Recurrent Neural Networks in Mobile Sampling and Intervention

A control theoretic approach to evaluate and inform ecological momentary interventions

OBJECTIVES

Ecological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual's mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA.

METHODS

We analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions.

RESULTS

We identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings.

CONCLUSIONS

Combined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Psychiatric disorders are complex and influenced by both genetic and environmental factors. However, studying the full spectrum of these disorders is hindered by practical limitations on measuring human behavior. This highlights the need for novel technologies that can measure behavioral changes at an intermediate level between diagnosis and genotype. Wearable devices are a promising tool in precision medicine, since they can record physiological measurements over time in response to environmental stimuli and do so at low cost and minimal invasiveness. Here we analyzed wearable and genetic data from a cohort of the Adolescent Brain Cognitive Development study. We generated >250 wearable-derived features and used them as intermediate phenotypes in an interpretable AI modeling framework to assign risk scores and classify adolescents with psychiatric disorders. Our model identifies key physiological processes and leverages their temporal patterns to achieve a higher performance than has been previously possible. To investigate how these physiological processes relate to the underlying genetic architecture of psychiatric disorders, we also utilized these intermediate phenotypes in univariate and multivariate GWAS. We identified a total of 29 significant genetic loci and 52 psychiatric-associated genes, including ELFN1 and ADORA3. These results show that wearable-derived continuous features enable a more precise representation of psychiatric disorders and exhibit greater detection power compared to categorical diagnostic labels. In summary, we demonstrate how consumer wearable technology can facilitate dimensional approaches in precision psychiatry and uncover etiological linkages between behavior and genetics.

Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of ecological momentary assessments that capture multiple responses in real-time at high frequency. However, ecological momentary assessment data are often multi-dimensional, correlated, and hierarchical. Mixed-effect models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The recurrent temporal restricted Boltzmann machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the HDRBM, to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world ecological momentary assessment datasets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for ecological momentary assessment studies.

Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks

INTRODUCTION

Ecological Momentary Assessment (EMA) holds promise for providing insights into daily life experiences when studying mental health phenomena. However, commonly used mixed-effects linear statistical models do not fully utilize the richness of the ultidimensional time-varying data that EMA yields. Recurrent Neural Networks (RNNs) provide an alternative data analytic method to leverage more information and potentially improve prediction, particularly for non-normally distributed outcomes.

METHODS

As part of a broader research study of suicidal thoughts and behavior in people with borderline personality disorder (BPD), eighty-four participants engaged in EMA data collection over one week, answering questions multiple times each day about suicidal ideation (SI), stressful events, coping strategy use, and affect. RNNs and mixed-effects linear regression models (MEMs) were trained and used to predict SI. Root mean squared error (RMSE), mean absolute percent error (MAPE), and a pseudo-R2 accuracy metric were used to compare SI prediction accuracy between the two modeling methods.

RESULTS

RNNs had superior accuracy metrics (full model: RMSE = 3.41, MAPE = 42 %, pseudo-R2 = 26 %) compared with MEMs (full model: RMSE = 3.84, MAPE = 56 %, pseudo-R2 = 16 %). Importantly, RNNs showed significantly more accurate prediction at higher values of SI. Additionally, RNNs predicted, with significantly higher accuracy, the SI scores of participants with depression diagnoses and of participants with higher depression scores at baseline.

CONCLUSION

In this EMA study with a moderately sized sample, RNNs were better able to learn and predict daily SI compared with mixed-effects models. RNNs should be considered as an option for EMA analysis.

Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter

BACKGROUND

Mood disorders involve a complex interplay between multifaceted internal emotional states, and complex external inputs. Dynamical systems theory suggests that this interplay between aspects of moods and environmental stimuli may hence determine key psychopathological features of mood disorders, including the stability of mood states, the response to external inputs, how controllable mood states are, and what interventions are most likely to be effective. However, a comprehensive computational approach to all these aspects has not yet been undertaken.

METHODS

Here, we argue that the combination of ecological momentary assessments (EMA) with a well-established dynamical systems framework-the humble Kalman filter-enables a comprehensive account of all these aspects. We first introduce the key features of the Kalman filter and optimal control theory and their relationship to aspects of psychopathology. We then examine the psychometric and inferential properties of combining EMA data with Kalman filtering across realistic scenarios. Finally, we apply the Kalman filter to a series of EMA datasets comprising over 700 participants with and without symptoms of depression.

RESULTS

The results show a naive Kalman filter approach performs favourably compared to the standard vector autoregressive approach frequently employed, capturing key aspects of the data better. Furthermore, it suggests that the depressed state involves alterations to interactions between moods; alterations to how moods responds to external inputs; and as a result an alteration in how controllable mood states are. We replicate these findings qualitatively across datasets and explore an extension to optimal control theory to guide therapeutic interventions.

CONCLUSIONS

Mood dynamics are richly and profoundly altered in depressed states. The humble Kalman filter is a well-established, rich framework to characterise mood dynamics. Its application to EMA data is valid; straightforward; and likely to result in substantial novel insights both into mechanisms and treatments.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review

Importance

Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry.

Observations

Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions.

Conclusions and Relevance

Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Transdiagnostic Ecological Momentary Intervention for Improving Self-Esteem in Youth Exposed to Childhood Adversity: The SELFIE Randomized Clinical Trial

Importance

Targeting low self-esteem in youth exposed to childhood adversity is a promising strategy for preventing adult mental disorders. Ecological momentary interventions (EMIs) allow for the delivery of youth-friendly, adaptive interventions for improving self-esteem, but robust trial-based evidence is pending.

Objective

To examine the efficacy of SELFIE, a novel transdiagnostic, blended EMI for improving self-esteem plus care as usual (CAU) compared with CAU only.

Design, Setting, and Participants

This was a 2-arm, parallel-group, assessor-blinded, randomized clinical trial conducted from December 2018 to December 2022. The study took place at Dutch secondary mental health services and within the general population and included youth (aged 12-26 years) with low self-esteem (Rosenberg Self-Esteem Scale [RSES] <26) exposed to childhood adversity.

Interventions

A novel blended EMI (3 face-to-face sessions, email contacts, app-based, adaptive EMI) plus CAU or CAU only.

Main Outcomes and Measures

The primary outcome was RSES self-esteem at postintervention and 6-month follow-up. Secondary outcomes included positive and negative self-esteem, schematic self-beliefs, momentary self-esteem and affect, general psychopathology, quality of life, observer-rated symptoms, and functioning.

Results

A total of 174 participants (mean [SD] age, 20.7 [3.1] years; 154 female [89%]) were included in the intention-to-treat sample, who were primarily exposed to childhood emotional abuse or neglect, verbal or indirect bullying, and/or parental conflict. At postintervention, 153 participants (87.9%) and, at follow-up, 140 participants (80.5%), provided primary outcome data. RSES self-esteem was, on average, higher in the experimental condition (blended EMI + CAU) than in the control condition (CAU) across both postintervention and follow-up as a primary outcome (B = 2.32; 95% CI, 1.14-3.50; P < .001; Cohen d-type effect size [hereafter, Cohen d] = 0.54). Small to moderate effect sizes were observed suggestive of beneficial effects on positive (B = 3.85; 95% CI, 1.83-5.88; P < .001; Cohen d = 0.53) and negative (B = -3.78; 95% CI, -6.59 to -0.98; P = .008; Cohen d = -0.38) self-esteem, positive (B = 1.58; 95% CI, 0.41-2.75; P = .008; Cohen d = 0.38) and negative (B = -1.71; 95% CI, -2.93 to -0.48; P = .006; Cohen d = -0.39) schematic self-beliefs, momentary self-esteem (B = 0.29; 95% CI, 0.01-0.57; P = .04; Cohen d = 0.24), momentary positive affect (B = 0.23; 95% CI, 0.01-0.45; P = .04; Cohen d = 0.20), momentary negative affect (B = -0.33; 95% CI, -0.59 to -0.03, P = .01, Cohen d = -0.27), general psychopathology (B = -17.62; 95% CI, -33.03 to -2.21; P = .03; Cohen d = -0.34), and quality of life (B = 1.16; 95% CI, 0.18-2.13; P = .02; Cohen d = 0.33) across postintervention and follow-up. No beneficial effects on symptoms and functioning were observed.

Conclusions and Relevance

A transdiagnostic, blended EMI demonstrated efficacy on the primary outcome of self-esteem and signaled beneficial effects on several secondary outcomes. Further work should focus on implementing this novel EMI in routine public mental health provision.

Trial Registration

Dutch Trial Register Identifier:NL7129(NTR7475).

Temporal Generative Models for Learning Heterogeneous Group Dynamics of Ecological Momentary Data

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of Ecological Momentary Assessments (EMAs) that capture multiple responses in real-time at high frequency. However, EMA data is often multi-dimensional, correlated, and hierarchical. Mixed-effects models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The Recurrent Temporal Restricted Boltzmann Machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the Heterogeneous-Dynamics Restricted Boltzmann Machine (HDRBM), to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world EMA data sets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for EMA studies.

[Development, Evaluation and Implementation of Digital Health Interventions (Part 1) - Discussion Paper of the Digital Health Working Group of the German Network for Health Services Research (DNVF)]

The development and application of digital interventions in health-related topics are gaining momentum in health service research. Digital interventions are often complex and need to be evaluated and implemented in complex settings. Due to their characteristics, this poses methodological challenges for health services research that have to be identified and addressed. Hence, the Working Group on Digital Health of the German Network for Health Services Research (DNVF) has prepared a discussion paper. This paper discusses methodological, practical and theoretical challenges associated with the development and evaluation of digital interventions from the perspective of health services research. Possible solutions are suggested and future research needs to address these methodological challenges are identified.

Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field

Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data.In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems.In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings.Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.

Artificial intelligence-informed mobile mental health apps for young people: a mixed-methods approach on users' and stakeholders' perspectives

BACKGROUND

Novel approaches in mobile mental health (mHealth) apps that make use of Artificial Intelligence (AI), Ecological Momentary Assessments, and Ecological Momentary Interventions have the potential to support young people in the achievement of mental health and wellbeing goals. However, little is known on the perspectives of young people and mental health experts on this rapidly advancing technology. This study aims to investigate the subjective needs, attitudes, and preferences of key stakeholders towards an AI-informed mHealth app, including young people and experts on mHealth promotion and prevention in youth.

METHODS

We used a convergent parallel mixed-method study design. Two semi-structured online focus groups (n = 8) and expert interviews (n = 5) to explore users and stakeholders perspectives were conducted. Furthermore a representative online survey was completed by young people (n = 666) to investigate attitudes, current use and preferences towards apps for mental health promotion and prevention.

RESULTS

Survey results show that more than two-thirds of young people have experience with mHealth apps, and 60% make regular use of 1-2 apps. A minority (17%) reported to feel negative about the application of AI in general, and 19% were negative about the embedding of AI in mHealth apps. This is in line with qualitative findings, where young people displayed rather positive attitudes towards AI and its integration into mHealth apps. Participants reported pragmatic attitudes towards data sharing and safety practices, implying openness to share data if it adds value for users and if the data request is not too intimate, however demanded transparency of data usage and control over personalization. Experts perceived AI-informed mHealth apps as a complementary solution to on-site delivered interventions in future health promotion among young people. Experts emphasized opportunities in regard with low-threshold access through the use of smartphones, and the chance to reach young people in risk situations.

CONCLUSIONS

The findings of this exploratory study highlight the importance of further participatory development of training components prior to implementation of a digital mHealth training in routine practice of mental health promotion and prevention. Our results may help to guide developments based on stakeholders' first recommendations for an AI-informed mHealth app.

Advancing translational research in neuroscience through multi-task learning

Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called "multi-task learning" (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.

Analysis of the Influence of Innovative Teaching Management Mode in Universities on Students' One-to-One Training and Psychotherapy

Objective

This study expects to investigate and verify the intervention effect of the university's innovative teaching management model on college students' resilience by implementing the university's innovative teaching management model for college students with low psychological resilience.

Method

The scientific scale is used to investigate the current level of college students' psychological resilience, and the development characteristics of college students' psychological resilience are obtained through statistical analysis. Based on the theoretical analysis of the application of psychological resilience intervention, combined with the theory of one-to-one tutoring and operational techniques, the university's innovative teaching management mode scheme is designed. The design adopts a quasi-experimental pre-experimental test to investigate and explore the intervention effect system of the university's innovative teaching management mode on college students' psychological resilience. In the intervention, each unit of activity is carried out in strict accordance with the established plan, and records and reflections are made at the end. One-to-one and face-to-face qualitative interviews were conducted with the subjects, and qualitative data were collected for qualitative analysis. Results/Discussion. Compared with the control group that did not receive the intervention, the psychological resilience of the subjects in the experimental group was significantly improved after receiving the intervention of the university's innovative teaching management model. The university's innovative teaching management model has a good intervention effect on the resilience of college students. The university's innovative teaching management model scheme compiled in this study integrates a variety of psychotherapy methods and combines one-to-one psychological counseling frameworks and techniques. It is an effective and easy-to-implement intervention scheme for college students' psychological resilience intervention.

A Novel Deep Learning Model for Analyzing Psychological Stress in College Students

Psychological stress refers to the load or oppression that people’s thoughts, feelings, and other inner processes bear, as well as the emotional shifts brought on by the school, work, society, everyday life, interpersonal connections, and other things. It can trigger people’s worry and other negative feelings, making them mentally dejected and frustrated, as well as raise people’s spirits to cheer up and meet stimuli and difficulties. College students are in their 20s, and they are energetic, have extreme mood swings, and are prone to mental problems. As a distinct group in the social development trend, college students are influenced by the learning and growth environment, and their understanding of the world, values, and outlook on life is maintained at the theoretical level, lacking practical thinking and experience, making it difficult to adapt in a short period of time. Excessive psychological strain is caused by new events and new contradicting conditions, which interfere with normal living and learning. This study employs a deep learning model to test and assess the psychological stress in college students, with the goal of addressing the varied psychological stresses that college students are prone to. The deep learning model employed in this paper is based on the classic ResNet50 network model, which compresses its network structure, lowering the computational cost of ResNet50 network model training and increasing the network’s efficiency. To boost processing performance and save storage and computational resources, we trained a network with few parameters, a small model, and high precision. The findings of the investigation can help college officials prevent and recognize problems in students early on. It actively builds a good home-school cooperation mechanism and enhances the students’ ability to cope with and solve stress through enhancing the students’ behavioral experience, so that students can form a good psychological stress coping thinking and behavior, while attaching importance to the cultivation of college students’ psychological quality.

Effects of a Novel, Transdiagnostic, Hybrid Ecological Momentary Intervention for Improving Resilience in Youth (EMIcompass): Protocol for an Exploratory Randomized Controlled Trial

BACKGROUND

Most mental disorders first emerge in youth and, in their early stages, surface as subthreshold expressions of symptoms comprising a transdiagnostic phenotype of psychosis, mania, depression, and anxiety. Elevated stress reactivity is one of the most widely studied mechanisms underlying psychotic and affective mental health problems. Thus, targeting stress reactivity in youth is a promising indicated and translational preventive strategy for adverse mental health outcomes that could develop later in life and for improving resilience. Compassion-focused interventions offer a wide range of innovative therapeutic techniques that are particularly amenable to being implemented as ecological momentary interventions (EMIs), a specific type of mobile health intervention, to enable youth to access interventions in a given moment and context in daily life. This approach may bridge the current gap in youth mental health care.

OBJECTIVE

This study aims to investigate the clinical feasibility, candidate underlying mechanisms, and initial signals of the efficacy of a novel, transdiagnostic, hybrid EMI for improving resilience to stress in youth-EMIcompass.

METHODS

In an exploratory randomized controlled trial, youth aged between 14 and 25 years with current distress, a broad Clinical High At-Risk Mental State, or the first episode of a severe mental disorder will be randomly allocated to the EMIcompass intervention (ie, EMI plus face-to-face training sessions) in addition to treatment as usual or a control condition of treatment as usual only. Primary (stress reactivity) and secondary candidate mechanisms (resilience, interpersonal sensitivity, threat anticipation, negative affective appraisals, and momentary physiological markers of stress reactivity), as well as primary (psychological distress) and secondary outcomes (primary psychiatric symptoms and general psychopathology), will be assessed at baseline, postintervention, and at the 4-week follow-up.

RESULTS

The first enrollment was in August 2019, and as of May 2021, enrollment and randomization was completed (N=92). We expect data collection to be completed by August 2021.

CONCLUSIONS

This study is the first to establish feasibility, evidence on underlying mechanisms, and preliminary signals of the efficacy of a compassion-focused EMI in youth. If successful, a confirmatory randomized controlled trial will be warranted. Overall, our approach has the potential to significantly advance preventive interventions in youth mental health provision.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00017265; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017265.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/27462.

Le phénotypage digital pour une pratique clinique en santé mentale mieux informée

Objectifs Cette revue trouve sa motivation dans l’observation que la prise de décision clinique en santé mentale est limitée par la nature des mesures typiquement obtenues lors de l’entretien clinique et la difficulté des cliniciens à produire des prédictions justes sur les états mentaux futurs des patients. L’objectif est de présenter un survol représentatif du potentiel du phénotypage digital couplé à l’apprentissage automatique pour répondre à cette limitation, tout en en soulignant les faiblesses actuelles.

Méthode Au travers d’une revue narrative de la littérature non systématique, nous identifions les avancées technologiques qui permettent de quantifier, instant après instant et dans le milieu de vie naturel, le phénotype humain au moyen du téléphone intelligent dans diverses populations psychiatriques. Des travaux pertinents sont également sélectionnés afin de déterminer l’utilité et les limitations de l’apprentissage automatique pour guider les prédictions et la prise de décision clinique. Finalement, la littérature est explorée pour évaluer les barrières actuelles à l’adoption de tels outils.

Résultats Bien qu’émergeant d’un champ de recherche récent, de très nombreux travaux soulignent déjà la valeur des mesures extraites des senseurs du téléphone intelligent pour caractériser le phénotype humain dans les sphères comportementale, cognitive, émotionnelle et sociale, toutes étant affectées par les troubles mentaux. L’apprentissage automatique permet d’utiles et justes prédictions cliniques basées sur ces mesures, mais souffre d’un manque d’interprétabilité qui freinera son emploi prochain dans la pratique clinique. Du reste, plusieurs barrières identifiées tant du côté du patient que du clinicien freinent actuellement l’adoption de ce type d’outils de suivi et d’aide à la décision clinique.

Conclusion Le phénotypage digital couplé à l’apprentissage automatique apparaît fort prometteur pour améliorer la pratique clinique en santé mentale. La jeunesse de ces nouveaux outils technologiques requiert cependant un nécessaire processus de maturation qui devra être encadré par les différents acteurs concernés pour que ces promesses puissent être pleinement réalisées.

Ambulatory assessment for precision psychiatry: Foundations, current developments and future avenues

Precision psychiatry stands to benefit from the latest digital technologies for assessment and analyses to tailor treatment towards individuals. Insights into dynamic psychological processes as they unfold in humans' everyday life can critically add value in understanding symptomatology and environmental stressors to provide individualized treatment where and when needed. Towards this goal, ambulatory assessment encompasses methodological approaches to investigate behavioral, physiological, and biological processes in humans' everyday life. It combines repeated assessments of symptomatology over time, e.g., via Ecological Momentary Assessment (e.g., smartphone-diaries), with monitoring of physical behavior, environmental characteristics (such as geolocations, social interactions) and physiological function via sensors, e.g., mobile accelerometers, global-positioning-systems, and electrocardiography. In this review, we expand on promises of ambulatory assessment in the investigation of mental states (e.g., real-life, dynamical and contextual perspective), on chances for precision psychiatry such as the prediction of courses of psychiatric disorders, detection of tipping points and critical windows of relapse, and treatment effects as exemplified by ongoing projects, and on future avenues of how ambulatory interventions can benefit personalized care for psychiatric patients (e.g., through real-time feedback in everyday life). Ambulatory assessment is a key contributor to precision psychiatry, opening up promising avenues in research, diagnoses, prevention and treatment.

Deep learning for small and big data in psychiatry

Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored treatments. To this end, it appears that the analysis of 'small' experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather 'small' samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.

Urban remediation: a new recovery-oriented strategy to manage urban stress after first-episode psychosis

PURPOSE

Urban living is a major risk factor for psychosis. Considering worldwide increasing rates of urbanization, new approaches are needed to enhance patients' wellbeing in cities. Recent data suggest that once psychosis has emerged, patients struggle to adapt to urban milieu and that they lose access to city centers, which contributes to isolation and reduced social contacts. While it is acknowledged that there are promising initiatives to improve mental health in cities, concrete therapeutic strategies to help patients with psychosis to better handle urban stress are lacking. We believe that we should no longer wait to develop and test new therapeutic approaches.

METHOD

In this review, we first focus on the role of urban planning, policies, and design, and second on possible novel therapeutic strategies at the individual level. We review how patients with psychosis may experience stress in the urban environment. We then review and describe a set of possible strategies, which could be proposed to patients with the first-episode psychosis.

RESULTS

We propose to group these strategies under the umbrella term of 'urban remediation' and discuss how this novel approach could help patients to recover from their first psychotic episode.

CONCLUSION

The concepts developed in this paper are speculative and a lot of work remains to be done before it can be usefully proposed to patients. However, considering the high prevalence of social withdrawal and its detrimental impact on the recovery process, we strongly believe that researchers should invest this new domain to help patients regain access to city centers.

Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI

A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical systems as well as on experimental fMRI time series, and demonstrate that the learnt dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.

Construction of medical equipment-based doctor health monitoring system

The health status of doctors has been overlooked by the society and even the doctors themselves, especially those doctors who work long hours. Their attention is always on patients, so they are more likely to ignore their own health problems. Therefore, in this paper, we propose a medical equipment-based doctor health monitoring system (hereinafter referred to as Doc-care). Doc-care can be used as a private health manager for doctors, and doctors can monitor their health indicators in real time while using medical equipment to aid diagnosis and treatment. When the doctor's health status is neglected, Doc-care can protect the doctor's health; combining with the convolutional neural network method to detect and grade the doctor's health indicators, to assess the doctor's real-time health status. After referring to the doctor's past health data in the cloud server, giving appropriate advice and predictions about the doctor's health status.