Developing a clinical translational neuroscience taxonomy for anxiety and mood disorder: protocol for the baseline-follow up Research domain criteria Anxiety and Depression ("RAD") project

Towards a consensus roadmap for a new diagnostic framework for mental disorders

Current nosology claims to separate mental disorders into distinct categories that do not overlap with each other. This nosological separation is not based on underlying pathophysiology but on convention-based clustering of qualitative symptoms of disorders which are typically measured subjectively. Yet, clinical heterogeneity and diagnostic overlap in disease symptoms and dimensions within and across different diagnostic categories of mental disorders is huge. While diagnostic categories provide the basis for general clinical management, they do not describe the underlying neurobiology that gives rise to individual symptomatic presentations. The ability to incorporate neurobiology into the diagnostic framework and to stratify patients accordingly will be a critical step forward for the development of new treatments for mental disorders. Furthermore, it will also allow physicians to provide patients with a better understanding of their illness's complexities and management. To realize this ambition, a paradigm shift is needed to build an understanding of how neuropsychiatric conditions can be defined more precisely using quantitative (multimodal) biological processes and markers and thus to significantly improve treatment success. The ECNP New Frontiers Meeting 2024 set out to develop a consensus roadmap for building a new diagnostic framework for mental disorders by discussing its rationale, outlook, and consequences with all stakeholders involved. This framework would instantiate a set of principles and procedures by which research could continuously improve precision diagnostics while moving away from traditional nosology. In this meeting report, the speakers' summaries from their presentations are combined to address three key elements for generating such a roadmap, namely, the application of innovative technologies, understanding the biology of mental illness, and translating biological understanding into new approaches. In general, the meeting indicated a crucial need for a biology-informed framework to establish more precise diagnosis and treatment for mental disorders to facilitate bringing the right treatment to the right patient at the right time.

Adaptive cognitive control circuit changes associated with problem-solving ability and depression symptom outcomes over 24 months

Mechanistically targeted behavioral interventions are a much-needed strategy for improving outcomes in depression, especially for vulnerable populations with comorbidities such as obesity. Such interventions may change behavior and outcome by changing underlying neural circuit function. However, it is unknown how these circuit-level modifications unfold over intervention and how individual differences in early circuit-level modifications may explain the heterogeneity of treatment effects. We addressed this need within a clinical trial of problem-solving therapy for participants with depression symptoms and comorbid obesity, focusing on the cognitive control circuit as a putative neural mechanism of action. Functional magnetic resonance imaging was applied to measure the cognitive control circuit activity at five time points over 24 months. Compared with participants who received usual care, those receiving problem-solving therapy showed that attenuations in cognitive control circuit activity were associated with enhanced problem-solving ability, which suggests that this circuit plays a key role in the mechanisms of problem-solving therapy. Attenuations in circuit activity were also associated with improved depression symptoms. Changes in cognitive control circuit activity at 2 months better predicted changes in problem-solving ability and depression symptoms at 6, 12, and 24 months, with predictive improvements ranging from 17.8 to 104.0%, exceeding baseline demographic and symptom characteristics. Our findings suggest that targeting the circuit mechanism of action could enhance the prediction of treatment outcomes, warranting future model refinement and improvement to pave the way for its clinical application.

Transdiagnostic Mood, Anxiety, and Trauma Symptom Factors in Alcohol Use Disorder: Neural Correlates Across 3 Brain Networks

BACKGROUND

Alcohol use disorder (AUD) is associated with high rates of trauma, mood, and anxiety disorders. Across these diagnoses, individual symptoms substantially overlap, highlighting the need for a transdiagnostic approach. Furthermore, there is limited research on how transdiagnostic psychopathology impacts the neural correlates of AUD. Thus, we aimed to identify symptom factors spanning diagnoses and examine how they relate to the neurocircuitry of addiction.

METHODS

Eighty-six veterans with AUD completed self-report measures and reward, incentive salience, and cognitive control functional magnetic resonance imaging tasks. Factor analysis was performed on self-reported trauma, depression, anxiety, and stress symptoms to obtain transdiagnostic symptom compositions. Neural correlates of a priori-defined regions of interest in the 3 networks were assessed. Independent sample t tests were used to compare the same nodes by DSM-5 diagnosis.

RESULTS

Four symptom factors were identified: Trauma distress, Negative affect, Hyperarousal, and Somatic anxiety. Trauma distress score was associated with increased cognitive control activity during response inhibition (dorsal anterior cingulate cortex). Negative affect was related to lower activation in reward regions (right caudate) but higher activation in cognitive control regions during response inhibition (left dorsolateral prefrontal cortex). Hyperarousal was related to lower reward activity during monetary reward anticipation (left caudate, right caudate). Somatic anxiety was not significantly associated with brain activation. No difference in neural activity was found by posttraumatic stress disorder, major depressive disorder, or generalized anxiety disorder diagnosis.

CONCLUSIONS

These hypothesis-generating findings offer transdiagnostic symptom factors that are differentially associated with neural function and could guide us toward a brain-based classification of psychiatric dysfunction in AUD. Results warrant further investigation of transdiagnostic approaches in addiction.

Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.

Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice

Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.

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

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

Neural correlates of repetitive negative thinking: Dimensional evidence across the psychopathological continuum

Repetitive negative thinking (RNT) captures an important transdiagnostic factor that predisposes to a maladaptive stress response and contributes to diverse psychiatric disorders. Although RNT can best be seen as a continuous symptom dimension that cuts across boundaries from health to various psychiatric disorders, the neural mechanisms underlying RNT have almost exclusively been studied in health and stress-related disorders, such as depression and anxiety disorders. We set out to study RNT from a large-scale brain network perspective in a diverse population consisting of healthy subjects and patients with a broader range of psychiatric disorders. We studied 46 healthy subjects along with 153 patients with a stress-related and/or neurodevelopmental disorder. We focused on three networks, that are associated with RNT and diverse psychiatric disorders: the salience network, default mode network (DMN) and frontoparietal network (FPN). We investigated the relationship of RNT with both network connectivity strength at rest and with the stress-induced changes in connectivity. Across our whole sample, the level of RNT was positively associated with the connectivity strength of the left FPN at rest, but negatively associated with stress-induced changes in DMN connectivity. These findings may reflect an upregulation of the FPN in an attempt to divert attention away from RNT, while the DMN result may reflect a less flexible adaptation to stress, related to RNT. Additionally, we discuss how our findings fit into the non-invasive neurostimulation literature. Taken together, our results provide initial insight in the neural mechanisms of RNT across the spectrum from health to diverse psychiatric disorders.

State and Trait Anxiety Related Gamma Oscillations in Patients With Anxiety Within the Research Domain Criteria Framework

OBJECTIVE

Diagnosis of anxiety has relied primarily on self-report. This study aimed to investigate the neural correlates of anxiety with quantitative electroencephalography (qEEG) focusing on the state and trait anxiety defined according to the Research Domain Criteria framework existing across the differential diagnosis, rather than focusing on the diagnosis.

METHODS

A total of 41 participants who visited a psychiatric clinic underwent resting state EEG and completed the State-Trait Anxiety Inventory. The absolute power of six frequency bands were analyzed: delta (1-4 Hz), theta (4-8 Hz), alpha (8-10 Hz), fast alpha (10-13.5 Hz), beta (13.5-30 Hz), and gamma (30-80 Hz).

RESULTS

State anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r=-0.484) and central (Cz, r=-0.523) regions, while trait anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r= -0.523), central (Cz, r=-0.568), parietal (P7, r=-0.500; P8, r=-0.541), and occipital (O1, r=-0.510; O2, r=-0.480) regions.

CONCLUSION

The present study identified the significantly negative correlations between the anxiety level and gamma band power in fronto-central and posterior regions assessed at resting status. Further studies to confirm our findings and identify the neural correlates of anxiety are needed.

Using what we know about threat reactivity models to understand mental health during the COVID-19 pandemic

The COVID-19 pandemic has been accompanied by unprecedented levels of stress and threats in a variety of domains (e.g., health, livelihood). Individual differences in threat reactivity may explain why some individuals are at elevated risk for the development or maintenance of psychopathology during the COVID-19 pandemic. This article describes several prominent models, mechanisms, and components of threat reactivity (e.g., appraisals, intolerance of uncertainty, avoidance) and discusses how they might help improve understanding of changes in psychopathology during and following the COVID-19 pandemic.

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

Greater baseline connectivity of the salience and negative affect circuits are associated with natural improvements in anxiety over time in untreated participants

BACKGROUND

There is limited research examining the natural trajectories of depression and anxiety, how these trajectories relate to baseline neural circuit function, and how symptom trajectory-circuit relationships are impacted by engagement in lifestyle activities including exercise, hobbies, and social interactions. To address these gaps, we assessed these relations over three months in untreated participants.

METHODS

262 adults (59.5% female, mean age 35) with symptoms of anxiety and depression, untreated with pharmacotherapy or behavioral therapy, completed the DASS-42, WHOQOL, and custom surveys at baseline and follow-up to assess symptoms, psychosocial function, and lifestyle activity engagement. At baseline, participants underwent fMRI under task-free and task-evoked conditions. We quantified six circuits implicated in these symptoms: default mode, salience, negative and positive affect, attention, and cognitive control.

RESULTS

From baseline to 3 months, some participants demonstrated a natural improvement in anxiety (24%) and depression (26%) symptoms. Greater baseline salience circuit connectivity (pFDR=0.045), specifically between the left and right insula (pFDR=0.045), and greater negative affect circuit connectivity elicited by sad faces (pFDR=0.030) were associated with anxiety symptom improvement. While engagement in lifestyle activities were not associated with anxiety improvements, engagement in hobbies moderated the association between negative affect circuit connectivity and anxiety symptom improvement (p = 0.048).

LIMITATIONS

The observational design limits causal inference.

CONCLUSIONS

Our findings highlight the role of the salience and negative affect circuits as potential circuit markers of natural anxiety symptom improvements over time. Future studies that identify biomarkers associated with symptom improvements are critical for the development of personalized treatment targets.

Coping Strategies, Neural Structure, and Depression and Anxiety During the COVID-19 Pandemic: A Longitudinal Study in a Naturalistic Sample Spanning Clinical Diagnoses and Subclinical Symptoms

BACKGROUND

Although the COVID-19 pandemic has been shown to worsen anxiety and depression symptoms, we do not understand which behavioral and neural factors may mitigate this impact. To address this gap, we assessed whether adaptive and maladaptive coping strategies affect symptom trajectory during the pandemic. We also examined whether pre-pandemic integrity of brain regions implicated in depression and anxiety affect pandemic symptoms.

METHODS

In a naturalistic sample of 169 adults (66.9% female; age 19-74 years) spanning psychiatric diagnoses and subclinical symptoms, we assessed anhedonia, tension, and anxious arousal symptoms using validated components (21-item Depression, Anxiety, and Stress Scale), coping strategies (Brief-Coping Orientation to Problems Experienced), and gray matter volume (amygdala) and cortical thickness (hippocampus, insula, anterior cingulate cortex) from magnetic resonance imaging T1-weighted scans. We conducted general linear mixed-effects models to test preregistered hypotheses that 1) maladaptive coping pre-pandemic and 2) lower structural integrity pre-pandemic would predict more severe pandemic symptoms; and 3) coping would interact with neural structure to predict pandemic symptoms.

RESULTS

Greater use of maladaptive coping strategies was associated with more severe anxious arousal symptoms during the pandemic (p = .011, false discovery rate-corrected p [p FDR] = .035), specifically less self-distraction (p = .014, p FDR = .042) and greater self-blame (p = .002, p FDR = .012). Reduced insula thickness pre-pandemic predicted more severe anxious arousal symptoms (p = .001, p FDR = .027). Self-distraction interacted with amygdala volume to predict anhedonia symptoms (p = .005, p FDR = .020).

CONCLUSIONS

Maladaptive coping strategies and structural variation in brain regions may influence clinical symptoms during a prolonged stressful event (e.g., COVID-19 pandemic). Future studies that identify behavioral and neural factors implicated in responses to global health crises are warranted for fostering resilience.

Evidence-Based Pharmacotherapy of Generalised Anxiety Disorder: Focus on Agomelatine

Recent network meta-analyses support the use of pharmacotherapy in patients with generalised anxiety disorder (GAD). Compared with placebo, drug treatment can improve symptoms and quality of life, and is more effective in preventing relapse. Selective serotonin reuptake inhibitors and serotonin–norepinephrine reuptake inhibitors are generally considered the first-line agents of choice in GAD, but in some patients, an alternative evidence-based treatment with a different mechanism of action may also be considered (e.g. those with severe GAD, inadequate response, adverse effects and/or contraindications). One example is agomelatine, a melatonin receptor agonist and serotonin 2C (5-HT_2C) receptor antagonist, which has been shown to have efficacy that is greater than placebo in patients with GAD, and to have a tolerability profile that compares favourably with that of escitalopram. Both agomelatine and escitalopram are efficacious in treating patients with GAD, including those with severe symptoms.

Identifying response and predictive biomarkers for Transcranial magnetic stimulation outcomes: protocol and rationale for a mechanistic study of functional neuroimaging and behavioral biomarkers in veterans with Pharmacoresistant depression

BACKGROUND

Although repetitive transcranial magnetic stimulation ('TMS') is becoming a gold standard treatment for pharmacoresistant depression, we lack neural target biomarkers for identifying who is most likely to respond to TMS and why. To address this gap in knowledge we evaluate neural targets defined by activation and functional connectivity of the dorsolateral prefrontal cortex-anchored cognitive control circuit, regions of the default mode network and attention circuit, and interactions with the subgenual anterior cingulate. We evaluate whether these targets and interactions between them change in a dose-dependent manner, whether changes in these neural targets correspond to changes in cognitive behavioral performance, and whether baseline and early change in neural target and cognitive behavioral performance predict subsequent symptom severity, suicidality, and quality of life outcomes. This study is designed as a pragmatic, mechanistic trial partnering with the National Clinical TMS Program of the Veteran's Health Administration.

METHODS

Target enrollment consists of 100 veterans with pharmacoresistant Major Depressive Disorder (MDD). All veterans will receive a clinical course of TMS and will be assessed at 'baseline' pre-TMS commencement, 'first week' after initiation of TMS (targeting five sessions) and 'post-treatment' at the completion of TMS (targeting 30 sessions). Veterans will be assessed using functional magnetic resonance imaging (fMRI), a cognitive behavioral performance battery, and established questionnaires. Multivariate linear mixed models will be used to assess whether neural targets change with TMS as a function of dose (Aim 1), whether extent and change of neural target relates to and predicts extent of behavioral performance (Aim 3), and whether extent of neural target change predicts improvement in symptom severity, suicidality, and quality of life (Aim 3). For all three aims, we will also assess the contribution of baseline moderators such as biological sex and age.

DISCUSSION

To our knowledge, our study will be the first pragmatic, mechanistic observational trial to use fMRI imaging and cognitive-behavioral performance as biomarkers of TMS treatment response in pharmacoresistant MDD. The results of this trial will allow providers to select suitable candidates for TMS treatment and better predict treatment response by assessing circuit connectivity and cognitive-behavioral performance at baseline and during early treatment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04663481 , December 5th, 2020, retrospectively registered. The first veteran was enrolled October 30th, 2020.

Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.

Correlation between executive function and quantitative EEG in patients with anxiety by the Research Domain Criteria (RDoC) framework

The Research Domain Criteria (RDoC) project was proposed by the National Institute of Mental Health in 2010 to create a new diagnostic system including symptoms and data from genetics, neuroscience, physiology, and self-reports. The purpose of this study was to determine the link between anxiety and executive functions through quantitative electroencephalography (qEEG) based on the RDoC system. Nineteen-channel EEGs were recorded at the psychiatric clinic from 41 patients with symptoms of anxiety. The EEG power spectra were analysed. The Executive Intelligence Test (EXIT) including the K-WAIS-IV, Stroop, controlled oral word association, and the design fluency tests were performed. A partial, inversed, and significant association was observed between executive intelligence quotient (EIQ) and the absolute delta power in the central region. Similarly, a partial, inversed, and significant association was observed between design fluency and the absolute delta power in the left parietal area. Our findings suggest that the increase in delta power in the central region and left P3 was negatively correlated with the decrease in executive function. It is expected that the absolute delta power plays a specific role in the task-negative default mode network in the relationship between anxiety and executive function.

Association of anxiety phenotypes with risk of depression and suicidal ideation in community youth

BACKGROUND

Anxiety symptoms are common in adolescence and are often considered developmentally benign. Yet for some, anxiety presents with serious comorbid nonanxiety psychopathology. Early identification of such "malignant" anxiety presentations is a major challenge. We aimed to characterize anxiety symptoms suggestive of risk for depression and suicidal ideation (SI) in community youths.

METHODS

Cross-sectional associations were evaluated in community youths (n = 7,054, mean age: 15.8) who were assessed for anxiety, depression, and SI. We employed factor and latent class analyses to identify anxiety clusters and subtypes. Longitudinal risk of anxiety was evaluated in a subset of 330 youths with longitudinal data on depression and SI (with baseline mean age of 12.3 years and follow-up mean age of 16.98 years).

OUTCOMES

Almost all (92%) adolescents reported anxiety symptoms. Data-driven approaches revealed anxiety factors and subtypes that were differentially associated with depression and SI. Cross-sectional analyses revealed that panic and generalized anxiety symptoms show the most robust associations with depression and SI. Longitudinal, multivariate analyses revealed that panic symptoms during early adolescence, not generalized anxiety symptoms, predict depression and SI for later adolescent years, particularly in males.

INTERPRETATION

Anxiety is common in youths, with certain symptom clusters/subtypes predicting risk for depression and SI. Panic symptoms in early adolescence, even below disorder threshold, predict high risk for late adolescent depression and SI.

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression

Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

The Heterogeneity of Mental Health Assessment

Across the landscape of mental health research and diagnosis, there is a diverse range of questionnaires and interviews available for use by clinicians and researchers to determine patient treatment plans or investigate internal and external etiologies. Although individually, these tools have each been assessed for their validity and reliability, there is little research examining the consistency between them in terms of what symptoms they assess, and how they assess those symptoms. Here, we provide an analysis of 126 different questionnaires and interviews commonly used to diagnose and screen for 10 different disorder types including depression, anxiety, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), addiction, bipolar disorder, eating disorder, and schizophrenia, as well as comparator questionnaires and interviews that offer an all-in-one cross-disorder assessment of mental health. We demonstrate substantial inconsistency in the inclusion and emphasis of symptoms assessed within disorders as well as considerable symptom overlap across disorder-specific tools. Within the same disorder, similarity scores across assessment tools ranged from 29% for assessment of bipolar disorder to a maximum of 58% for OCD. Furthermore, when looking across disorders, 60% of symptoms were assessed in at least half of all disorders illustrating the extensive overlap in symptom profiles between disorder-specific assessment tools. Biases in assessment toward emotional, cognitive, physical or behavioral symptoms were also observed, further adding to the heterogeneity across assessments. Analysis of other characteristics such as the time period over which symptoms were assessed, as well as whether there was a focus toward frequency, severity or duration of symptoms also varied substantially across assessment tools. The consequence of this inconsistent and heterogeneous assessment landscape is that it hinders clinical diagnosis and treatment and frustrates understanding of the social, environmental, and biological factors that contribute to mental health symptoms and disorders. Altogether, it underscores the need for standardized assessment tools that are more disorder agnostic and span the full spectrum of mental health symptoms to aid the understanding of underlying etiologies and the discovery of new treatments for psychiatric dysfunction.

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.

[Anxiety in the elderly]

Anxiety occurs in about one third of people over 65 years of age. However, its identification in this age has significant difficulties. The clinical manifestations, pathogenetic mechanisms, approaches to the diagnosis and treatment of various types of anxiety are described in the article. Particular attention is paid to the comorbidity of anxiety disorders in elderly patients. A comprehensive approach to the treatment of elderly patients with anxiety includes psychotherapeutic and pharmacotherapeutic approaches. Special attention should be paid to the efficacy and safety of the drugs, which is especially important in this category of patients.

Anxiety and Brain Networks of Attentional Control

Advances in the study of brain networks can be applied to our understanding of anxiety disorders (eg, generalized anxiety, obsessive-compulsive, and posttraumatic stress disorders) to enable us to create targeted treatments. These disorders have in common an inability to control thoughts, emotions, and behaviors related to a perceived threat. Here we review animal and human imaging studies that have revealed separate brain networks related to various negative emotions. Research has supported the idea that brain networks of attention serve to control emotion networks as well as the thoughts and behaviors related to them. We discuss how attention networks can modulate both positive and negative affect. Disorders arise from both abnormal activation of negative affect and a lack of attentional control. Training attention has been one way to foster improved attentional control. We review attention training studies as well as efforts to generally improve attention networks through stimulation in self-regulation.

Multi-unit relations among neural, self-report, and behavioral correlates of emotion regulation in comorbid depression and obesity

Depression is a leading cause of disability and is commonly comorbid with obesity. Emotion regulation is impaired in both depression and obesity. In this study, we aimed to explicate multi-unit relations among brain connectivity, behavior, and self-reported trait measures related to emotion regulation in a comorbid depressed and obese sample (N = 77). Brain connectivity was quantified as fractional anisotropy (FA) of the uncinate fasciculi, a white matter tract implicated in emotion regulation and in depression. Use of emotion regulation strategies was assessed using the Emotion Regulation Questionnaire (ERQ). We additionally measured reaction times to identifying negative emotions, a behavioral index of depression-related emotion processing biases. We found that greater right uncinate fasciculus FA was related to greater usage of suppression (r = 0.27, p = 0.022), and to faster reaction times to identifying negative emotions, particularly sadness (r = −0.30, p = 0.010) and fear (r = −0.35, p = 0.003). These findings suggest that FA of the right uncinate fasciculus corresponds to maladaptive emotion regulation strategies and emotion processing biases that are relevant to co-occurring depression and obesity. Interventions that consider these multi-unit associations may prove to be useful for subtyping and improving clinical outcomes for comorbid depression and obesity.

Neurobiology of the dorsolateral prefrontal cortex in GAD: Aberrant neurometabolic correlation to hippocampus and relationship to anxiety sensitivity and IQ

INTRODUCTION

The neurometabolism underlying the cognitive and affective symptoms associated with generalized anxiety disorder (GAD) remain poorly understood. After we have linked worry to intelligence in patients with GAD, we hypothesized that aberrant neurometabolic correlations between hippocampus and neocortical regions may underlie a shared substrate in GAD patients for both anxiety sensitivity and intelligence.

METHODS

GAD patients (n = 16; F = 11) and healthy volunteers (n = 16; F = 10) were assessed using ¹H-MRSI. Co-axial planes I [hippocampus (HIPP)] and co-axial plane III [dorsolateral prefrontal cortex (DLPFC), central gyrus (CG)] were examined. Using general linear models, we examined resting metabolite concentrations using HIPP as a hub to CG and DLPFC. Neocortical ROIs were related to Anxiety Sensitivity Index (ASI) and Full Scale IQ (FSIQ) in GAD patients versus controls.

RESULTS

Right hippocampal Cho/Cr directly predicted left DLPFC Cho/Cr in GAD (r = 0.75), an effect distinguishable (p = 0.0004) from controls. Left HIPP Cho/Cr positively predicted left CG Cho/Cr in GAD, an effect distinguishable from controls. In patients, both left and right DLPFC Cho/Cr positively predicted ASI but only left DLPFC Cho/Cr inversely predicted IQ. By contrast, IQ in controls correlated directly with left CG Cho/Cr.

LIMITATIONS

Small sample size precluded us from investigating how gender and FSIQ subscales related to neurochemical correlations in the ROIs examined.

CONCLUSIONS

Aberrant resting state neurochemical correlation between left DLPFC and right HIPP may contribute to GAD symptomatology. Unlike controls, in GAD, IQ and worry may share a common yet inverse neurometabolic substrate in left DLPFC.

[Research domain criteria (RDoC) : Psychiatric research as applied cognitive neuroscience]

BACKGROUND

Just before the official launch of the DSM-5 in 2013, the Research Domain Criteria (RDoC) initiative of the National Institute of Mental Health was made public and is becoming increasingly more important in psychiatric research.

OBJECTIVE

The aim of this paper is to clarify the conceptual approach of RDoC, to systematically discuss limitations, to present exemplary RDoC-based studies and to consider the relevance of the RDoC concepts for clinicians and scientists.

MATERIAL AND METHODS

The is a qualitative introduction and review article with a critical discussion.

RESULTS AND DISCUSSION

The RDoC initiative was not conceived as an alternative diagnostic manual to DSM-5 or IDC-10/11 for use in clinical practice. It is a new systematic framework for psychiatric research based on the most recent results of cognitive neuroscience and aims to map mental disorders dimensionally and transdiagnostically. Despite some weaknesses, it is currently the most elaborated and scientifically grounded approach for multidisciplinary research on mental disorders. In contrast to the purely symptom-based DSM and ICD approaches, which are agnostic with respect to the pathogenesis of mental diseases, the explicit aim of the RDoC initiative is to systematize biological knowledge about risk factors and causes of mental disorders; therefore, it has a much greater potential to develop new and individualized therapeutic strategies based on disease mechanisms.

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders

IMPORTANCE

The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices.

OBJECTIVE

To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017.

MAIN OUTCOMES AND MEASURES

We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference.

RESULTS

Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171).

CONCLUSIONS AND RELEVANCE

These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

Utilizing a transdiagnostic neuroscience-informed approach to differentiate the components of a complex clinical presentation: A case report

Background

Recent research recognizes considerable overlap in the clinical presentation of psychiatric disorders such as Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder and Social Anxiety Disorder. The diagnostic approach collects symptoms to reflect a single underlying psychopathological process. The Research Domain Criteria (RDoC) emphasizes psychopathology as arising from combinations of abnormalities in core underlying constructs that can be measured at many levels of analysis, from biological to behavioral. Patients who present with clinical heterogeneity may benefit from transdiagnostic case conceptualization that integrates detailed symptom information across multiple measurements spanning multiple domains of functioning based in the RDoC framework.

Case presentation

We report on one case that was included in a research study focused on advancing knowledge towards a transdiagnostic, brain-based model of anxiety and depression. The 20-year-old male patient presented at a community mental health clinic for inattention, low mood, sleep problems and anxious symptoms. The patient also presented with primary problems in negative valence systems (anxiety, avoidance, and bias towards negative information), cognitive systems (fluctuating cognitive ability over time, poor concentration and ability to focus), and social processing systems (deficits in social communication skills). Conceptualizing this case through a transdiagnostic lens augmented the patient's treatment plan by including a more integrative approach. Treatment included social skills training, progressive relaxation exercises, and basic psychoeducation in emotional expression and independent living skills.

Conclusion

This case illustrates the utility of a transdiagnostic approach, particularly when a traditional diagnostic model generates conflicting evidence and/or multiple comorbidities. RDoC provides a framework for integrating abnormalities across multiple dimensions. Furthermore, it lays the foundation for future integration of brain-behavior relationships into case conceptualization and personalized treatment approaches.

The new field of 'precision psychiatry'

BACKGROUND

Precision medicine is a new and important topic in psychiatry. Psychiatry has not yet benefited from the advanced diagnostic and therapeutic technologies that form an integral part of other clinical specialties. Thus, the vision of precision medicine as applied to psychiatry - 'precision psychiatry' - promises to be even more transformative than in other fields of medicine, which have already lessened the translational gap.

DISCUSSION

Herein, we describe 'precision psychiatry' and how its several implications promise to transform the psychiatric landscape. We pay particular attention to biomarkers and to how the development of new technologies now makes their discovery possible and timely. The adoption of the term 'precision psychiatry' will help propel the field, since the current term 'precision medicine', as applied to psychiatry, is impractical and does not appropriately distinguish the field. Naming the field 'precision psychiatry' will help establish a stronger, unique identity to what promises to be the most important area in psychiatry in years to come.

CONCLUSION

In summary, we provide a wide-angle lens overview of what this new field is, suggest how to propel the field forward, and provide a vision of the near future, with 'precision psychiatry' representing a paradigm shift that promises to change the landscape of how psychiatry is currently conceived.