Smartphones and the Neuroscience of Mental Health

Ethological computational psychiatry: Challenges and opportunities

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.

Emotions under control? Better cognitive control is associated with reduced negative emotionality but increased negative emotional reactivity within individuals

Associations between impaired cognitive control and maladaptive emotion regulation have been extensively studied between individuals. However, it remains unclear if this relationship holds within individuals. In this study, we tested the assumption that momentary within-person fluctuation in cognitive control (working memory updating and response inhibition) is associated with emotional reactivity in everyday life. We conducted an experience sampling study (eight two-hourly prompts daily) where participants repeatedly performed short 2-back and Go/no-go tasks in daily life. We assessed negative and positive affective states, and unpleasantness of a recent event to capture emotional reactivity. We analyzed two overlapping samples: a Go/no-go and a 2-back dataset (N = 161/158). Our results showed that better momentary working memory updating was associated with decreased negative affect if the recent event was on average unpleasant for the given individual. However, better-than-average working memory updating in interaction with higher event-unpleasantness predicted higher negative affect levels (i.e., higher negative emotional reactivity). These findings may challenge the account of better cognitive control being universally related to adaptive emotion regulation. Although it is unlikely that emotional reactivity boosts working memory, future studies should establish the direction of causality.

The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes

In many important real-world decision domains, such as finance, the environment, and health, behavior is strongly influenced by experience. Renewed interest in studying this influence led to important advancements in the understanding of these decisions from experience (DfE) in the last 20 years. Building on this literature, we suggest ways the standard experimental design should be extended to better approach important real-world DfE. These extensions include, for example, introducing more complex choice situations, delaying feedback, and including social interactions. When acting upon experiences in these richer and more complicated environments, extensive cognitive processes go into making a decision. Therefore, we argue for integrating cognitive processes more explicitly into experimental research in DfE. These cognitive processes include attention to and perception of numeric and nonnumeric experiences, the influence of episodic and semantic memory, and the mental models involved in learning processes. Understanding these basic cognitive processes can advance the modeling, understanding and prediction of DfE in the laboratory and in the real world. We highlight the potential of experimental research in DfE for theory integration across the behavioral, decision, and cognitive sciences. Furthermore, this research could lead to new methodology that better informs decision-making and policy interventions.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling

Introduction

In recent years, the widespread shift from on-site to remote work has led to a decline in employees' mental health. Consequently, this transition to remote work poses several challenges for both employees and employers. To address these challenges, there is an urgent need for techniques to detect declining mental health in employees' daily lives. Emotion-based health assessment, which examines emotional diversity (emodiversity) experienced in daily life, is a possible solution. However, the feasibility of emodiversity remains unclear, especially from the perspectives of its applicability to remote workers and countries other than Europe and the United States. This study investigated the association between subjective mental health decline and emotional factors, such as emodiversity, as well as physical conditions, in remote workers in Japan.

Method

To explore this association, we conducted a consecutive 14-day prospective observational experiment on 18 Japanese remote workers. This experiment comprised pre-and post-questionnaire surveys, physiological sensing, daytime emotion self-reports, and subjective health reports at end-of-day. In daytime emotion self-reports, we introduced smartphone-based experience sampling (also known as ecological momentary assessment), which is suitable for collecting context-dependent self-reports precisely in a recall bias-less manner. For 17 eligible participants (mean ± SD, 39.1 ± 9.1 years), we evaluated whether and how the psycho-physical characteristics, including emodiversity, changed on subjective mental health-declined experimental days after analyzing descriptive statistics.

Results

Approximately half of the experimental days (46.3 ± 18.9%) were conducted under remote work conditions. Our analysis showed that physical and emotional indices significantly decreased on mental health-declined days. Especially on high anxiety and depressive days, we found that emodiversity indicators significantly decreased (global emodiversity on anxiety conditions, 0.409 ± 0.173 vs. 0.366 ± 0.143, p = 0.041), and positive emotional experiences were significantly suppressed (61.5 ± 7.7 vs. 55.5 ± 6.4, p < 0.001).

Discussion

Our results indicated that the concept of emodiversity can be applicable even to Japanese remote workers, whose cultural background differs from that of individuals in Europe and the United States. Emodiversity showed significant associations with emotion dysregulation-related mental health deterioration, suggesting the potential of emodiversity as useful indicators in managing such mental health deterioration among remote workers.

Development and Validation of a Smartphone-Based App for the Longitudinal Assessment of Anxiety in Daily Life

Current methods to assess human anxiety often ignore that anxiety is a dynamic process and have limitations such as high recall bias and low generalizability to real life. Smartphone apps using ecological momentary assessment (EMA) may overcome such limitations. We developed a smartphone app for the longitudinal evaluation of anxiety symptoms using EMA. We assessed the feasibility (retention and compliance) and psychometric properties (reliability and validity) of the app over 6 months in a sample of 99 participants with different levels of anxiety. The EMA-based smartphone app was highly feasible. It showed excellent within-person and between-person reliability, high convergent and moderate discriminant validity, and significant incremental validity. Assessing anxiety longitudinally using a smartphone and following EMA principles is feasible and can be reliable and valid. Studies combining EMA-based anxiety longitudinal assessments with other assessment methods deserve further research and may offer novel insights into human anxiety.

Mobile Solutions for Clinical Surveillance and Evaluation in Infancy-General Movement Apps

The Prechtl General Movements Assessment (GMA) has become a clinician and researcher toolbox for evaluating neurodevelopment in early infancy. Given that it involves the observation of infant movements from video recordings, utilising smartphone applications to obtain these recordings seems like the natural progression for the field. In this review, we look back on the development of apps for acquiring general movement videos, describe the application and research studies of available apps, and discuss future directions of mobile solutions and their usability in research and clinical practice. We emphasise the importance of understanding the background that has led to these developments while introducing new technologies, including the barriers and facilitators along the pathway. The GMApp and Baby Moves apps were the first ones developed to increase accessibility of the GMA, with two further apps, NeuroMotion and InMotion, designed since. The Baby Moves app has been applied most frequently. For the mobile future of GMA, we advocate collaboration to boost the field's progression and to reduce research waste. We propose future collaborative solutions, including standardisation of cross-site data collection, adaptation to local context and privacy laws, employment of user feedback, and sustainable IT structures enabling continuous software updating.

Towards a neurocomputational account of social controllability: From models to mental health

Controllability, or the influence one has over their surroundings, is crucial for decision-making and mental health. Traditionally, controllability is operationalized in sensorimotor terms as one's ability to exercise their actions to achieve an intended outcome (also termed "agency"). However, recent social neuroscience research suggests that humans also assess if and how they can exert influence over other people (i.e., their actions, outcomes, beliefs) to achieve desired outcomes ("social controllability"). In this review, we will synthesize empirical findings and neurocomputational frameworks related to social controllability. We first introduce the concepts of contextual and perceived controllability and their respective relevance for decision-making. Then, we outline neurocomputational frameworks that can be used to model social controllability, with a focus on behavioral economic paradigms and reinforcement learning approaches. Finally, we discuss the implications of social controllability for computational psychiatry research, using delusion and obsession-compulsion as examples. Taken together, we propose that social controllability could be a key area of investigation in future social neuroscience and computational psychiatry research.

The development of a digital story-retell elicitation and analysis tool through citizen science data collection, software development and machine learning

Background

In order to leverage the potential benefits of technology to speech and language therapy language assessment processes, large samples of naturalistic language data must be collected and analysed. These samples enable the development and testing of novel software applications with data relevant to their intended clinical application. However, the collection and analysis of such data can be costly and time-consuming. This paper describes the development of a novel application designed to elicit and analyse young children's story retell narratives to provide metrics regarding the child's use of grammatical structures (micro-structure) and story grammar (macro-structure elements). Key aspects for development were (1) methods to collect story retells, ensure accurate transcription and segmentation of utterances; (2) testing the reliability of the application to analyse micro-structure elements in children's story retells and (3) development of an algorithm to analyse narrative macro-structure elements.

Methods

A co-design process was used to design an app which would be used to gather story retell samples from children using mobile technology. A citizen science approach using mainstream marketing via online channels, the media and billboard ads was used to encourage participation from children across the United Kingdom. A stratified sampling framework was used to ensure a representative sample was obtained across age, gender and five bands of socio-economic disadvantage using partial postcodes and the relevant indices of deprivation. Trained Research Associates (RA) completed transcription and micro and macro-structure analysis of the language samples. Methods to improve transcriptions produced by automated speech recognition were developed to enable reliable analysis. RA micro-structure analyses were compared to those generated by the digital application to test its reliability using intra-class correlation (ICC). RA macro-structure analyses were used to train an algorithm to produce macro-structure metrics. Finally, results from the macro-structure algorithm were compared against a subset of RA macro-structure analyses not used in training to test its reliability using ICC.

Results

A total of 4,517 profiles were made in the app used in data collection and from these participants a final set of 599 were drawn which fulfilled the stratified sampling criteria. The story retells ranged from 35.66 s to 251.4 s in length and had word counts ranging from 37 to 496, with a mean of 148.29 words. ICC between the RA and application micro-structure analyses ranged from 0.213 to 1.0 with 41 out of a total of 44 comparisons reaching 'good' (0.70-0.90) or 'excellent' (>0.90) levels of reliability. ICC between the RA and application macro-structure features were completed for 85 samples not used in training the algorithm. ICC ranged from 0.5577 to 0.939 with 5 out of 7 metrics being 'good' or better.

Conclusion

Work to date has demonstrated the potential of semi-automated transcription and linguistic analyses to provide reliable, detailed and informative narrative language analysis for young children and for the use of citizen science based approaches using mobile technologies to collect representative and informative research data. Clinical evaluation of this new app is ongoing, so we do not yet have data documenting its developmental or clinical sensitivity and specificity.

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.

Risk taking for potential losses but not gains increases with time of day

Humans exhibit distinct risk preferences when facing choices involving potential gains and losses. These preferences are believed to be subject to neuromodulatory influence, particularly from dopamine and serotonin. As neuromodulators manifest circadian rhythms, this suggests decision making under risk might be affected by time of day. Here, in a large subject sample collected using a smartphone application, we found that risky options with potential losses were increasingly chosen over the course of the day. We observed this result in both a within-subjects design (N = 2599) comparing risky options chosen earlier and later in the day in the same individuals, and in a between-subjects design (N = 26,720) showing our effect generalizes across ages and genders. Using computational modelling, we show this diurnal change in risk preference reflects a decrease in sensitivity to increasing losses, but no change was observed in the relative impacts of gains and losses on choice (i.e., loss aversion). Thus, our findings reveal a striking diurnal modulation in human decision making, a pattern with potential importance for real-life decisions that include voting, medical decisions, and financial investments.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Stronger coupling of emotional instability with reward processing in borderline personality disorder is predicted by schema modes

BACKGROUND

Mood instability and risk-taking are hallmarks of borderline personality disorder (BPD). Schema modes are combinations of self-reflective evaluations, negative emotional states, and destructive coping strategies common in BPD. When activated, they can push patients with BPD into emotional turmoil and a dissociative state of mind. Our knowledge of the underlying neurocognitive mechanisms driving these changes is incomplete. We hypothesized that in patients with BPD, affective instability is more influenced by reward expectation, outcomes, and reward prediction errors (RPEs) during risky decision-making than in healthy controls. Additionally, we expected that these alterations would be related to schema modes.

METHODS

Thirty-two patients with BPD and thirty-one healthy controls were recruited. We used an established behavioral paradigm to measure mood fluctuations during risky decision-making. The impact of expectations and RPEs on momentary mood was quantified by a computational model, and its parameters were estimated with hierarchical Bayesian analysis. Model parameters were compared using High-Density Intervals.

RESULTS

We found that model parameters capturing the influence of RPE and Certain Rewards on mood were significantly higher in patients with BPD than in controls. These model parameters correlated significantly with schema modes, but not with depression severity.

CONCLUSIONS

BPD is coupled with altered associations between mood fluctuation and reward processing under uncertainty. Our findings seem to be BPD-specific, as they stand in contrast with the correlates of depressive symptoms. Future studies should establish the clinical utility of these alterations, such as predicting or assessing therapeutic response in BPD.

Mood and implicit confidence independently fluctuate at different time scales

Mood is an important ingredient of decision-making. Human beings are immersed into a sea of ​​emotions where episodes of high mood alternate with episodes of low mood. While changes in mood are well characterized, little is known about how these fluctuations interact with metacognition, and in particular with confidence about our decisions. We evaluated how implicit measurements of confidence are related with mood states of human participants through two online longitudinal experiments involving mood self-reports and visual discrimination decision-making tasks. Implicit confidence was assessed on each session by monitoring the proportion of opt-out trials when an opt-out option was available, as well as the median reaction time on standard correct trials as a secondary proxy of confidence. We first report a strong coupling between mood, stress, food enjoyment, and quality of sleep reported by participants in the same session. Second, we confirmed that the proportion of opt-out responses as well as reaction times in non-opt-out trials provided reliable indices of confidence in each session. We introduce a normative measure of overconfidence based on the pattern of opt-out selection and the signal-detection-theory framework. Finally and crucially, we found that mood, sleep quality, food enjoyment, and stress level are not consistently coupled with these implicit confidence markers, but rather they fluctuate at different time scales: mood-related states display faster fluctuations (over one day or half-a-day) than confidence level (two-and-a-half days). Therefore, our findings suggest that spontaneous fluctuations of mood and confidence in decision making are independent in the healthy adult population.

Computational models of subjective feelings in psychiatry

Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between lab-based behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.

Explaining World-Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest

Navigation ability varies widely across humans. Prior studies have reported that being younger and a male has an advantage for navigation ability. However, these studies have generally involved small numbers of participants from a handful of western countries. Here, we review findings from our project Sea Hero Quest, which used a video game for mobile and tablet devices to test 3.9 million people on their navigation ability, sampling across every nation-state and from 18 to 99 years of age. Results revealed that the task has good ecological validity and across all countries sufficiently sampled (N = 63), age is linked to a near-linear decline in navigation ability from the early 20s. All countries showed a male advantage, but this varied considerably and could be partly predicted by gender inequality. We found that those who reported growing up in a city were on average worse at navigating than those who grew up outside cities and that navigation performance helped identify those at greater genetic risk of Alzheimer's disease. We discuss the advantages and challenges of using a mobile app to study cognition and the future avenues for understanding individual differences in navigation ability arising from this research.

A practical guide for studying human behavior in the lab

In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.

Individualized prediction of consummatory anhedonia from functional connectome in major depressive disorder

BACKGROUND

Anhedonia is a key symptom of major depressive disorder (MDD) and other psychiatric diseases. The neural basis of anhedonia has been widely examined, yet the interindividual variability in neuroimaging biomarkers underlying individual-specific symptom severity is not well understood.

METHODS

To establish an individualized prediction model of anhedonia, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity profiles of MDD patients.

RESULTS

The CPM can successfully and reliably predict individual consummatory but not anticipatory anhedonia. The predictive model mainly included salience network (SN), frontoparietal network (FPN), default mode network (DMN), and motor network. Importantly, subsequent computational lesion prediction and consummatory-specific model prediction revealed that connectivity of the SN with DMN and FPN is essential and specific for the prediction of consummatory anhedonia.

CONCLUSIONS

This study shows that brain functional connectivity, especially the connectivity of SN-FPN and SN-DMN, can specifically predict individualized consummatory anhedonia in MDD. These findings suggest the potential of functional connectomes for the diagnosis and prognosis of anhedonia in MDD and other disorders.

Developments in scalable strategies for detecting early markers of cognitive decline

Effective strategies for early detection of cognitive decline, if deployed on a large scale, would have individual and societal benefits. However, current detection methods are invasive or time-consuming and therefore not suitable for longitudinal monitoring of asymptomatic individuals. For example, biological markers of neuropathology associated with cognitive decline are typically collected via cerebral spinal fluid, cognitive functioning is evaluated from face-to-face assessments by experts and brain measures are obtained using expensive, non-portable equipment. Here, we describe scalable, repeatable, relatively non-invasive and comparatively inexpensive strategies for detecting the earliest markers of cognitive decline. These approaches are characterized by simple data collection protocols conducted in locations outside the laboratory: measurements are collected passively, by the participants themselves or by non-experts. The analysis of these data is, in contrast, often performed in a centralized location using sophisticated techniques. Recent developments allow neuropathology associated with potential cognitive decline to be accurately detected from peripheral blood samples. Advances in smartphone technology facilitate unobtrusive passive measurements of speech, fine motor movement and gait, that can be used to predict cognitive decline. Specific cognitive processes can be assayed using 'gamified' versions of standard laboratory cognitive tasks, which keep users engaged across multiple test sessions. High quality brain data can be regularly obtained, collected at-home by users themselves, using portable electroencephalography. Although these methods have great potential for addressing an important health challenge, there are barriers to be overcome. Technical obstacles include the need for standardization and interoperability across hardware and software. Societal challenges involve ensuring equity in access to new technologies, the cost of implementation and of any follow-up care, plus ethical issues.

The promise of a model-based psychiatry: building computational models of mental ill health

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.

Increased Habit Frequency in the Daily Lives of Patients with Acute Anorexia Nervosa

Strict eating routines and frequent rigid behavior patterns are commonly observed in patients with anorexia nervosa (AN). A recent theory proposes that while these behaviors may have been reinforced initially, they later become habitual. To date, however, research has been overly focused on eating-disorder (ED)-related habits. Over the course of seven days, we applied an ecological momentary assessment (EMA) to investigate the habit frequency and strength of ED-specific (food intake) and ED-unspecific (hygiene) habits in the daily lives of a sample of n = 57 AN and n = 57 healthy controls (HC). The results of the hierarchical models revealed that habits were significantly more likely in patients compared with HC for both categories, independently. Furthermore, a lower body mass index (BMI) was associated with increased habit frequency in AN. Our study strengthens the habit theory of AN by showing the relevance of habits beyond ED-specific behavioral domains. This also supports the development of innovative therapeutic interventions targeting habitual behavior in EDs.

Meta-analytic evidence that mindfulness training alters resting state default mode network connectivity

This meta-analysis sought to expand upon neurobiological models of mindfulness through investigation of inherent brain network connectivity outcomes, indexed via resting state functional connectivity (rsFC). We conducted a systematic review and meta-analysis of rsFC as an outcome of mindfulness training (MT) relative to control, with the hypothesis that MT would increase cross-network connectivity between nodes of the Default Mode Network (DMN), Salience Network (SN), and Frontoparietal Control Network (FPCN) as a mechanism of internally-oriented attentional control. Texts were identified from the databases: MEDLINE/PubMed, ERIC, PSYCINFO, ProQuest, Scopus, and Web of Sciences; and were screened for inclusion based on experimental/quasi-experimental trial design and use of mindfulness-based training interventions. RsFC effects were extracted from twelve studies (mindfulness n = 226; control n = 204). Voxel-based meta-analysis revealed significantly greater rsFC (MT > control) between the left middle cingulate (Hedge's g = .234, p = 0.0288, I2 = 15.87), located within the SN, and the posterior cingulate cortex, a focal hub of the DMN. Egger's test for publication bias was nonsignificant, bias = 2.17, p = 0.162. In support of our hypothesis, results suggest that MT targets internetwork (SN-DMN) connectivity implicated in the flexible control of internally-oriented attention.

Using Remotely Supervised At-Home TES for Enhancing Mental Resilience

We are in the midst of a mental health crisis with major depressive disorder being the most prevalent among mental health disorders and up to 30% of patients not responding to first-line treatments. Noninvasive Brain Stimulation (NIBS) techniques have proven to be effective in treating depression. However, there is a fundamental problem of scale. Currently, any type of NIBS treatment requires patients to repeatedly visit a clinic to receive brain stimulation by trained personnel. This is an often-insurmountable barrier to both patients and healthcare providers in terms of time and cost. In this perspective, we assess to what extent Transcranial Electrical Stimulation (TES) might be administered with remote supervision in order to address this scaling problem and enable neuroenhancement of mental resilience at home. Social, ethical, and technical challenges relating to hardware- and software-based solutions are discussed alongside the risks of stimulation under- or over-use. Solutions to provide users with a safe and transparent ongoing assessment of aptitude, tolerability, compliance, and/or misuse are proposed, including standardized training, eligibility screening, as well as compliance and side effects monitoring. Looking into the future, such neuroenhancement could be linked to prevention systems which combine home-use TES with digital sensor and mental monitoring technology to index decline in mental wellbeing and avoid relapse. Despite the described social, ethical legal, and technical challenges, the combination of remotely supervised, at-home TES setups with dedicated artificial intelligence systems could be a powerful weapon to combat the mental health crisis by bringing personalized medicine into people’s homes.

Machine Learning and Prediction in Psychological Assessment

Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.

Gauging the happiness benefit of US urban parks through Twitter

The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we analyze over 1.5 million tweets to estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year’s Day.

New principles and new paths needed for online research in mental health: Commentary on Burnette et al. (2021)

Online methods have become a powerful research tool, allowing us to conduct well-powered studies, to explore and replicate effects, and to recruit often rare and diverse samples. However, concerns about the validity and reliability of the data collected from some platforms have reached crescendo. In this issue, Burnette et al. (2021) describe how commonly employed protective measures such as captchas, response consistency requirements, and attention checks may no longer be sufficient to ensure high-quality data in survey-based studies on Amazon's Mechanical Turk. We echo and elaborate on these concerns, but believe that although imperfect, online research will continue to be incredibly important in driving progress in mental health science. Not all platforms or populations are well suited to every research question and so we posit that the future of online research will be much more varied, and in no small part supported by citizen scientists and those with lived experience. Whatever the medium, researchers cannot stand still; we must continuously reflect and adapt to technological advances, demographics, and motivational shifts of our participants. Online research is difficult but worthwhile.

Psychedelic Therapy's Transdiagnostic Effects: A Research Domain Criteria (RDoC) Perspective

Accumulating clinical evidence shows that psychedelic therapy, by synergistically combining psychopharmacology and psychological support, offers a promising transdiagnostic treatment strategy for a range of disorders with restricted and/or maladaptive habitual patterns of emotion, cognition and behavior, notably, depression (MDD), treatment resistant depression (TRD) and addiction disorders, but perhaps also anxiety disorders, obsessive-compulsive disorder (OCD), Post-Traumatic Stress Disorder (PTSD) and eating disorders. Despite the emergent transdiagnostic evidence, the specific clinical dimensions that psychedelics are efficacious for, and associated underlying neurobiological pathways, remain to be well-characterized. To this end, this review focuses on pre-clinical and clinical evidence of the acute and sustained therapeutic potential of psychedelic therapy in the context of a transdiagnostic dimensional systems framework. Focusing on the Research Domain Criteria (RDoC) as a template, we will describe the multimodal mechanisms underlying the transdiagnostic therapeutic effects of psychedelic therapy, traversing molecular, cellular and network levels. These levels will be mapped to the RDoC constructs of negative and positive valence systems, arousal regulation, social processing, cognitive and sensorimotor systems. In summarizing this literature and framing it transdiagnostically, we hope we can assist the field in moving toward a mechanistic understanding of how psychedelics work for patients and eventually toward a precise-personalized psychedelic therapy paradigm.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study

BACKGROUND

Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored.

OBJECTIVE

We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up.

METHODS

The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning-based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status.

RESULTS

Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F_1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group.

CONCLUSIONS

Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers-passive-sensing of shifts in category-based social media app usage during the lockdown-can identify individuals at risk for psychiatric sequelae.