A theoretical and empirical modeling of anxiety integrated with RDoC and temporal dynamics

Algorithm-Based Modular Psychotherapy Alleviates Brain Inflammation in Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is marked by prolonged and excessive worry, physical signs of anxiety, and associated neuroinflammation. Traditional treatments, like pharmacotherapy and cognitive-behavioral therapy (CBT), often leave residual symptoms and have high relapse rates. This study aimed to explore the efficacy of algorithm-based modular psychotherapy (MoBa), a combination of CBT and mindfulness meditation as validated by the research domain criteria (RDoC), in reducing anxiety and neuroinflammation in GAD. A longitudinal design was used, with 50 patients with GAD undergoing a 12-week MoBa treatment. The patients were investigated pre- and post-treatment using MRI to measure neuroinflammatory markers (DBSI-RF, diffusion-basis spectral imaging-based restricted fraction) in the hippocampus, amygdala, and neocortex. Clinical symptoms were assessed using the Hamilton Anxiety Rating Scale (HAM-A) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Results indicated significant reductions in both anxiety symptoms and MRI RF values in the amygdala, suggesting decreased neuroinflammation. A reduction in anxiety was associated with the amelioration of neuroinflammation in the amygdala. These results suggest that MoBa is effective in alleviating both the psychological and neuroinflammatory aspects of GAD, offering a promising personalized treatment approach. Future research should focus on long-term effects and the mechanisms through which MoBa impacts neuroinflammation and anxiety.

Leveraging deep learning models to understand the daily experience of anxiety in teenagers over the course of a year

INTRODUCTION

Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessment is through point-in-time measures like the GAD-7. Unfortunately, this survey and others like it can be subject to recall bias and do not fully capture the variability in an individual's day-to-day symptom experience. The current work aims to evaluate how point-in-time assessments like the GAD-7 relate to daily measurements of anxiety in a teenage population.

METHODS

To evaluate this relationship, we leveraged data collected at four separate three week intervals from 30 teenagers (age 15-17) over the course of a year. The specific items of interest were a single item anxiety severity measure collected three times per day and end-of-month GAD-7 assessments. Within this sample, 40 % of individuals reported clinical levels of generalized anxiety disorder symptoms at some point during the study. The first component of analysis was a visual inspection assessing how daily anxiety severity fluctuated around end-of-month reporting via the GAD-7. The second component was a between-subjects comparison assessing whether individuals with similar GAD-7 scores experienced similar symptom dynamics across the month as represented by latent features derived from a deep learning model. With this approach, similarity was operationalized by hierarchical clustering of the latent features.

RESULTS

The aim clearly indicated that an individual's daily experience of anxiety varied widely around what was captured by the GAD-7. Additionally, when hierarchical clustering was applied to the three latent features derived from the (LSTM) encoder (r = 0.624 for feature reconstruction), it was clear that individuals with similar GAD-7 outcomes were experiencing different symptom dynamics. Upon further inspection of the latent features, the LSTM model appeared to rely as much on anxiety variability over the course of the month as it did on anxiety severity (p < 0.05 for both mean and RMSSD) to represent an individual's experience.

DISCUSSION

This work serves as further evidence for the heterogeneity within the experience of anxiety and that more than just point-in-time assessments are necessary to fully capture an individual's experience.

Examining the temporal dynamics of anxiety and depressive symptoms during a therapist-supported, smartphone-based intervention for depression: Longitudinal observational study

OBJECTIVE

This study examined the temporal dynamics of anxiety and depressive symptoms during a 12-week therapist-supported, smartphone-delivered digital health intervention for symptoms of depression and anxiety.

METHODS

A total of 290 participants were included in the present analyses (age Mean = 39.64, SD = 10.25 years; 79% female; 54% self-reported psychotropic medication use). Linear mixed models were used to examine the concurrent anxiety-depression association and (2) the lead-lag anxiety-depression relationship, with greater anxiety predicted to precede an increase in depression.

RESULTS

In support of Hypothesis 1, greater anxiety during the current biweekly assessment was associated with greater depressive symptoms during the current biweekly assessment. In support of Hypothesis 2, greater anxiety during the prior biweekly assessment was associated with greater depressive symptoms during the current biweekly assessment but not vice-versa.

CONCLUSION

These findings demonstrate that anxiety and depressive symptoms may overlap and fluctuate in concert, with anxiety symptoms predicting subsequent depressive symptoms but not vice-versa. With sensitivity to study limitations, implications for future intervention designs are discussed.

Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments

Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assessed using these methodologies. In this research, we utilize smartphone sensors and personalized deep learning models to predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms. Participants (N = 32) with generalized anxiety disorder and/or social anxiety disorder (based on self-report) installed a smartphone application and completed ecological momentary assessment symptoms assessing their anxiety and avoidance symptoms hourly for the course of one week (T = 2007 assessments). During the same period, the smartphone app collected information about physiological activation (heart rate and heart rate variability), exposure to light, social contact, and GPS location. GPS locations were coded to reveal the type of location and the weather information. Personalized deep learning models using the smartphone sensor data were capable of predicting the majority of total variation in anxiety symptoms (R² = 0.748) and predicting a large proportion of within-person variation at the hour-by-hour level (mean R² = 0.385). These results suggest that personalized deep learning models using smartphone sensor data are capable of accurately predicting future anxiety disorder symptom changes.

EEG correlates of attentional control in anxiety disorders: A systematic review of error-related negativity and correct-response negativity findings

BACKGROUND

Anxiety disorders are highly prevalent and cause substantial personal, social and economic burden. Altered attentional control has been shown to be present across anxiety disorders and is associated with specific changes in brain activity which can be recorded by electroencephalogram (EEG). These include changes in the EEG markers of error-related negativity (ERN) and correct-response negativity (CRN), both believed to reflect response monitoring and attentional control pathophysiology in anxiety. The aim of this review was to systematically assess the research on ERN and CRN in attentional control in individuals with clinical anxiety and healthy controls, across emotional and non-emotional attentional control.

METHODS

A comprehensive literature search was conducted for studies published prior to October 22^nd, 2020. Details of the protocol for this systematic review were registered on PROSPERO (CRD42019144885).

RESULTS

66 studies had their data extracted. All 66 studies measured ERN, with 85% finding significantly increased ERN amplitudes associated with clinical anxiety. Only 44 of the extracted studies analysed CRN and only ~20% of these found significant changes in CRN amplitude associated with individuals with clinical anxiety.

LIMITATIONS

There were several anxiety disorders that had either limited literature (i.e. specific phobia, separation anxiety disorder or agoraphobia) or nil literature (i.e. selective mutism) available. No extracted studies included samples of older adults (i.e. aged 60+ years), and only six extracted studies included measures of emotional attentional control.

CONCLUSIONS

Findings indicate the promising utility of ERN of attentional control as a robust, transdiagnostic trait marker of clinical anxiety.

Digital health data-driven approaches to understand human behavior

Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals' behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.

Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

BACKGROUND

Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier.

OBJECTIVE

This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset.

METHODS

In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity.

RESULTS

The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect.

CONCLUSIONS

These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

Phenotype Network and Brain Structural Covariance Network of Anxiety

Network-based approach for psychological phenotypes assumes the dynamical interactions among the psychiatric symptoms, psychological characteristics, and neurocognitive performances arise, as they coexist, propagate, and inhibit other components within the network of mental phenomena. For differential types of dataset from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intraindividual covariance network could be used. Accordingly, these network-based approaches for anxiety-related psychological phenomena have been helpful in quantitative and pictorial understanding of qualitative dynamics among the diverse psychological phenomena as well as mind-environment interactions. Brain structural covariance refers to the correlative patterns of diverse brain morphological features among differential brain regions comprising the brain, as calculated per participant or across the participants. These covarying patterns of brain morphology partly overlap with longitudinal patterns of brain cortical maturation and also with propagating pattern of brain morphological changes such as cortical thinning and brain volume reduction in patients diagnosed with neurologic or psychiatric disorders along the trajectory of disease progression. Previous studies that used the brain structural covariance network could show neural correlates of specific anxiety disorder such as panic disorder and also elucidate the neural underpinning of anxiety symptom severity in diverse psychiatric and neurologic disorder patients.

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data

With the recent growth in intensive longitudinal designs and the corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. The available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple-subject time series are greatly underpowered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are then used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject time-series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach, relative to other existing approaches.

Current evolutionary adaptiveness of anxiety: Extreme phenotypes of anxiety predict increased fertility across multiple generations

OBJECTIVE

Although recent research has begun to examine the impact of elevated anxiety on evolutionary fitness, no prior research has examined anxiety across a continuum. Such research is important as the effect of traits across a continuum on fertility hold important implications for the levels and distribution of the traits in later generations.

METHOD

In a three-generational sample (N = 2657) the linear and quadratic relationship between anxiety and the number of children, grandchildren, and great-grandchildren 15 years later was examined.

RESULTS

The findings suggested that anxiety had a positive quadratic relationship with the number of children, grandchildren, and great-grandchildren 15 years later. These relationships were not significantly moderated by sex. Moreover, most of the variance between anxiety and the number of great-grandchildren was explained by anxiety's influence on the number of children and grandchildren, as opposed to anxiety having an independent direct impact on the number of great-grandchildren.

CONCLUSION

These findings suggest that extreme values from the mean anxiety are associated with increased evolutionary fitness within the modern environment.

Steps Toward an Integrative Clinical Systems Psychology

Clinical fields of the "sciences of the mind" (psychotherapy, psychiatry, etc.) lack integrative conceptual frameworks that have explanatory power. Mainly descriptive-classificatory taxonomies like DSM dominate the field. New taxonomies such as Research Domain Criteria (RDoC) aim to collect scientific knowledge regarding "systems" for "processes" of the brain. These terms have a supradisciplinary" meaning if they are considered in context of Systems Science. This field emerges as a platform of theories like general systems theory, catastrophe theory, synergetics, chaos theory, etc. It provides a lot of abstract concepts, constructs, methods and models. We assume that these tools also enable theoretical integration in the diversified field of clinical practice in the sciences of the mind. Additionally, systems thinking in clinical psychology improves conceptual links to currently network-oriented neurobiology. However, clear definitions of systemic terms are necessary to emerge from their mainly metaphorical use. Here we revise mainly terms like "structure", "process" and "dynamics" as they are used already in psychology, psychoanalysis, psychopathology and psychiatry in an ill-defined way. For instance, affective-cognitive structures like "life space" or "object representations" can be seen as products of mental processing. These structures, in turn, modulate dynamics of mental processes. Additionally, we suggest a coupled network concept of emotions and motivations as the main subsystem that modulates mental dynamics that results in a qualitative systemic model of the mind. Finally, we assume that a revisited systemic approach could improve interdisciplinary understanding of the mental.