Using the research domain criteria framework to track domains of change in comorbid PTSD and SUD

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records

BACKGROUND

Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.

METHODS

In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.

RESULTS

The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.

CONCLUSIONS

The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Investigating the network structure and causal relationships among bridge symptoms of comorbid depression and anxiety: A Bayesian network analysis

BACKGROUND

The network analysis method emphasizes the interaction between individual symptoms to identify shared or bridging symptoms between depression and anxiety to understand comorbidity. However, the network analysis and community detection approach have limitations in identifying causal relationships among symptoms. This study aims to address this gap by applying Bayesian network (BN) analysis to investigate potential causal relationships.

METHOD

Data were collected from a sample of newly enrolled college students. The network structure of depression and anxiety was estimated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) Scale measures, respectively. Shared symptoms between depression and anxiety were identified through network analysis and clique percolation (CP) method. The causal relationships among symptoms were estimated using BN.

RESULTS

The strongest bridge symptoms, as indicated by bridge strength, include sad mood (PHQ2), motor (PHQ8), suicide (PHQ9), restlessness (GAD5), and irritability (GAD6). These bridge symptoms formed a distinct community using the CP algorithm. Sad mood (PHQ2) played an activating role, influencing other symptoms. Meanwhile, restlessness (GAD5) played a mediating role with reciprocal influences on both anxiety and depression symptoms. Motor (PHQ8), suicide (PHQ9), and irritability (GAD6) assumed recipient positions.

CONCLUSION

BN analysis presents a valuable approach for investigating the complex interplay between symptoms in the context of comorbid depression and anxiety. It identifies two activating symptoms (i.e., sadness and worry), which serve to underscore the fundamental differences between these two disorders. Additionally, psychomotor symptoms and suicidal ideations are recognized as recipient roles, being influenced by other symptoms within the network.

Neuroscience-informed classification of prevention interventions in substance use disorders: An RDoC-based approach

Neuroscience has contributed to uncover the mechanisms underpinning substance use disorders (SUD). The next frontier is to leverage these mechanisms as active targets to create more effective interventions for SUD treatment and prevention. Recent large-scale cohort studies from early childhood are generating multiple levels of neuroscience-based information with the potential to inform the development and refinement of future preventive strategies. However, there are still no available well-recognized frameworks to guide the integration of these multi-level datasets into prevention interventions. The Research Domain Criteria (RDoC) provides a neuroscience-based multi-system framework that is well suited to facilitate translation of neurobiological mechanisms into behavioral domains amenable to preventative interventions. We propose a novel RDoC-based framework for prevention science and adapted the framework for the existing preventive interventions. From a systematic review of randomized controlled trials using a person-centered drug/alcohol preventive approach for adolescents, we identified 22 unique preventive interventions. By teasing apart these 22 interventions into the RDoC domains, we proposed distinct neurocognitive trajectories which have been recognized as precursors or risk factors for SUDs, to be targeted, engaged and modified for effective addiction prevention.

Enhancing post-traumatic stress disorder patient assessment: Leveraging Natural Language Processing for Research of Domain Criteria Identification using electronic medical records

Background

Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.

Methods

In our study, we created an NLP workflow to analyze electronic medical record (EMR) data, and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, allmpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.

Results

The sentence transformer model demonstrated superior F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. Women had the highest abnormalities of sensorimotor systems, while veterans had the highest abnormalities of negative and positive valence systems. The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.

Conclusions

The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Progesterone Attenuates the Stress Response in Individuals with Alcohol Dependence and Post-Traumatic Stress Disorder - A Pilot Study

OBJECTIVE

Evidence from laboratory studies suggests that progesterone may be effective in reducing stress and craving, and may improve cognitive performance in smokers and individuals with cocaine dependence. The objective of this study was to examine if progesterone would attenuate stress-induced craving, anxiety, affect and physiological measures, as well as improve stress-induced cognitive performance (processing speed and selective attention) in individuals diagnosed with alcohol use disorder (AUD) and post traumatic stress disorder (PTSD).

METHODS

This laboratory study included (n = 13) participants who were diagnosed with current AUD and PTSD who were randomly assigned to recive either progesterone (200mg bid) or placebo in identical looking capsules for 3 days. On the fourth day they completed a laboratory session. In the morning of the test session, they received the last dose of medication and completed the rest of the laboratory procedures. The procedures included presentation in random order of personalized trauma and neutral scripts with relaxation in between. Main outcomes included measure of craving, anxiety, affect and cognitive performance.

RESULTS

Consistent with other research, trauma scripts produced significantly greater increases in craving, anxiety and negative affect when compared with neutral scripts. Progesterone significantly reduced stress-induced symptoms of craving, anxiety, fear, anger and sadness but had no effect on positive emotions (joy, relaxation). Progesterone was effective in ameliorating stress-induced decreases in cognitive performance.

CONCLUSIONS

The findings from this study demonstrate that progesterone can be effective in reducing stress-induced craving, anxiety and negative affect in a laboratory setting in individuals with comorbid AUD and PTSD. Interestingly, progesterone also improved cognitive performance. These findings require replication in a larger clinical trial and may have implications for treatment among individuals with AUD and PTSD.This study was registered as NCT02187224, at www.clinicaltrials.gov.

Using the Research Domain Criteria as a framework to integrate psychophysiological findings into stress management and psychotherapy interventions

Research on the psychophysiology of stress is expanding rapidly, but the field lacks a clear integrative framework to help translate research findings into empirically supported stress interventions. The Research Domain Criteria (RDoC) is an excellent candidate to explore as a framework to integrate stress research. The RDoC framework is a dimensional, multi-modal approach to psychopathology proposed as an alternative to categorical approaches used by the International Classification of Diseases (ICD) and the Diagnostic and Statistical Manual (DSM). The goal of this paper is to explore the RDoC as a framework to integrate psychophysiology research into therapeutic interventions for stress. The RDoC consists of six domains: negative valence systems, positive valence systems, cognitive systems, social processes systems, arousal/regulatory systems, and sensorimotor systems, and provides an excellent structure for integrating information from multiple levels of functioning including physiology, behavior, and self-report, as well as genes, molecules, cells, and brain circuits. Integrating psychophysiological research on stress using the RDoC framework can direct and amplify stress management and psychotherapeutic interventions. First, the RDoC provides a clear foundation for conceptualizing the stress response in terms of important concepts such as allostasis and adaptation. In this perspective, the terms "allostatic response" or "adaptation response" are more descriptive terms than "stress response" in understanding bodily responses to life threats and challenges. Second, psychophysiological approaches can be used in the context of modalities such as biofeedback and mindfulness to both collect psychophysiological data and then integrate that data into a broader therapeutic framework. Heart rate variability (HRV) biofeedback is being used more frequently as part of a therapeutic intervention package with stress management and psychotherapy, and HRV data is also used to provide outcome evidence on the efficacy of treatment. Mindfulness practices are commonly used in combination with stress management and psychotherapy, and psychophysiological data (HRV, EEG, blood pressure, etc.) is often collected to explore and understand mind/body relationships. In conclusion, the lack of a clear framework to assess and understand mind/body functioning limits current stress research and interventions. The RDoC provides a strong framework to assess and integrate physiological and psychological data and improve stress interventions.

ADHD-Gaming Disorder Comorbidity in Children and Adolescents: A Narrative Review

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurobiological condition characterized by developmentally inadequate levels of inattention, hyperactivity, and impulsivity, and a neurobiological disruption in brain neurotransmitters and circuitry causing abnormal responses to rewards. Playing electronic games generates a biological response that activates the neuronal circuits linked to pleasure and reward, and there is a growing attention to this type of activity, which can also turn into a mental health condition. The existence and the boundaries between the functional and the dysfunctional are still a source of debate, with the recognition of 'Internet Gaming Disorder' (IGD) as a condition belonging to the broader area of addiction requiring more in-depth study with respect to the DSM-5, while 'Gaming Disorder' (GD) was officially recognized as a new diagnosis by the World Health Organization (WHO) in the updated revision of the International Classification of Diseases (ICD-11). Notwithstanding, the suggested criteria for the diagnosis of Gaming Disorder are still debated. Since ADHD has been reported as a risk factor for developing addictions, this narrative review aims to provide the current state-of-the art of the knowledge about the comorbidity between ADHD and Gaming Disorder. For this aim, a literature search was conducted using a combination of specific keywords and the results are discussed within the R-Do-C framework and dimensions, and implications for treatment are considered.

Elucidating the Neurobiologic Etiology of Comorbid PTSD and Substance Use Disorders

Early childhood maltreatment and other traumatic event experiences ("trauma") are common among youth, including those with substance use problems including substance use disorders (SUD). Particularly, interpersonal violence is associated with high rates of comorbidity between posttraumatic stress disorder (PTSD) and SUD, and these comorbid disorders exhibit high levels of overlapping symptomatology. Theoretical models proposed to explain the bidirectional relationship between PTSD and SUD include the self-medication hypothesis and susceptibility hypothesis. In this article, we explore neurobiologic changes associated with trauma, PTSD, and SUD that underly dysregulated stress response. Examining lessons learned from recent translational and clinical research, we propose that further elucidating the neurobiologic etiology of comorbid PTSD and SUD will require a collaborative, interdisciplinary approach, including the integration of preclinical and clinical studies, exploration of biologic markers in clinical studies, and accumulation of larger studies and longitudinal studies with the power to study PTSD and SUD. Such research can transform the field and ultimately reduce high rates and costly impairment of co-occurring PTSD and SUD across the lifespan.

Structural Equation Modeling (SEM): Gaming Disorder Leading Untreated Attention-Deficit/Hyperactivity Disorder to Disruptive Mood Dysregulation

(1) Background: Internet gaming disorder (IGD) in youths likely leads to disruptive mood dysregulation, especially among those with attention-deficit/hyperactivity disorder (ADHD). Whether IGD mediates the pathways leading ADHD to disruptive emotional dysfunction remains unclear. This study aims to elucidate the direct or indirect influence of IGD on ADHD; (2) Method: The Swanson, Nolan, and Pelham Version IV questionnaire was used to evaluate symptoms of ADHD and oppositional defiant disorder, and the Chen gaming disorder scale was used to measure IGD. A psychiatrist diagnosed ADHD, IGD, and disruptive mood dysregulation disorder (DMDD)-like symptoms. Structural equation modeling was applied to evaluate the role of IGD in mediating ADHD progression to disruptive mood dysregulation; (3) Results: Among a total of 102 ADHD youths, 53 (52%) of them with IGD were significantly more likely to have poor interpersonal relationships (p < 0.01) and DMDD-like symptoms (p < 0.01) than ADHD youths without IGD. IGD played a mediating role in increasing the risk of disruptive mood dysregulation in ADHD youths; (4) Conclusions: The findings suggest that IGD mediates ADHD’s progression to disruptive mood dysregulation. Intensive biopsychosocial interventions are warranted for ADHD youths with IGD. More children and adolescents became mood-dysregulated after excessive gaming during the COVID-19 pandemic; this study’s results suggest that child mental health experts develop earlier detection and prevention strategies for children and adolescents hidden behind internet addiction.

The Child PTSD Symptom Scale: Psychometric Properties among Earthquake Survivors

Evidence for a single underlying factor structure of posttraumatic stress disorder (PTSD) in children remains elusive. We assessed the underlying factor structure of the Child PTSD Symptom Scale through exploratory (EFA) and confirmatory factor analyses (CFA) in 570 survivors of the 2015 Gorkha earthquake in Nepal. The EFA suggests that the three-factor DSM-IV model fit these data best. The CFA suggests that while the DSM-IV model adequately fit these data, the four-factor King model fit them better. There was no evidence of differential item functioning by age or gender, and internal consistency of the scale was high. PTSD (overall or by factor) was not correlated with functional impairment. Inconsistent psychometric results across contexts and methodologies suggest that our current theoretical conceptualizations and empirical models of posttraumatic stress are lacking. Future studies must both document the instrument properties to assure internal validity and cross-study comparisons and, bolstered by increased psychometric data and analyses, rework theoretical models of PTSD with improved cross-cultural validity.

Treatment Efficacy of Internet Gaming Disorder With Attention Deficit Hyperactivity Disorder and Emotional Dysregulaton

BACKGROUND

Recent youth with Attention Deficit Hyperactivity Disorder (ADHD) noticed emotional dysregulation if they had Internet Gaming Disorder (IGD). This study aims to understand the treatment efficacy of IGD with ADHD and emotional dysregulaton.

METHOD

A total of 101 ADHD youths were recruited. We used the Chen Internet Addiction Scale and IGD criteria of the diagnotsic statistical manual (DSM)-5 to confirm IGD. The Swanson, Nolan, and Pelham questionnaire Version IV was used for symptoms of ADHD and oppositional defiant disorder. Disruptive mood dysregulation disorder was assessed by psychiatrist.

RESULTS

There is a new phenomenon that emotional dysregulation has been frequently noticed in severely gaming-addicted ADHD youth. Treatment efficacy of IGD is good when the underlying symptom of ADHD is controlled. Symptom scores of disruptive mood dysregulation (DMDD) were significantly reduced by 71.9%, 74.8%, and 84.4% at week 2, 3, and 4, respectively (P ≤ .001) after adjusting baseline symptom severity.

CONCLUSION

IGD may strongly arouse emotional dysregulation. Future DSM criteria could consider these gaming-addicted youth as a specific subclass of ADHD.

Medications for substance use disorders (SUD): emerging approaches

INTRODUCTION

Substance use disorders are a group of chronic relapsing disorders of the brain, which have massive public health and societal impact. In some disorders (e.g., heroin/prescription opioid addictions) approved medications have a major long-term benefit. For other substances (e.g., cocaine, amphetamines and cannabis) there are no approved medications, and for alcohol there are approved treatments, which are not in wide usage. Approved treatments for tobacco use disorders are available, and novel medications are also under study. Areas covered: Medication-based approaches which are in advanced preclinical stages, or which have reached proof-of concept clinical laboratory studies, as well as clinical trials. Expert opinion: Current challenges involve optimizing translation between preclinical and clinical development, and between clinical laboratory studies to therapeutic clinical trials. Comorbidities including depression or anxiety are challenges for study design and analysis. Improved pharmacogenomics, biomarker and phenotyping approaches are areas of interest. Pharmacological mechanisms currently under investigation include modulation of glutamatergic, GABA, vasopressin and κ-receptor function, as well as inhibition of monoamine re-uptake. Other factors that affect potential market size for emerging medications include stigma, availability of treatment settings, adoption by clinicians, and the prevalence of persons with SUD who are not actively treatment-seeking.