive biomarkers of treatment response, and aid to build more optimal treatment strategies.

Neonatal brain volume as a marker of differential susceptibility to parenting quality and its association with neurodevelopment across early childhood

Parenting quality is associated with child cognitive and executive functions (EF), which are important predictors of social and academic development. However, children vary in their susceptibility to parenting behaviors, and the neurobiological underpinnings of this susceptibility are poorly understood. In a prospective longitudinal study, we examined whether neonatal total brain volume (TBV) and subregions of interest (i.e., hippocampus (HC) and anterior cingulate gyrus (ACG)) moderate the association between maternal sensitivity and cognitive/EF development across early childhood. Neonates underwent a brain magnetic resonance imaging scan. Their cognitive performance and EF was characterized at 2.0 ± 0.1 years (N = 53) and at 4.9 ± 0.8 years (N = 36) of age. Maternal sensitivity was coded based on observation of a standardized play situation at 6-mo postpartum. Neonatal TBV moderated the association between maternal sensitivity and 2-year working memory as well as all 5-year cognitive outcomes, suggesting that the positive association between maternal sensitivity and child cognition was observed only among children with large or average but not small TBV as neonates. Similar patterns were observed for TBV-corrected HC and ACG volumes. The findings suggest that larger neonatal TBV, HC and ACG may underlie susceptibility to the environment and affect the degree to which parenting quality shapes long-term cognitive development.

Exposure to food in anorexia nervosa and brain correlates of food-related anxiety: findings from a pilot study

BACKGROUND

Although the primary target of treatment for anorexia nervosa (AN) is weight gain, established psychological interventions focus on maintaining factors of AN, and do not specifically address eating behaviours. We have previously reported results of a case series investigating in-vivo food exposure in AN, demonstrating the feasibility and acceptability of this treatment together with evidence of significant clinical change (Cardi, Leppanen, Mataix-Cols, Campbell, & Treasure, 2019). The current study examined the neural circuitry of food-related anxiety.

METHODS

We examined neural reactivity (fMRI) to food images pre- and post-food exposure therapy (n=16), and compared it to a group of healthy control participants (HC n=21) who were scanned on two occasions.

RESULTS

Prior to treatment, the AN group (compared to HC) showed less reactivity in the anterior cingulate cortex (ACC). Following exposure treatment, patients (compared to HC), show increased activity in the dorsolateral prefrontal cortex, decreased activity in the superior parietal lobe and no differences in the ACC. The level of activation of the insula (pre-treatment) predicted the degree of post-treatment reduction in self-reported food anxiety in AN. Changes in food-related anxiety were also associated with changes in neural activation in a cluster located in the middle temporal gyrus/lateral parietal cortex.

LIMITATIONS

The primary limitations of this work are the small sample size and lack of patient comparison group.

CONCLUSIONS

Exposure to food in AN may be associated with changes in neural circuitries implicated in emotion regulation and attentional processes. However, these findings need replication in larger and controlled studies.

A Comparison of Two Neurobiological Models of Fear and Anxiety: A "Construct Validity" Application?

The main focus of this article is on an application of "construct validity," although it is better thought of as a construct-progressivity assessment (CPA) for reasons developed in the article and related to the concepts of "truth" and "validity" in science. The specific example presented involves the recent LeDoux and Pine two-system model (TSM) and the more traditional fear-center model (FCM), two important constructs in even broader debates in recent fear research. The focal point of the TSM-FCM dispute is arguably the contrasting interpretation of four empirical "findings" that are summarized in a section on findings of this article and then explored later in depth as "empirical arguments." This notion of an empirical argument is closely related to Kane's "argument-based" analysis of construct validity. In addition, it is essential to describe and then apply what are called "epistemic values" to the TSM-FCM example. The CPA in the present article ultimately tilts in favor of the TSM and against the FCM, on empirical as well as on more general epistemic-value grounds, with the caveat that any CPA is temporally contingent and may reach a different conclusion later, depending on future instruments and advances.

Visual cortical regions show sufficient test-retest reliability while salience regions are unreliable during emotional face processing

Functional magnetic resonance imaging studies frequently use emotional face processing tasks to probe neural circuitry related to psychiatric disorders and treatments with an emphasis on regions within the salience network (e.g., amygdala). Findings across previous test-retest reliability studies of emotional face processing have shown high variability, potentially due to differences in data analytic approaches. The present study comprehensively examined the test-retest reliability of an emotional faces task utilizing multiple approaches to region of interest (ROI) analysis and by examining voxel-wise reliability across the entire brain for both neural activation and functional connectivity. Analyses included 42 healthy adult participants who completed an fMRI scan concurrent with an emotional faces task on two separate days with an average of 25.52 days between scans. Intraclass correlation coefficients (ICCs) were calculated for the 'FACES-SHAPES' and 'FACES' (compared to implicit baseline) contrasts across the following: anatomical ROIs identified from a publicly available brain atlas (i.e., Brainnetome), functional ROIs consisting of 5-mm spheres centered on peak voxels from a publicly available meta-analytic database (i.e., Neurosynth), and whole-brain, voxel-wise analysis. Whole-brain, voxel-wise analyses of functional connectivity were also conducted using both anatomical and functional seed ROIs. While group-averaged neural activation maps were consistent across time, only one anatomical ROI and two functional ROIs showed good or excellent individual-level reliability for neural activation. The anatomical ROI was the right medioventral fusiform gyrus for the FACES contrast (ICC ​= ​0.60). The functional ROIs were the left and the right fusiform face area (FFA) for both FACES-SHAPES and FACES (Left FFA ICCs ​= ​0.69 & 0.79; Right FFA ICCs ​= ​0.68 & 0.66). Poor reliability (ICCs ​< ​0.4) was identified for almost all other anatomical and functional ROIs, with some exceptions showing fair reliability (ICCs ​= ​0.4-0.59). Whole-brain voxel-wise analysis of neural activation identified voxels with good (ICCs ​= ​0.6-0.74) to excellent reliability (ICCs ​> ​0.75) that were primarily located in visual cortex, with several clusters in bilateral dorsal lateral prefrontal cortex (DLPFC). Whole-brain voxel-wise analyses of functional connectivity for amygdala and fusiform gyrus identified very few voxels with good to excellent reliability using both anatomical and functional seed ROIs. Exceptions included clusters in right cerebellum and right DLPFC that showed reliable connectivity with left amygdala (ICCs ​> ​0.6). In conclusion, results indicate that visual cortical regions demonstrate good reliability at the individual level for neural activation, but reliability is generally poor for salience regions often focused on within psychiatric research (e.g., amygdala). Given these findings, future clinical neuroimaging studies using emotional faces tasks to examine individual differences might instead focus on visual regions and their role in psychiatric disorders.

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).

Changes in functional connectivity with cognitive behavioral therapy for social anxiety disorder predict outcomes at follow-up

Approximately half of individuals with Social Anxiety Disorder (SAD) treated with psychological intervention do not achieve clinically significant improvement or retain long-term gains. Neurobiological models of SAD propose that disruptions in functioning of amygdala-prefrontal circuitry is implicated in short-term treatment response. However, whether treatment-related changes in functional connectivity predict long-term well-being after psychotherapy is unknown. Patients with SAD completed an incidental emotion regulation task during fMRI before and after treatment with cognitive behavioral therapy or acceptance and commitment therapy (n = 23, collapsed across groups). Psychophysiological interaction analyses using amygdala seed regions were conducted to assess changes in functional connectivity from pre-to post-treatment that predicted symptom change from 6 to 12-month follow-up. Negative change (i.e., greater inverse/weaker positive) in amygdala connectivity with the dorsomedial prefrontal cortex (dmPFC) and dorsal anterior cingulate cortex (dACC) predicted greater symptom reduction during follow-up. Positive change in amygdala connectivity with the cerebellum, fusiform gyrus, and pre-central and post-central gyri predicted less symptom reduction (e.g., no change or worsening). Results suggest that strengthened amygdala connectivity with regulatory regions may promote better long-term outcomes, whereas changes with visual and sensorimotor regions may represent sensitization to emotion-related cues, conferring poorer outcomes. Clinical implications for treatment personalization are discussed, should effects replicate in larger samples.

Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning

BACKGROUND

Tailoring healthcare to patients' individual needs is a central goal of precision medicine. Combining smartphone-based interventions with machine learning approaches may help attaining this goal. The aim of our study was to explore the predictability of the success of smartphone-based psychotherapeutic micro-interventions in eliciting mood changes using machine learning.

METHODS

Participants conducted daily smartphone-based psychotherapeutic micro-interventions, guided by short video clips, for 13 consecutive days. Participants chose one of four intervention techniques used in psychotherapeutic approaches. Mood changes were assessed using the Multidimensional Mood State Questionnaire. Micro-intervention success was predicted using random forest (RF) tree-based mixed-effects logistic regression models. Data from 27 participants were used, totaling 324 micro-interventions, randomly split 100 times into training and test samples, using within-subject and between-subject sampling.

RESULTS

Mood improved from pre- to post-intervention in 137 sessions (initial success-rate: 42.3%). The RF approach resulted in predictions of micro-intervention success significantly better than the initial success-rate within and between subjects (positive predictive value: 0.732 (95%-CI: 0.607; 0.820) and 0.698 (95%-CI: 0.564; 0.805), respectively). Prediction quality was highest using the RF approach within subjects (rand accuracy: 0.75 (95%-CI: 0.641; 0.840), Matthew's correlation coefficient: 0.483 (95%-CI: 0.323; 0.723)).

LIMITATIONS

The RF approach does not allow firm conclusions about the exact contribution of each factor to the algorithm's predictions. We included a limited number of predictors and did not compare whether predictability differed between psychotherapeutic techniques.

CONCLUSIONS

Our findings may pave the way for translation and encourage scrutinizing personalized prediction in the psychotherapeutic context to improve treatment efficacy.

pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.

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.

Amantadine exerts anxiolytic like effect in mice: Evidences for the involvement of nitrergic and GABAergic signaling pathways

Amantadine is a glutamatergic antagonist that works by inhibiting the NMDA receptor. Besides the inhibition of NMDA receptors amantadine also stabilizes the glutamatergic system and protects the neurons against the NMDA toxicity. Amantadine treatment also reduces the production of NO and metabolism of GABA. Therefore amantadine modulates glutamate, GABA and NO which are known to be implicated in the pathogenesis of anxiety and related behavior. The present study was designed to investigate the anxiolytic like effect of amantadine in mice. Nitrergic and GABAergic signaling influence in the anxiolytic like effect of amantadine was also studied. Amantadine (25, 50 and 75 mg/kg, i.p.) was administered and the anxiety related behavior was determined using light and dark box (LDB) and elevated plus maze (EPM) methods. Further, the effect of various treatments on the whole brain glutamate, nitrite and GABA levels were also determined. The results obtained demonstrated that the amantadine (50 mg/kg, i.p.) exerted anxiolytic like effect in mice and reduced the levels of glutamate, nitrite and GABA in the brain of mice as compared to control. Further, the influence of NO and GABA in the anxiolytic like effect of the amantadine was also determined. The results obtained demonstrated that NO donor counteracted while NO inhibitor potentiated the anxiolytic like effect of amantadine in mice. Also the combined treatment of amantadine (25 mg/kg, i.p.) and diazepam (1 mg/kg, i.p.) did not affect the anxiety related behavior, brain GABA and nitrite level of mice but reduced the levels the brain glutamate levels significantly as compared to amantadine (25 mg/kg, i.p.) and diazepam (1 mg/kg, i.p.) treated mice. Thus, amantadine exerted anxiolytic like effect in mice and the anxiolytic like effect of amantadine was modulated by nitrergic and GABAergic signaling pathway.

Clinical Strategies and Technical Challenges in Psychoradiology

Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of treatment response and prognosis, and illness risk prediction for patients with psychiatric disorders. Addressing complex issues, such as the biological heterogeneity of psychiatric syndromes and unclear neurobiological mechanisms underpinning radiological abnormalities, is a challenge that needs to be resolved. With the advance of multimodal imaging and more efforts in standardization of image acquisition and analysis, psychoradiology is becoming a promising tool for the future of clinical care for patients with psychiatric disorders.

Mesocorticolimbic Pathways Encode Cue-Based Expectancy Effects on Pain

Expectation interacting with nociceptive input can shape the perception of pain. It has been suggested that reward-related expectations are associated with the activation of the ventral tegmental area (VTA), which projects to the striatum (e.g., nucleus accumbens [NAc]) and prefrontal cortex (e.g., rostral anterior cingulate cortex [rACC]). However, the role of these projection pathways in encoding expectancy effects on pain remains unclear. In this study, we leveraged a visual cue conditioning paradigm with a long pain anticipation period and collected magnetic resonance imaging (MRI) data from 30 healthy human subjects (14 females). At the within-subject level, whole-brain functional connectivity (FC) analyses showed that the mesocortical pathway (VTA-rACC FC) and the mesolimbic pathway (VTA-NAc FC) were enhanced with positive expectation but inhibited with negative expectation during pain anticipation period. Mediation analyses revealed that cue-based expectancy effects on pain were mainly mediated by the VTA-NAc FC, and structural equation modeling showed that VTA-based FC influenced pain perception by modulating pain-evoked brain responses. At the between-subject level, multivariate pattern analyses demonstrated that gray matter volumes in the VTA, NAc, and rACC were able to predict the magnitudes of conditioned pain responses associated with positive and/or negative expectations across subjects. Our results therefore advance the current understanding of how the reward system is linked to the interaction between expectation and pain. Furthermore, they provide precise functional and structural information on mesocorticolimibic pathways that encode within-subject and between-subject variability of expectancy effects on pain.SIGNIFICANCE STATEMENT Studies have suggested that reward-related expectation is associated with the activation of the VTA, which projects to the striatum and prefrontal cortex. However, the role of these projection pathways in encoding expectancy effects on pain remains unclear. Using multimodality MRI and a visual cue conditioning paradigm, we found that the functional connectivity and gray matter volumes in key regions (the VTA, NAc, and rostral ACC) within the mesocorticolimbic pathways encoded expectancy effects on pain. Our results advance the current understanding of how the reward system is linked to the interaction between expectation and pain, and provide precise functional and structural information on mesocorticolimbic pathways that encode expectancy effects on pain.

The effects of age on cerebral responses to self-initiated actions during social interactions: An exploratory study

Self-initiated action is critical to social interaction and individuals with social anxiety find it particularly difficult to initiate social interactions. We showed earlier that social exclusion encumbered self-initiated actions in the Cyberball task in young adults. Here, we examined whether the behavioral performance and regional responses during self-initiated actions vary with age in 53 participants (21-74 years; 27 men). Behaviorally, participants were slower in tossing the ball during exclusion (EX) than during fair game (FG) sessions in both men and women. In women but not in men the reaction time (RT) burden (RT_EX - RT_FG; RT prolonged during social exclusion) of ball toss was positively correlated with age despite no observed sex difference in Social Interaction Anxiety Scale scores. The pregenual anterior cingulate cortex, thalamus, left occipital cortex (OC) and left insula/orbitofrontal cortex responded to ball toss in EX vs. FG in negative correlation with age in women but not in men. Further, the activation of left OC fully mediated the relationship between age and RT burden in women. Thus, older women are more encumbered in self-initiated action during social exclusion, although this behavioral burden is not reflected in subjective reports of social anxiety. Age-related diminution in OC activities may reflect the neural processes underlying the difficulty in initiating social interactions in women. Together, the findings identified age-sensitive behavioral and neural processes of self-initiated action in the Cyberball task and suggest the importance of considering age and sex differences in studies of social interaction.

Principal component analysis and brain-based predictors of emotion regulation in anxiety and depression

BACKGROUND

Reappraisal, an adaptive emotion regulation strategy, is associated with frontal engagement. In internalizing psychopathologies (IPs) such as anxiety and depression frontal activity is atypically reduced suggesting impaired regulation capacity. Yet, successful reappraisal is often demonstrated at the behavioral level. A data-driven approach was used to clarify brain and behavioral relationships in IPs.

METHODS

During functional magnetic resonance imaging, anxious [general anxiety disorder (n = 43), social anxiety disorder (n = 72)] and depressed (n = 47) patients reappraised negative images to reduce negative affect ('ReappNeg') and viewed negative images ('LookNeg'). After each trial, the affective state was reported. A cut-point (i.e. values <0 based on ΔReappNeg-LookNeg) demarcated successful reappraisers. Neural activity for ReappNeg-LookNeg, derived from 37 regions of interest, was submitted to Principal Component Analysis (PCA) to identify unique components of reappraisal-related brain response. PCA factors, symptom severity, and self-reported habitual reappraisal were submitted to discriminant function analysis and linear regression to examine whether these data predicted successful reappraisal (yes/no) and variance in reappraisal ability.

RESULTS

Most patients (63%) were successful reappraisers according to the behavioral criterion (values<0; ΔReappNeg-LookNeg). Discriminant function analysis was not significant for PCA factors, symptoms, or habitual reappraisal. For regression, more activation in a factor with high loadings for frontal regions predicted better reappraisal facility. Results were not significant for other variables.

CONCLUSIONS

At the individual level, more activation in a 'frontal' factor corresponded with better reappraisal facility. However, neither brain nor behavioral variables classified successful reappraisal (yes/no). Findings suggest individual differences in regions strongly implicated in reappraisal play a role in on-line reappraisal capability.

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.

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

Neuroimaging findings as predictors of treatment outcome of psychotherapy in anxiety disorders

Anxiety disorders are the largest group of mental disorders and a leading cause of impairment, implicating in high costs for health systems and society. Effective pharmacological and psychological treatments are available, but a significant fraction of these patients does not respond adequately to these treatments. The objective of this study is to identify neuroimaging findings that could predict response to psychotherapy in anxiety disorders.

METHODS

The authors reviewed psychotherapy clinical trials with neuroimaging conducted with patients with anxiety disorders. A systematic review was performed in MEDLINE database through PubMed, the Cochrane Collaboration's Clinical Trials Register (CENTRAL), PsycINFO and Thomson Reuters's Web of Science.

RESULTS

From the studies included in this review, 24 investigated anxiety disorder patients, and findings in the amygdala, dorsolateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC) and insula predicted response to psychotherapy in social anxiety disorder. Findings in ACC, hippocampus, insula, dlPFC, amygdala and inferior frontal gyrus (iFG) predicted response to psychotherapy in panic disorder and generalized anxiety disorder.

LIMITATIONS

There was great heterogeneity between the included studies regarding neuroimaging techniques and the tasks performed during functional neuroimaging.

CONCLUSION

Neuroimaging studies suggest that abnormalities in hippocampus, amygdala, iFG, uncus and areas linked with emotional regulation (dlPFC and ACC), predict a good outcome to psychotherapy in anxiety disorders.

Translational machine learning for psychiatric neuroimaging

Despite its initial promise, neuroimaging has not been widely translated into clinical psychiatry to assist in the prediction of diagnoses, prognoses, and optimal therapeutic strategies. Machine learning approaches may enhance the translational potential of neuroimaging because they specifically focus on overcoming biases by optimizing the generalizability of pipelines that measure complex brain patterns to predict targets at a single-subject level. This article introduces some fundamentals of a translational machine learning approach before selectively reviewing literature to-date. Promising initial results are then balanced by the description of limitations that should be considered in order to interpret existing research and maximize the possibility of future translation. Future directions are then presented in order to inspire further research and progress the field towards clinical translation.

Connectome-Based Prediction of Cocaine Abstinence

OBJECTIVE

The authors sought to identify a brain-based predictor of cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. CPM is a predictive tool and a method of identifying networks that underlie specific behaviors ("neural fingerprints").

METHODS

Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine use disorder, and again at the end of 12 weeks of treatment. CPM with leave-one-out cross-validation was conducted to identify pretreatment networks that predicted abstinence (percent cocaine-negative urine samples during treatment). Networks were applied to posttreatment functional MRI data to assess changes over time and ability to predict abstinence during follow-up. The predictive ability of identified networks was then tested in a separate, heterogeneous sample of individuals who underwent scanning before treatment for cocaine use disorder (N=45).

RESULTS

CPM predicted abstinence during treatment, as indicated by a significant correspondence between predicted and actual abstinence values (r=0.42, df=52). Identified networks included connections within and between canonical networks implicated in cognitive/executive control (frontoparietal, medial frontal) and in reward responsiveness (subcortical, salience, motor/sensory). Connectivity strength did not change with treatment, and strength at posttreatment assessment also significantly predicted abstinence during follow-up (r=0.34, df=39). Network strength in the independent sample predicted treatment response with 64% accuracy by itself and 71% accuracy when combined with baseline cocaine use.

CONCLUSIONS

These data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder, and they identify specific abstinence networks that may be targeted in novel interventions.

Disarming Ex-Combatants' Minds: Toward Situated Reintegration Process in Post-conflict Colombia

Collective violence in the context of armed conflict impacts the economy, health systems, and social stability of affected countries. This is considered a complex phenomenon with interwoven biological, psychological, social, cultural, and political factors. However, most of the research on this topic still lacks suitable established integrative approaches to assess multilevel perspectives. Social, cognitive and affective mental processes (SCAMP) are critical factors that should be considered in multilevel approaches. In this article, we critically discuss some of the classically isolated approaches used in violence research, the absence of successful interventions for ex-combatants reintegration, and the specific neglect of SCAMP in these interventions. We present the case of post-conflict Colombia as a unique opportunity to study the different roots of collective violence, and we call for a more robust and situated approach to understanding of and intervention in this multifaceted phenomenon. In addition, we suggest a two-stage approach for addressing ex-combatants’ reintegration programs, which considers the situated nature of post-conflict scenarios and the urgent need for evidence-based interventions. This approach focuses on the comprehensive scientific assessment of specific factors involved in violence exposure and the subsequent design of successful interventions. The implementation of this approach will contribute to the effective reintegration of individuals who have been exposed to extreme violence for more than 50 years.