Translational research in psychiatry: The Research Domain Criteria Project (RDoC)

Multiple dimensions approach in polysubstance use: An ESEM analysis based on the RDoC framework

Substance use disorders (SUD) are associated with cognitive alterations, influenced by genetic, neurodevelopmental, and environmental factors, and frequently co-occur with emotional disorders, complicating treatment strategies. Employing the Research Domain Criteria (RDoC) framework and Exploratory Structural Equation Modeling (ESEM), this study investigated the direct and indirect relationships between drug abuse, neurocognitive performance, emotional indicators, and structural changes in brain regions implicated in emotional regulation and executive functions in SUD patients and healthy controls. High-resolution magnetic resonance imaging (MRI) scans were acquired from patients with SUD (n=50) and healthy controls (HC=50). Group differences were assessed using voxel-based morphometry (VBM) and regions of interest (ROIs). Participants were evaluated for substance use (ASSIST) and completed a battery of cognitive tasks and emotional tests. The SUD group exhibited higher levels of depression and anxiety, as well as cognitive deficits, compared to the control group. Additionally, the SUD group showed significantly lower gray matter volume (GMV) in cortical and limbic areas. However, the ESEM analysis indicated that limbic areas indirectly and negatively impacted ASSIST scores. Thus, we can conclude that structural impairments in limbic areas strongly influenced substance use patterns, even when reductions in cortical volume and impaired cognitive performance were also present.

Transdiagnostic factors predicting prescription opioid-use disorder severity: A 12-month prospective study in patients on long-term opioid therapy for chronic pain

BACKGROUND

Research has suggested that transdiagnostic factors related to reward, cognitive, and regulatory processes are involved in addictive behaviors and the experience of pain. However, studies of Prescription Opioid-Use Disorder (POUD) in a chronic pain population are scarce. This study aimed to analyze the predictive power of anticipatory pleasure experience, obsessive-compulsive behavior, cognitive control, emotion dysregulation, and sleep on POUD severity in chronic pain patients on long-term opioid therapy.

METHODS

A three-wave prospective study was conducted in a sample of 67 patients. Individual interviews were held to collect self-reported data on transdiagnostic factors, POUD, and sociodemographic and clinical variables. Statistical analysis included repeated measures multinomial mixed models, unadjusted and covariate-adjusted.

RESULTS

Rates of moderate-severe POUD significantly increased at 6- and 12-month follow-ups compared to initial assessment. The inability to experience anticipatory pleasure (OR [95 %CI] = 0.93 [0.89-0.98]), emotion dysregulation (OR = 1.06 [1.03-1.10]), and poorer sleep quality (OR = 1.25 [1.07-1.45]) predicted moderate-severe POUD even in the adjusted models (p< 0.05). In contrast, obsessive-compulsive behavior (OR = 1.02 [0.99-1.06]) was no longer significant after adjustment for covariates (p> 0.05). In addition, cognitive control was the only factor that predicted both mild (OR = 0.96 [0.93-0.99]) and moderate-severe (OR = 0.94 [0.90-0.98]) disorder (p< 0.05). Furthermore, when all transdiagnostic factors were included in a single model, cognitive control and anticipatory pleasure experience emerged as the strongest predictors of POUD severity, respectively (p< 0.05).

CONCLUSIONS

The identification of key transdiagnostic factors related to POUD severity allows for a more specific profiling of patients at increased risk, potentially leading to improved treatment targets for chronic pain population on opioid therapy.

Brain-Derived Neurotrophic Factor (BDNF) as a Predictor of Treatment Response in Schizophrenia and Bipolar Disorder: A Systematic Review

Brain-derived neurotrophic factor (BDNF) is a potential biomarker of response to treatment in psychiatric disorders. As it plays a role in the pathophysiological development of schizophrenia and bipolar disorder, it is of interest to study its role in predicting therapeutic responses in both conditions. We carried out a systematic review of the literature, looking for differences in baseline BDNF levels and the Val66Met BDNF polymorphism in these disorders between responders and non-responders, and found information showing that the Val/Val genotype and higher baseline BDNF levels may be present in patients that respond successfully to pharmacological and non-pharmacological treatments. However, there is still limited evidence to support the role of the Val66Met polymorphism and baseline BDNF levels as predictors of treatment response.

Psychotherapies in current clinical psychology and psychiatry in Spain

BACKGROUND

The practice of psychotherapy in Spain (Europe) continues being a reason for doubts, uncertainty, controversy or confusion. The access to psychotherapy is also controversial. To contribute to clarify all this in order to improve quality and minimise risks is the aim of our work.

METHOD

Update of the regulations and legal rules which are applicable in psychotherapy to those graduated in both psychology and medicine.

RESULTS AND DISCUSSION

Neuroscience, clinical psychology and psychiatry develop in a parallel and convergent way. It is necessary to emphasise the risks of pseudotherapies, including pseudopsychotherapies, and pseudosciences in general. The practice of psychotherapy as a psychological treatment necessarily implies the healthcare field both for the private and public sectors. In order to practice as a psychotherapist in Spain, it is necessary to be a psychologist specialised in clinical psychology or a doctor specialised in psychiatry. The figure of the sanitary general psychologist is a regulated profession in Spain at present, but that is not equivalent to be a specialist. The former psychologists who have the required legal qualification are also allowed to act for the public sector in the healthcare field. Other doctors who are not psychiatrists, other psychologists or the nurses specialised in mental health are not allowed to name themselves or act as psychotherapists in any field or sector.

Modelling the effects of the exposome score within the extended psychosis phenotype

It has been reported that cumulative measures of risk factors for psychosis might help to predict its development. However, it remains unknown as to whether these measures are also associated with the extended psychosis phenotype that refers to a continuum of features bridging subclinical symptoms with clinically relevant outcomes. In this study, we aimed to investigate the association of the exposome score (ES) with psychosis risk in a non-clinical population. A total of 1100 non-clinical adults (aged 18-35 years, 51.4% females) with a negative history of psychiatric treatment were recruited. The Prodromal Questionnaire-16 (PQ-16) was used to screen for psychosis risk. Self-reports were used to record environmental exposures. The ES was significantly higher in participants with the positive PQ-16 screening. Specifically, the prevalence of obstetric complications, non-right handedness, all categories of childhood trauma, and problematic cannabis use was significantly higher in this group of participants. A network analysis demonstrated that the ES was directly connected not only to items representing psychotic experiences ("paranoid thoughts", "a lack of control over own ideas or thoughts", "thought echo", and "being distracted by distant sounds") but also those covering depressive or anxiety symptoms ("uninterested in things used to enjoy" and "feeling anxious when meeting people for the first time"). In conclusion, the ES is associated with the extended psychosis phenotype, suggesting its potential to identify individuals who may benefit from further psychosis risk assessment. The ES appears to contribute to non-specific psychopathology, which may, in some cases, progress to psychosis.

Neural correlates of irritability symptom relief in adolescents pre- and post-trauma-focused cognitive behavioral therapy: A pilot study on reward processing

Despite that Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) is a first-line, evidence-based treatment for youths experiencing trauma-related symptoms, treatment responses vary and it remains unclear for whom and how this treatment works. In this context, we examined pre-treatment neural reward processing and pre- vs. post-treatment changes in neural reward processing, in relation to irritability - a transdiagnostic and dimensional feature present in multiple trauma-related syndromes, following TF-CBT. Adolescents (N = 22) with childhood trauma history completed a child-friendly monetary incentive delay task during fMRI acquisition, prior to and after the treatment, and irritability symptoms were assessed at five time points over the course of the treatment. Individual irritability slopes (i.e., irritability change rate) and intercepts (i.e., initial irritability level), generated by linear growth curve modeling, were integrated with fMRI data. Repeated ANCOVAs demonstrated that both pre-treatment neural response to reward and pre- vs. post-treatment changes in neural reward processing correlated with irritability symptom relief, such that opposite baseline neural reward processing profiles and differential changing patterns were observed in individuals showing irritability symptom relief vs. not. Together, our findings provide proof of concept that integrating brain information with clinical information has the potential to identify predictors and mechanisms of symptom relief.

Obsessive-compulsive symptoms in anxiety and depressive disorders: Influence of recent and/or traumatic life events

INTRODUCTION

A high prevalence of obsessive-compulsive symptoms (OCSs) in anxiety-depressive disorders ranging from 30 to 67% has been described.

OBJECTIVE

This study aims to assess the presence and persistence of OCSs in an outpatient sample of subjects with anxiety and depressive disorders, as well as its relationship with recent life events (RLEs) and/or traumatic experiences (TEs).

METHOD

We conducted a prospective, observational, analytical study of 200 subjects with DSM-5 diagnoses of anxiety and/or depression. Participants were included by consecutive sampling and were evaluated at baseline and after 6-12 months (mean 8.5 months) of follow-up. The severity of the symptoms was assessed through the Hamilton Anxiety Scale (HARS) and Hamilton Scale for the evaluation of depression (HRSD-17), and comorbidity was assessed through the International Neuropsychiatric Interview (MINI). The Revised Inventory of Obsessions and Compulsions (OCI-R), the Recent Vital Changes Questionnaire (CVSV), and the Diagnostic Scale for Post-Traumatic Stress (PDS) were also administered.

RESULTS

54% of the sample presented OCSs, and 30.5% presented one or more TEs throughout life. At the baseline visit, the presence of OCSs was associated with the severity of depressive symptoms (p=0.028), the presence of TEs (p<0.01), symptoms of post-traumatic stress disorder (p<0.01) and the number of RLEs (p<0.01). Response rate at follow-up was 38%, and persistence of OCSs was found in 60.5% of patients, independent of depressive or anxious symptoms, but was associated with the number of RLEs (p<0.01).

CONCLUSION

The presence of OCSs in patients with anxiety and depressive disorders is frequent and persistent. Anxious-depressive patients with a history of TEs and RLEs had higher OCS levels. These findings highlight the importance of early detection and the use of therapeutic strategies focused on resilience to stress and trauma.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.

OBJECTIVE

The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.

METHODS

This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.

RESULTS

A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.

CONCLUSIONS

Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Obsessive-compulsive symptoms in anxiety and depressive disorders: Influence of recent and/or traumatic life events

INTRODUCTION

A high prevalence of obsessive-compulsive symptoms (OCSs) in anxiety-depressive disorders ranging from 30 to 67% has been described.

OBJECTIVE

This study aims to assess the presence and persistence of OCSs in an outpatient sample of subjects with anxiety and depressive disorders, as well as its relationship with recent life events (RLEs) and/or traumatic experiences (TEs).

METHOD

We conducted a prospective, observational, analytical study of 200 subjects with DSM-5 diagnoses of anxiety and/or depression. Participants were included by consecutive sampling and were evaluated at baseline and after 6-12 months (mean 8.5 months) of follow-up. The severity of the symptoms was assessed through the Hamilton Anxiety Scale (HARS) and Hamilton Scale for the evaluation of depression (HRSD-17), and comorbidity was assessed through the International Neuropsychiatric Interview (MINI). The Revised Inventory of Obsessions and Compulsions (OCI-R), the Recent Vital Changes Questionnaire (CVSV), and the Diagnostic Scale for Post-Traumatic Stress (PDS) were also administered.

RESULTS

54% of the sample presented OCSs, and 30.5% presented one or more TEs throughout life. At the baseline visit, the presence of OCSs was associated with the severity of depressive symptoms (p=0.028), the presence of TEs (p<0.01), symptoms of post-traumatic stress disorder (p<0.01) and the number of RLEs (p<0.01). Response rate at follow-up was 38%, and persistence of OCSs was found in 60.5% of patients, independent of depressive or anxious symptoms, but was associated with the number of RLEs (p<0.01).

CONCLUSION

The presence of OCSs in patients with anxiety and depressive disorders is frequent and persistent. Anxious-depressive patients with a history of TEs and RLEs had higher OCS levels. These findings highlight the importance of early detection and the use of therapeutic strategies focused on resilience to stress and trauma.