Taking Psychiatry Research Online

Reliable affordances: A generative modeling approach for test-retest reliability of the affordances task

The affordances task serves as an important tool for the assessment of cognition and visuomotor functioning, and yet its test-retest reliability has not been established. In the affordances task, participants attend to a goal-directed task (e.g., classifying manipulable objects such as cups and pots) while suppressing their stimulus-driven, irrelevant reactions afforded by these objects (e.g., grasping their handles). This results in cognitive conflicts manifesting at the task level and the response level. In the current study, we assessed the reliability of the affordances task for the first time. While doing so, we referred to the "reliability paradox," according to which behavioral tasks that produce highly replicable group-level effects often yield low test-retest reliability due to the inadequacy of traditional correlation methods in capturing individual differences between participants. Alongside the simple test-retest correlations, we employed a Bayesian generative model that was recently demonstrated to result in a more precise estimation of test-retest reliability. Two hundred and ninety-five participants completed an online version of the affordances task twice, with a one-week gap. Performance on the online version replicated results obtained under in-lab administrations of the task. While the simple correlation method resulted in weak test-retest measures of the different effects, the generative model yielded a good reliability assessment. The current results support the utility of the affordances task as a reliable behavioral tool for the assessment of group-level and individual differences in cognitive and visuomotor functioning. The results further support the employment of generative modeling in the study of individual differences.

Automated analysis of speech as a marker of sub-clinical psychotic experiences

Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications. Although sub-clinical research could aid to tackle most of these challenges, there have been only few studies conducted in speech and psychosis research in non-clinical populations. This work aims to facilitate this work by summarizing automated speech analytical concepts and the intersection of this field with psychosis research. We review psychosis continuum and sub-clinical psychotic experiences, and the benefits of researching them. Then, we discuss the connection between speech and psychotic symptoms. Thirdly, we overview current and state-of-the art approaches to the automated analysis of speech both in terms of language use (text-based analysis) and vocal features (audio-based analysis). Then, we review techniques applied in subclinical population and findings in these samples. Finally, we discuss research challenges in the field, recommend future research endeavors and outline how research in subclinical populations can tackle the listed challenges.

Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG)

Research has shown that relying only on self-reports for diagnosing psychiatric disorders does not yield accurate results at all times. The advances of technology as well as artificial intelligence and other machine learning algorithms have allowed the introduction of point of care testing (POCT) including EEG characterization and correlations with possible psychopathology. Nonlinear methods of EEG analysis have significant advantages over linear methods. Empirical mode decomposition (EMD) is a reliable nonlinear method of EEG pre-processing. In this chapter, we compare two existing EEG complexity measures - Higuchi fractal dimension (HFD) and sample entropy (SE), with our newly proposed method using Higuchi fractal dimension from the Hilbert Huang transform (HFD-HHT). We present an example using the three complexity measures on a 2-minute EEG recorded from a healthy 20-year-old male after signal pre-processing. Furthermore, we showed the usefulness of these complexity measures in the classification of major depressive disorder (MDD) with healthy controls. Our study is in line with previous research and has shown an increase in HFD and SE values in the full, alpha and beta frequency bands suggestive of an increase in EEG irregularity. Moreover, the HFD-HHT values decreased in those three bands for majority of electrodes which is suggestive of a decrease in irregularity in the frequency-time domain. We conclude that all three complexity measures can be vital features useful for EEG analysis which could be incorporated in POCT systems.

Measuring Compulsivity as a Self-Reported Multidimensional Transdiagnostic Construct: Large-Scale (N = 182,000) Validation of the Cambridge-Chicago Compulsivity Trait Scale

Compulsivity has potential transdiagnostic relevance to a range of psychiatric disorders, but it has not been well-characterized and there are few existing measures available for measuring the construct across clinical and nonclinical samples that have been validated at large population scale. We aimed to characterize the multidimensional latent structure of self-reported compulsivity in a population-based sample of British children and adults (N = 182,145) using the Cambridge-Chicago Compulsivity Trait Scale (CHI-T). Exploratory structural equation modeling provided evidence for a correlated two-factor model consisting of (a) Perfectionism and (b) Reward Drive dimensions. Evidence was obtained for discriminant validity in relation to the big five personality dimensions and acceptable test-retest reliability. The CHI-T, here validated at extremely large scale, is suitable for use in studies seeking to understand the correlates and basis of compulsivity in clinical and nonclinical participants. We provide extensive normative data to facilitate interpretation in future studies.

Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression

Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good and useful accuracy. The LSTM network that was trained on a modified dataset with only 1000 most-important features from the random forest model with temporality showed the best performance with a mean AUC of 0.901 and a standard deviation of 0.006 using a training dataset and a mean AUC of 0.810 and 0.01 using a hold-out test dataset. Comparing the results from the technical evaluation with the views of psychiatrists shows that there are now opportunities to refine and integrate such prediction models into pragmatic point-of-care clinical decision support tools for supporting mental healthcare delivery.

Inattentive responding can induce spurious associations between task behaviour and symptom measures

Although online samples have many advantages for psychiatric research, some potential pitfalls of this approach are not widely understood. Here we detail circumstances in which spurious correlations may arise between task behaviour and symptom scores. The problem arises because many psychiatric symptom surveys have asymmetric score distributions in the general population, meaning that careless responders on these surveys will show apparently elevated symptom levels. If these participants are similarly careless in their task performance, this may result in a spurious association between symptom scores and task behaviour. We demonstrate this pattern of results in two samples of participants recruited online (total N = 779) who performed one of two common cognitive tasks. False-positive rates for these spurious correlations increase with sample size, contrary to common assumptions. Excluding participants flagged for careless responding on surveys abolished the spurious correlations, but exclusion based on task performance alone was less effective.

Measuring psychiatric symptoms online: A systematic review of the use of inventories on Amazon Mechanical Turk (mTurk)

Symptom measurement in psychiatric research increasingly uses digitized self-report inventories and is turning to crowdsourcing platforms for recruitment, e.g., Amazon Mechanical Turk (mTurk). The impact of digitizing pencil-and-paper inventories on the psychometric properties is underexplored in mental health research. Against this background, numerous studies report high prevalence estimates of psychiatric symptoms in mTurk samples. Here we develop a framework to evaluate the online implementation of psychiatric symptom inventories relative to two domains, that is, the adherence to (i) validated scoring and (ii) standardized administration. We apply this new framework to the online use of the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Alcohol Use Disorder Identification Test (AUDIT). Our systematic review of the literature identified 36 implementations of these three inventories on mTurk across 27 publications. We also evaluated methodological approaches to enhance data quality, e.g., the use of bot detection and attention check items. Of the 36 implementations, 23 reported the applied diagnostic scoring criteria and only 18 reported the specified symptom timeframe. None of the 36 implementations reported adaptations made in their digitization of the inventories. While recent reports attribute higher rates of mood, anxiety, and alcohol use disorders on mTurk to data quality, our findings indicate that this inflation may also relate to the assessment methods. We provide recommendations to enhance both data quality and fidelity to validated administration and scoring methods.

Practices, knowledge, and attitudes about lithium treatment: Results of online surveys completed by clinicians and lithium-treated patients

BACKGROUND

Lithium remains the gold-standard medication for acute and prophylactic treatment of bipolar disorder. Understanding clinicians' practices and patients' experiences, knowledge and attitudes about lithium may improve its clinical use.

METHODS

Online anonymous surveys collected information about clinician's practices and level of confidence in managing lithium and patients' experiences with lithium treatment and information received about benefits and side effects. Knowledge and attitudes regarding lithium were assessed with the Lithium Knowledge Test (LKT) and the Lithium Attitudes Questionnaire (LAQ).

RESULTS

Among 201 clinicians, 64.2% endorsed often treating patients with lithium and reported high levels of confidence in assessing and managing lithium. Practices concerning clinical indications, drug titration, and serum levels were guideline-concordant, but compliance with monitoring recommendations was less frequent. Practitioners were interested in receiving more education about lithium. The patients' survey recruited 219 participants with 70.3% being current lithium users. Most patients (68%) found lithium helpful and 71% reported experiencing any kind of side effect. Most responders did not receive information about side effects or other benefits of lithium. Patients with higher scores on the LKT were more likely to have positive attitudes about lithium.

LIMITATIONS

Cross-sectional design with predominantly English-speaking participants from Brazil and North America.

CONCLUSIONS

There is a discrepancy between guidelines, clinician confidence and knowledge of lithium use and practice. A deeper understanding of how to monitor, prevent and manage long-term side effects and which patients are most likely to benefit from lithium may narrow the gap between knowledge and use.

Role of the amygdala in disrupted integration and effective connectivity of cortico-subcortical networks in apathy

BACKGROUND

Apathy is a quantitative reduction in motivation and goal-directed behaviors, not only observed in neuropsychiatric disorders, but also present in healthy populations. Although brain abnormalities associated with apathy in clinical disorders have been studied, the organization of brain networks in healthy individuals has yet to be identified.

METHOD

We examined properties of intrinsic brain networks in healthy individuals with varied levels of apathy. By using functional magnetic resonance imaging in combination with graph theory analysis and dynamic causal modeling analysis, we tested communications among nodes and modules as well as effective connectivity among brain networks.

RESULTS

We found that the average participation coefficient of the subcortical network, especially the amygdala, was lower in individuals with high than low apathy. Importantly, we observed weaker effective connectivity fromthe hippocampus and parahippocampal gyrus to the amygdala, and from the amygdala to the parahippocampal gyrus and medial frontal cortex in individuals with apathy.

CONCLUSION

These findings suggest that individuals with high apathy exhibit aberrant communication within the cortical-to-subcortical network, characterized by differences in amygdala-related effective connectivity. Our work sheds light on the neural basis of apathy in subclinical populations and may have implications for understanding the development of clinical conditions that feature apathy.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BACKGROUND

Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.

METHODS

A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.

RESULTS

Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).

CONCLUSIONS

An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Self-reported influences on willingness to receive COVID-19 vaccines among physically ill, mentally ill, and healthy individuals

OBJECTIVE

Individuals with mental and physical disorders have been disproportionately affected by adverse health outcomes due to the COVID-19 pandemic, and yet vaccine hesitancy persists despite clear evidence of health benefits. Therefore, our study explored factors influencing willingness to receive a COVID-19 vaccine.

METHODS

Individuals with mental illness (n = 332), physical illness (n = 331), and no health issues (n = 328) were recruited via Amazon Mechanical Turk. Participants rated willingness to obtain a fully approved COVID-19 vaccine or a vaccine approved only for experimental/emergency use and influences in six domains upon their views. We examined differences by health status.

RESULTS

Participants across groups were moderately willing to receive a COVID-19 vaccine. Perceived risk was negatively associated with willingness. Participants differentiated between vaccine risk by approval stage and were less willing to receive an experimental vaccine. Individuals with mental illness rated risk of both vaccines similarly to healthy individuals. Individuals with physical illness expressed less willingness to receive an experimental vaccine. Domain influences differently affected willingness by health status as well as by vaccine approval status.

CONCLUSIONS

Our findings are reassuring regarding the ability of people with mental disorders to appreciate risk in medical decision-making and the ability of people of varied health backgrounds to distinguish between the benefits and risks of clinical care and research, refuting the prevailing notions of psychiatric exceptionalism and therapeutic misconception. Our findings shine a light on potential paths forward to support vaccine acceptance.

Association Between Intimate Partner Violence Subtypes and Post-traumatic Stress Disorder Symptoms and Hazardous Substance Use

Intimate partner violence (IPV) is a common problem for women in the United States and is associated with symptoms of post-traumatic stress disorder (PTSD) as well as hazardous use of substances like alcohol and drugs. However, not all subtypes of IPV (i.e., physical, sexual, and psychological) are equally predictive of PTSD and hazardous substance use. Although previous research suggests that psychological IPV has the strongest relative effect on PTSD symptoms and substance use, there is less research on IPV subtypes' cumulative effects. In this study, we examined the relative and cumulative effects of physical, sexual, and psychological IPV on PTSD symptoms and hazardous substance use in a sample of women in the United States recruited via Amazon's Mechanical Turk (N = 793) using bootstrapped multiple regression and configural frequency analyses. Results suggest that physical IPV had the most pronounced influence (medium-large effect sizes) on substance use across women, but that the cumulative effects of all three IPV subtypes were most closely associated with diagnostic levels of both PTSD and substance use at the level of groups of women. These findings clarify and extend previous research on the differential effects of IPV subtypes and provide directions for future research and clinical intervention.

Value-free random exploration is linked to impulsivity

Deciding whether to forgo a good choice in favour of exploring a potentially more rewarding alternative is one of the most challenging arbitrations both in human reasoning and in artificial intelligence. Humans show substantial variability in their exploration, and theoretical (but only limited empirical) work has suggested that excessive exploration is a critical mechanism underlying the psychiatric dimension of impulsivity. In this registered report, we put these theories to test using large online samples, dimensional analyses, and computational modelling. Capitalising on recent advances in disentangling distinct human exploration strategies, we not only demonstrate that impulsivity is associated with a specific form of exploration—value-free random exploration—but also explore links between exploration and other psychiatric dimensions.

The Stage 1 protocol for this Registered Report was accepted in principle on 19/03/2021. The protocol, as accepted by the journal, can be found at 10.6084/m9.figshare.14346506.v1.

Deciding between known rewarding options and exploring novel avenues is central to decision making. Humans show variability in their exploration. Here, the authors show that impulsivity is associated to an increased usage of a cognitively cheap (and sometimes sub-optimal) exploration strategy.

Maternal Perceptions of Safeguards for Research Involving Children

The vitality of clinical research and the health of the public relies on continued efforts to engage children in clinical research in a fully protected and ethically robust manner. Parents serve as proxy decision-makers assessing the risks and benefits of any given study in order to do what is in the best interest of their child. This study investigated maternal perceptions of research safeguards and mothers' willingness to enroll their children in clinical research studies. We hypothesized that mothers' perceptions of the protectiveness of safeguard procedures utilized in clinical research would be associated with mothers' willingness to enroll their children in research studies with such safeguards. Through a survey conducted via Amazon Mechanical Turk, mothers were asked to rate the perceived protectiveness of four safeguard procedures (confidential data coding, data and safety monitoring boards (DSMBs), institutional review boards (IRBs), and informed consent) and the degree to which they were willing to have their child participate in research studies in the presence of each of the four safeguard procedures. Respondents generally perceived safeguard procedures to be protective. Mothers' trust in researchers' honesty positively impacted perceptions of the protectiveness of research safeguard procedures and willingness to enroll children in research. Mothers of only healthy children perceived research safeguards to be more protective than mothers with at least one child with at least one health issue. This study provides insight into whether maternal perceptions of the protectiveness of different safeguard procedures are associated with mothers' willingness to enroll their children in research.

Influence of Dispositional Optimism on Ethically Salient Research Perspectives: A Pilot Study

Research participants should be drawn as fairly as possible from the potential volunteer population. Underlying personality traits are underexplored as factors influencing research decision-making. Dispositional optimism, known to affect coping, physical health, and psychological well-being, has been minimally studied with respect to research-related attitudes. We conducted an exploratory, online survey with 151 individuals (with self-reported mental illness [n = 50], physical illness [n = 51], or neither [n = 50]) recruited via MTurk. We evaluated associations between dispositional optimism (assessed with the Life Orientation Test-Revised) and general research attitudes, perceived protectiveness of five research safeguards, and willingness to participate in research using safeguards. Strongly optimistic respondents expressed more positive research attitudes and perceived four safeguards as more positively influencing willingness to participate. Optimism was positively associated with expressed willingness to participate in clinical research. Our findings add to a limited literature on the influence of individual traits on ethically salient research perspectives.

Personality as a mediator of autistic traits and internalizing symptoms in two community samples

Background

Autism spectrum disorder (ASD) is characterized by deficits in social functioning and is comorbid with internalizing disorders and symptoms. While personality is associated with these symptoms and social functioning in non-ASD samples, its role mediating the relationship between ASD traits and internalizing symptoms is not clear.

Methods

We studied the mediating effect of personality on the correlations between ASD traits and internalizing symptoms (i.e., depression, anxiety, stress) in two samples. Additionally, we explored the moderating effect of gender. Analyses were applied to a small (Study 1; N = 101) undergraduate sample. A broader sample recruited via an online crowdsourcing platform (Study 2; N = 371) was used to validate the results.

Results

Study 1’s mediation analyses revealed that neuroticism was the only significant mediator. Study 2 replicated these results by finding extraversion to be an additional mediator for anxiety and extraversion, openness, and agreeableness as additional mediators for stress. Moderation analyses revealed that gender was never a significant moderator.

Conclusions

These results support the effects of personality on the relationship between autism traits and internalizing symptoms. Future research should explore these effects in clinical samples to better understand the role of personality in symptomatology and the need to address it as part of intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-022-00774-z.

The effect of apathy and compulsivity on planning and stopping in sequential decision-making

Real-life decision-making often comprises sequences of successive decisions about whether to take opportunities as they are encountered or keep searching for better ones instead. We investigated individual differences related to such sequential decision-making and link them especially to apathy and compulsivity in a large online sample (discovery sample: n = 449 and confirmation sample: n = 756). Our cognitive model revealed distinct changes in the way participants evaluated their environments and planned their own future behaviour. Apathy was linked to decision inertia, i.e., automatically persisting with a sequence of searches for longer than appropriate given the value of searching. Thus, despite being less motivated, they did not avoid the effort associated with longer searches. In contrast, compulsivity was linked to self-reported insensitivity to the cost of continuing with a sequence of searches. The objective measures of behavioural cost insensitivity were clearly linked to compulsivity only in the discovery sample. While the confirmation sample showed a similar effect, it did not reach significance. Nevertheless, in both samples, participants reported awareness of such bias (experienced as "overchasing"). In addition, this awareness made them report preemptively avoiding situations related to the bias. However, we found no evidence of them actually preempting more in the task, which might mean a misalignment of their metacognitive beliefs or that our behavioural measures were incomplete. In summary, individual variation in distinct, fundamental aspects of sequential decision-making can be linked to variation in 2 measures of behavioural traits associated with psychological illness in the normal population.

Stakeholders' Perspectives regarding Participation in Neuromodulation-Based Dementia Intervention Research

This study evaluated stakeholders' perspectives regarding participation in two hypothetical neuromodulation trials focused on individuals with Alzheimer's disease and related disorders (ADRDs). Stakeholders (i.e., individuals at risk for ADRDs [n = 56], individuals with experience as a caregiver for someone with a cognitive disorder [n = 60], and comparison respondents [n = 124]) were recruited via MTurk. Primary outcomes were willingness to enroll (or enroll one's loved one), feeling lucky to have the opportunity to enroll, and feeling obligated to enroll in two protocols (transcranial magnetic stimulation, TMS; deep brain stimulation, DBS). Relative to the Comparison group, the At Risk group endorsed higher levels of "feeling lucky" regarding both research protocols, and higher willingness to participate in the TMS protocol. These findings provide tentative reassurance regarding the nature of decision making regarding neurotechnology-based research on ADRDs. Further work is needed to evaluate the full range of potential influences on research participation.

Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks

We describe the design and evaluation of a sub-clinical digital assessment tool that integrates digital biomarkers of depression. Based on three standard cognitive tasks (D2 Test of Attention, Delayed Matching to Sample Task, Spatial Working Memory Task) on which people with depression have been known to perform differently than a control group, we iteratively designed a digital assessment tool that could be deployed outside of laboratory contexts, in uncontrolled home environments on computer systems with widely varying system characteristics (e.g., displays resolution, input devices). We conducted two online studies, in which participants used the assessment tool in their own homes, and completed subjective questionnaires including the Patient Health Questionnaire (PHQ-9)-a standard self-report tool for assessing depression in clinical contexts. In a first study (n = 269), we demonstrate that each task can be used in isolation to significantly predict PHQ-9 scores. In a second study (n = 90), we replicate these results and further demonstrate that when used in combination, behavioral metrics from the three tasks significantly predicted PHQ-9 scores, even when taking into account demographic factors known to influence depression such as age and gender. A multiple regression model explained 34.4% of variance in PHQ-9 scores with behavioral metrics from each task providing unique and significant contributions to the prediction.

Factors Influencing Perceived Helpfulness and Participation in Innovative Research: A Pilot Study of Individuals with and without Mood Symptoms

Little is known about how individuals with and without mood disorders perceive the inherent risks and helpfulness of participating in innovative psychiatric research, or about the factors that influence their willingness to participate. We conducted an online survey with 80 individuals (self-reported mood disorder [n = 25], self-reported good health [n = 55]) recruited via MTurk. We assessed respondents' perceptions of risk and helpfulness in study vignettes associated with two innovative research projects (intravenous ketamine therapy and wearable devices), as well as their willingness to participate in these projects. Respondents with and without mood disorders perceived risk similarly across projects. Respondents with no mood disorders viewed both projects as more helpful to society than to research volunteers, while respondents with mood disorders viewed the projects as equally helpful to volunteers and society. Individuals with mood disorders perceived ketamine research, and the two projects on average, as more helpful to research volunteers than did individuals without mood disorders. Our findings add to a limited empirical literature on the perspectives of volunteers in innovative psychiatric research.

Psychiatric symptoms influence reward-seeking and loss-avoidance decision-making through common and distinct computational processes

AIM

Psychiatric symptoms are often accompanied by impairments in decision-making to attain rewards and avoid losses. However, due to the complex nature of mental disorders (e.g., high comorbidity), symptoms that are specifically associated with deficits in decision-making remain unidentified. Furthermore, the influence of psychiatric symptoms on computations underpinning reward-seeking and loss-avoidance decision-making remains elusive. Here, we aim to address these issues by leveraging a large-scale online experiment and computational modeling.

METHODS

In the online experiment, we recruited 1900 non-diagnostic participants from the general population. They performed either a reward-seeking or loss-avoidance decision-making task, and subsequently completed questionnaires about psychiatric symptoms.

RESULTS

We found that one trans-diagnostic dimension of psychiatric symptoms related to compulsive behavior and intrusive thought (CIT) was negatively correlated with overall decision-making performance in both the reward-seeking and loss-avoidance tasks. A deeper analysis further revealed that, in both tasks, the CIT psychiatric dimension was associated with lower preference for the options that recently led to better outcomes (i.e. reward or no-loss). On the other hand, in the reward-seeking task only, the CIT dimension was associated with lower preference for recently unchosen options.

CONCLUSION

These findings suggest that psychiatric symptoms influence the two types of decision-making, reward-seeking and loss-avoidance, through both common and distinct computational processes.

Reliability of web-based affective auditory stimulus presentation

Web-based experimental testing has seen exponential growth in psychology and cognitive neuroscience. However, paradigms involving affective auditory stimuli have yet to adapt to the online approach due to concerns about the lack of experimental control and other technical challenges. In this study, we assessed whether sounds commonly used to evoke affective responses in-lab can be used online. Using recent developments to increase sound presentation quality, we selected 15 commonly used sound stimuli and assessed their impact on valence and arousal states in a web-based experiment. Our results reveal good inter-rater and test-retest reliabilities, with results comparable to in-lab studies. Additionally, we compared a variety of previously used unpleasant stimuli, allowing us to identify the most aversive among these sounds. Our findings demonstrate that affective sounds can be reliably delivered through web-based platforms, which help facilitate the development of new auditory paradigms for affective online experiments.

Smartphones and the Neuroscience of Mental Health

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Fear of Dependency and Life-Space Mobility as Predictors of Attitudes Toward Assistive Devices in Older Adults

Assistive devices can help older adults remain independent; however, they may hesitate to use them due to fears of appearing dependent by embodying aging stereotypes. Reluctance to use assistive devices may lead to decreased life space mobility. The selective optimization with compensation (SOC) model posits that older adults employ strengths to accommodate for age-related functioning declines. The current study examines the predictive power of health perceptions, dependency fears, aging stereotypes, and life space on older adults' views of assistive devices. Results suggest that older adults with greater life space and dependency fears are more likely to view assistive devices positively.

Perceived protectiveness of research safeguards and influences on willingness to participate in research: A novel MTurk pilot study

Little is known about how individuals with mood disorders view the protectiveness of research safeguards, and whether their views affect their willingness to participate in psychiatric research. We conducted an online survey with 80 individuals (self-reported mood disorder [n = 25], self-reported good health [n = 55]) recruited via MTurk. We assessed respondents' perceptions of the protectiveness of five common research safeguards, as well as their willingness to participate in research that incorporates each safeguard. Perceived protectiveness was strongly related to willingness to participate in research for four of the safeguards. Our findings add to a limited literature on the motivations and perspectives of key stakeholders in psychiatric research.

Recent Developments in the Habit Hypothesis of OCD and Compulsive Disorders

This chapter aims to familiarise the reader with a diverse and fast-growing literature concerning the role that habits play in obsessive-compulsive disorder (OCD). Core concepts will be introduced, including how the balance between habits and a more deliberate form of action selection (goal-directed control) has traditionally been measured and how cross-species translation, neuroscience tools, and computational modelling have been used to build on these basic principles and reveal core mechanisms under study today. Next, the application of these methods to the study of OCD and related disorders will be detailed, converging on a theory that enhanced habit expression, and indeed compulsions in OCD, might arise from deficits in goal-directed control systems. These basic findings will be contextualised in terms of major tide changes in the field, including the shift from categorical disease frameworks to dimensional ones. Mechanistically, recent research concerning how goal-directed deficits arise, perhaps through failures in the construction of a mental model, are discussed along with studies critically evaluating our ability to measure habits in humans, in a laboratory setting. The chapter ends with a nod to the future, focusing on the need for clinically oriented, longitudinal, and intervention-based research that aim to translate what is now a wealth of cross-sectional mechanistic insights to the clinic.

Computational Psychiatry for Computers

Computational psychiatry is a nascent field that attempts to use multi-level analyses of the underlying computational problems that we face in navigating a complex, uncertain and changing world to illuminate mental dysfunction and disease. Two particular foci of the field are the costs and benefits of environmental adaptivity and the danger and necessity of heuristics. Here, we examine the extent to which these foci and others can be used to study the actual and potential flaws of the artificial computational devices that we are increasingly inventing and empowering to navigate this very same environment on our behalf.

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

Background

Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use.

Objective

This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models.

Methods

To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology.

Results

We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism.

Conclusions

This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.

The role of Experiential Avoidance in transdiagnostic compulsive behavior: A structural model analysis

Compulsivity is recognized as a transdiagnostic phenotype, underlying a variety of addictive and obsessive-compulsive behaviors. However, current understanding of how it should be operationalized and the processes contributing to its development and maintenance is limited. The present study investigated if there was a relationship between the affective process Experiential Avoidance (EA), an unwillingness to tolerate negative internal experiences, and the frequency and severity of transdiagnostic compulsive behaviors. A large sample of adults (N = 469) completed online questionnaires measuring EA, psychological distress and the severity of seven obsessive-compulsive and addiction-related behaviors. Using structural equation modelling, results indicated a one-factor model of compulsivity was superior to the two-factor model (addictive- vs OCD-related behaviors). The effect of EA on compulsivity was fully mediated by psychological distress, which in turn had a strong direct effect on compulsivity. This suggests distress is a key mechanism in explaining why people with high EA are more prone to compulsive behaviors. The final model explained 41% of the variance in compulsivity, underscoring the importance of these constructs as likely risk and maintenance factors for compulsive behavior. Implications for designing effective psychological interventions for compulsivity are discussed.

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries.

Attitudes of Mothers Regarding Willingness to Enroll Their Children in Research

This study assessed mothers' perspectives regarding research involvement by their children, factors that might affect perceptions of research risks, and attitudes regarding willingness to enroll children in research. Participants completed a survey on Amazon Mechanical Turk. Mothers were less inclined to enroll children in research involving procedures posing higher risk (regression coefficient = -0.51). Mothers without mental health issues with children without health issues were more sensitive to risk than mothers without mental health issues with children with health issues (estimated difference = 0.49). Mothers with mental health issues were more willing than mothers without mental health issues to enroll children in research (regression coefficient = -0.90). Among mothers with mental health issues, having a child with a health issue was associated with increased willingness to enroll in research, compared with having children without health issues (estimated difference = 0.65).

Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression

OBJECTIVES

Biomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions.

METHODS

Resting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex-matched healthy control subjects. Artifact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms.

RESULTS

Depressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro-parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder.

CONCLUSIONS

Complexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.

Anxiety Impedes Adaptive Social Learning Under Uncertainty

Very little is known about how individuals learn under uncertainty when other people are involved. We propose that humans are particularly tuned to social uncertainty, which is especially noisy and ambiguous. Individuals exhibiting less tolerance for uncertainty, such as those with anxiety, may have greater difficulty learning in uncertain social contexts and therefore provide an ideal test population to probe learning dynamics under uncertainty. Using a dynamic trust game and a matched nonsocial task, we found that healthy subjects (n = 257) were particularly good at learning under negative social uncertainty, swiftly figuring out when to stop investing in an exploitative social partner. In contrast, subjects with anxiety (n = 97) overinvested in exploitative partners. Computational modeling attributed this pattern to a selective reduction in learning from negative social events and a failure to enhance learning as uncertainty rises-two mechanisms that likely facilitate adaptive social choice.

Negative association of perceived risk and willingness to participate in innovative psychiatric research protocols

Psychiatric researchers grapple with concerns that individuals with mental illness may be less likely to appreciate risks of research participation, particularly compared to people not suffering from mental illness. Therefore, empirical studies that directly compare the perspectives of such individuals are needed. In addition, it is important to evaluate perspectives regarding varied types of research protocols, particularly as innovative psychiatric research protocols emerge. In this pilot study, respondents with a mood disorder (n = 25) as well as respondents without a mood disorder (n = 55) were recruited using Amazon's Mechanical Turk (MTurk) platform. These respondents were surveyed regarding four psychiatric research projects (i.e., experimental medication [pill form]; non-invasive magnetic brain stimulation; experimental medication [intravenous infusion]; and implantation of a device in the brain). Regardless of health status, respondents rated the four research protocols as somewhat to highly risky. The brain-device implant protocol was seen as the most risky, while the magnetic brain stimulation project was viewed as "somewhat risky". Respondents, on average and regardless of health status, rated their willingness at or below "somewhat willing." Respondents were least willing to participate in the brain-device implant protocol, whereas they were "somewhat willing" to participate in the magnetic brain stimulation protocol. Trust in medical research was negatively associated with perceived risk of research protocols. Perceived risk was negatively associated with willingness to participate, even when adjusting for potential confounders, suggesting that attunement to risk crosses diagnostic, gender, and ethnic categories, and is more salient to research decision-making than trust in medical research and dispositional optimism. The findings of this study may offer reassurance about the underlying decision-making processes of individuals considering participation in innovative neuroscience studies.

Transdiagnostic Phenotyping Reveals a Host of Metacognitive Deficits Implicated in Compulsivity

Recent work suggests that obsessive-compulsive disorder (OCD) patients have a breakdown in the relationship between explicit beliefs (i.e. confidence about states) and updates to behaviour. The precise computations underlying this disconnection are unclear because case-control and transdiagnostic studies yield conflicting results. Here, a large online population sample (N = 437) completed a predictive inference task previously studied in the context of OCD. We tested if confidence, and its relationship to action and environmental evidence, were specifically associated with self-reported OCD symptoms or common to an array of psychiatric phenomena. We then investigated if a transdiagnostic approach would reveal a stronger and more specific match between metacognitive deficits and clinical phenotypes. Consistent with prior case-control work, we found that decreases in action-confidence coupling were associated with OCD symptoms, but also 5/8 of the other clinical phenotypes tested (8/8 with no correction applied). This non-specific pattern was explained by a single transdiagnostic symptom dimension characterized by compulsivity that was linked to inflated confidence and several deficits in utilizing evidence to update confidence. These data highlight the importance of metacognitive deficits for our understanding of compulsivity and underscore how transdiagnostic methods may prove a more powerful alternative over studies examining single disorders.

Comparison of the Association Between Goal-Directed Planning and Self-reported Compulsivity vs Obsessive-Compulsive Disorder Diagnosis

IMPORTANCE

Dimensional definitions of transdiagnostic mental health problems have been suggested as an alternative to categorical diagnoses, having the advantage of capturing heterogeneity within diagnostic categories and similarity across them and bridging more naturally psychological and neural substrates.

OBJECTIVE

To examine whether a self-reported compulsivity dimension has a stronger association with goal-directed and related higher-order cognitive deficits compared with a diagnosis of obsessive-compulsive disorder (OCD).

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, patients with OCD and/or generalized anxiety disorder (GAD) from across the United States completed a telephone-based diagnostic interview by a trained rater, internet-based cognitive testing, and self-reported clinical assessments from October 8, 2015, to October 1, 2017. Follow-up data were collected to test for replicability.

MAIN OUTCOMES AND MEASURES

Performance was measured on a test of goal-directed planning and cognitive flexibility (Wisconsin Card Sorting Test [WCST]) and a test of abstract reasoning. Clinical variables included DSM-5 diagnosis of OCD and GAD and 3 psychiatric symptom dimensions (general distress, compulsivity, and obsessionality) derived from a factor analysis.

RESULTS

Of 285 individuals in the analysis (mean [SD] age, 32 [12] years; age range, 18-77 years; 219 [76.8%] female), 111 had OCD; 82, GAD; and 92, OCD and GAD. A diagnosis of OCD was not associated with goal-directed performance compared with GAD at baseline (β [SE], -0.02 [0.02]; P = .18). In contrast, a compulsivity dimension was negatively associated with goal-directed performance (β [SE], -0.05 [0.02]; P = .003). Results for abstract reasoning task and WCST mirrored this pattern; the compulsivity dimension was associated with abstract reasoning (β [SE], 2.99 [0.63]; P < .001) and several indicators of WCST performance (eg, categories completed: β [SE], -0.57 [0.09]; P < .001), whereas OCD diagnosis was not (abstract reasoning: β [SE], 0.39 [0.66]; P = .56; categories completed: β [SE], -0.09 [0.10]; P = .38). Other symptom dimensions relevant to OCD, obsessionality, and general distress had no reliable association with goal-directed performance, WCST, or abstract reasoning. Obsessionality had a positive association with requiring more trials to reach the first category on the WCST at baseline (β [SE], 2.92 [1.39]; P = .04), and general distress was associated with impaired goal-directed performance at baseline (β [SE],-0.04 [0.02]; P = .01). However, unlike the key results of this study, neither survived correction for multiple comparisons or was replicated at follow-up testing.

CONCLUSIONS AND RELEVANCE

Deficits in goal-directed planning in OCD may be more strongly associated with a compulsivity dimension than with OCD diagnosis. This result may have implications for research assessing the association between brain mechanisms and clinical manifestations and for understanding the structure of mental illness.

Problematic use of the Internet is a unidimensional quasi-trait with impulsive and compulsive subtypes

BACKGROUND

Problematic use of the Internet has been highlighted as needing further study by international bodies, including the European Union and American Psychiatric Association. Knowledge regarding the optimal classification of problematic use of the Internet, subtypes, and associations with clinical disorders has been hindered by reliance on measurement instruments characterized by limited psychometric properties and external validation.

METHODS

Non-treatment seeking individuals were recruited from the community of Stellenbosch, South Africa (N = 1661), and Chicago, United States of America (N = 827). Participants completed an online version of the Internet Addiction Test, a widely used measure of problematic use of the Internet consisting of 20-items, measured on a 5-point Likert-scale. The online questions also included demographic measures, time spent engaging in different online activities, and clinical scales. The psychometric properties of the Internet Addiction Test, and potential problematic use of the Internet subtypes, were characterized using factor analysis and latent class analysis.

RESULTS

Internet Addiction Test data were optimally conceptualized as unidimensional. Latent class analysis identified two groups: those essentially free from Internet use problems, and those with problematic use of the Internet situated along a unidimensional spectrum. Internet Addiction Test scores clearly differentiated these groups, but with different optimal cut-offs at each site. In the larger Stellenbosch dataset, there was evidence for two subtypes of problematic use of the Internet that differed in severity: a lower severity "impulsive" subtype (linked with attention-deficit hyperactivity disorder), and a higher severity "compulsive" subtype (linked with obsessive-compulsive personality traits).

CONCLUSIONS

Problematic use of the Internet as measured by the Internet Addiction Test reflects a quasi-trait - a unipolar dimension in which most variance is restricted to a subset of people with problems regulating Internet use. There was no evidence for subtypes based on the type of online activities engaged in, which increased similarly with overall severity of Internet use problems. Measures of comorbid psychiatric symptoms, along with impulsivity, and compulsivity, appear valuable for differentiating clinical subtypes and could be included in the development of new instruments for assessing the presence and severity of Internet use problems.

An Evaluation of Amazon's Mechanical Turk, Its Rapid Rise, and Its Effective Use

Over the past 2 decades, many social scientists have expanded their data-collection capabilities by using various online research tools. In the 2011 article "Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data?" in Perspectives on Psychological Science, Buhrmester, Kwang, and Gosling introduced researchers to what was then considered to be a promising but nascent research platform. Since then, thousands of social scientists from seemingly every field have conducted research using the platform. Here, we reflect on the impact of Mechanical Turk on the social sciences and our article's role in its rise, provide the newest data-driven recommendations to help researchers effectively use the platform, and highlight other online research platforms worth consideration.

Overlapping dimensional phenotypes of impulsivity and compulsivity explain co-occurrence of addictive and related behaviors

OBJECTIVE

Impulsivity and compulsivity have been implicated as important transdiagnostic dimensional phenotypes with potential relevance to addiction. We aimed to develop a model that conceptualizes these constructs as overlapping dimensional phenotypes and test whether different components of this model explain the co-occurrence of addictive and related behaviors.

METHODS

A large sample of adults (N = 487) was recruited through Amazon's Mechanical Turk and completed self-report questionnaires measuring impulsivity, intolerance of uncertainty, obsessive beliefs, and the severity of 6 addictive and related behaviors. Hierarchical clustering was used to organize addictive behaviors into homogenous groups reflecting their co-occurrence. Structural equation modeling was used to evaluate fit of the hypothesized bifactor model of impulsivity and compulsivity and determine the proportion of variance explained in the co-occurrence of addictive and related behaviors by each component of the model.

RESULTS

Addictive and related behaviors clustered into 2 distinct groups: Impulse-Control Problems, consisting of harmful alcohol use, pathological gambling, and compulsive buying, and Obsessive-Compulsive-Related Problems, consisting of obsessive-compulsive symptoms, binge eating, and internet addiction. The hypothesized bifactor model of impulsivity and compulsivity provided the best empirical fit, with 3 uncorrelated factors corresponding to a general Disinhibition dimension, and specific Impulsivity and Compulsivity dimensions. These dimensional phenotypes uniquely and additively explained 39.9% and 68.7% of the total variance in Impulse-Control Problems and Obsessive-Compulsive-Related Problems.

CONCLUSION

A model of impulsivity and compulsivity that represents these constructs as overlapping dimensional phenotypes has important implications for understanding addictive and related behaviors in terms of shared etiology, comorbidity, and potential transdiagnostic treatments.

Depressive symptoms are associated with blunted reward learning in social contexts

Depression is characterized by a marked decrease in social interactions and blunted sensitivity to rewards. Surprisingly, despite the importance of social deficits in depression, non-social aspects have been disproportionally investigated. As a consequence, the cognitive mechanisms underlying atypical decision-making in social contexts in depression are poorly understood. In the present study, we investigate whether deficits in reward processing interact with the social context and how this interaction is affected by self-reported depression and anxiety symptoms in the general population. Two cohorts of subjects (discovery and replication sample: N = 50 each) took part in an experiment involving reward learning in contexts with different levels of social information (absent, partial and complete). Behavioral analyses revealed a specific detrimental effect of depressive symptoms-but not anxiety-on behavioral performance in the presence of social information, i.e. when participants were informed about the choices of another player. Model-based analyses further characterized the computational nature of this deficit as a negative audience effect, rather than a deficit in the way others' choices and rewards are integrated in decision making. To conclude, our results shed light on the cognitive and computational mechanisms underlying the interaction between social cognition, reward learning and decision-making in depressive disorders.

ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury

Mild traumatic brain injury (mTBI) can affect high-level executive functioning long after somatic symptoms resolve. We tested if simple EEG responses within an oddball paradigm could capture variance relevant to this clinical problem. The P3a and P3b components reflect bottom-up and top-down processes driving engagement with exogenous stimuli. Since these features are related to primitive decision abilities, abnormal amplitudes following mTBI may account for problems in the ability to exert executive control. Sub-acute (<2 weeks) mTBI participants (N = 38) and healthy controls (N = 24) were assessed at an initial session as well as a two-month follow-up (sessions 1 and 2). We contrasted the initial assessment to a comparison group of participants with chronic symptomatology following brain injury (N = 23). There were no group differences in P3a or P3b amplitudes. Yet in the sub-acute mTBI group, higher symptomatology on the Frontal Systems Behavior scale (FrSBe), a questionnaire validated as measuring symptomatic distress related to frontal lobe injury, correlated with lower P3a in session 1. This relationship was replicated in session 2. These findings were distinct from chronic TBI participants, who instead expressed a relationship between increased FrSBe symptoms and a lower P3b component. In the sub-acute group, P3b amplitudes in the first session correlated with the degree of symptom change between sessions 1 and 2, above and beyond demographic predictors. Controls did not show any relationship between FrSBe symptoms and P3a or P3b. These findings identify symptom-specific alterations in neural systems that vary along the time course of post-concussive symptomatology.

The Potential of Game-Based Digital Biomarkers for Modeling Mental Health

BACKGROUND

Assessment for mental health is performed by experts using interview techniques, questionnaires, and test batteries and following standardized manuals; however, there would be myriad benefits if behavioral correlates could predict mental health and be used for population screening or prevalence estimations. A variety of digital sources of data (eg, online search data and social media posts) have been previously proposed as candidates for digital biomarkers in the context of mental health. Playing games on computers, gaming consoles, or mobile devices (ie, digital gaming) has become a leading leisure activity of choice and yields rich data from a variety of sources.

OBJECTIVE

In this paper, we argue that game-based data from commercial off-the-shelf games have the potential to be used as a digital biomarker to assess and model mental health and health decline. Although there is great potential in games developed specifically for mental health assessment (eg, Sea Hero Quest), we focus on data gathered "in-the-wild" from playing commercial off-the-shelf games designed primarily for entertainment.

METHODS

We argue that the activity traces left behind by natural interactions with digital games can be modeled using computational approaches for big data. To support our argument, we present an investigation of existing data sources, a categorization of observable traits from game data, and examples of potentially useful game-based digital biomarkers derived from activity traces.

RESULTS

Our investigation reveals different types of data that are generated from play and the sources from which these data can be accessed. Based on these insights, we describe five categories of digital biomarkers that can be derived from game-based data, including behavior, cognitive performance, motor performance, social behavior, and affect. For each type of biomarker, we describe the data type, the game-based sources from which it can be derived, its importance for mental health modeling, and any existing statistical associations with mental health that have been demonstrated in prior work. We end with a discussion on the limitations and potential of data from commercial off-the-shelf games for use as a digital biomarker of mental health.

CONCLUSIONS

When people play commercial digital games, they produce significant volumes of high-resolution data that are not only related to play frequency, but also include performance data reflecting low-level cognitive and motor processing; text-based data that are indicative of the affective state; social data that reveal networks of relationships; content choice data that imply preferred genres; and contextual data that divulge where, when, and with whom the players are playing. These data provide a source for digital biomarkers that may indicate mental health. Produced by engaged human behavior, game data have the potential to be leveraged for population screening or prevalence estimations, leading to at-scale, nonintrusive assessment of mental health.

Machine learning and big data in psychiatry: toward clinical applications

Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.

Genome-wide association study identifies a novel locus associated with psychological distress in the Japanese population

Major depressive disorder (MDD) is a common and disabling psychiatric disorder. A recent mega analysis of genome-wide association studies (GWASs) identified 44 loci associated with MDD, though most of the genetic etiologies of the MDD/psychological distress remain unclear. To further understand the genetic basis of MDD/psychological distress, we conducted a GWAS in East Asia with more than 10,000 participants of Japanese ancestry who had enrolled in a direct-to-consumer genetic test. After quality control on the genotype data, 10,330 subjects with a total of 8,567,708 imputed SNPs were eligible for the analysis. The participants completed a self-administered questionnaire on their past medical history and health conditions that included the 6-item Kessler screening scale (K6 scale) for psychological distress (cut-off point of 5) and past medical history of MDD, resulting in 3981 subjects assigned to "psychologically distressed group" [cases], and the remaining 6349 subjects were assigned to the "non-psychologically distressed group" [controls]. In this GWAS, we found an association with genome-wide significance at rs6073833 (P = 7.60 × 10^-9) in 20q13.12. This is, to the best of our knowledge, the first large-scale GWAS for psychological distress using data from direct-to-consumer (DTC) genetic tests in a population of non-European-ancestry, and the present study thus detected a novel locus significantly associated with psychological distress in the Japanese population.

Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making

Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision-making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision-making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost-benefit decision-making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real-life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders.

Real-world unexpected outcomes predict city-level mood states and risk-taking behavior

Fluctuations in mood states are driven by unpredictable outcomes in daily life but also appear to drive consequential behaviors such as risk-taking. However, our understanding of the relationships between unexpected outcomes, mood, and risk-taking behavior has relied primarily upon constrained and artificial laboratory settings. Here we examine, using naturalistic datasets, how real-world unexpected outcomes predict mood state changes observable at the level of a city, in turn predicting changes in gambling behavior. By analyzing day-to-day mood language extracted from 5.2 million location-specific and public Twitter posts or 'tweets', we examine how real-world 'prediction errors'-local outcomes that deviate positively from expectations-predict day-to-day mood states observable at the level of a city. These mood states in turn predicted increased per-person lottery gambling rates, revealing how interplay between prediction errors, moods, and risky decision-making unfolds in the real world. Our results underscore how social media and naturalistic datasets can uniquely allow us to understand consequential psychological phenomena.