Appreciating methodological complexity and integrating neurobiological perspectives to advance the science of resilience

Kalisch and colleagues identify several routes to a better understanding of mechanisms underlying resilience and highlight the need to integrate findings from neuroscience and animal learning. We argue that appreciating methodological complexity and integrating neurobiological perspectives will advance the science of resilience and ultimately help improve the lives of those exposed to stress and adversit

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach

A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient’s treatment response was modelled by identifying longitudinal trajectories using the Hamilton Depression Rating Scale (HDRS-17). Then, individual items of the HDRS-17 at admission as well as individual patient characteristics were entered as predictors of response/non-response trajectories into the binary classification model (eXtremeGradient Boosting; XGBoost). The model was evaluated on a hold-out set and explained in human-interpretable form by SHapley Additive explanation (SHAP) values. The prediction model yielded a multi-class AUC = 0.80 in the hold-out set. The predictive power for the binary classification yielded an AUC = 0.83 (sensitivity = .80, specificity = .77). Most relevant predictors for non-response were insomnia symptoms, younger age, anxiety symptoms, depressed mood, being unemployed, suicidal ideation and somatic symptoms of depressive disorder. Non-responders to routine treatment for depression can be identified and screened for potential next-generation treatments. Such predictors may help personalize treatment and improve treatment response

Impact of Cannabis Use on Treatment Outcomes among Adults Receiving Cognitive-Behavioral Treatment for PTSD and Substance Use Disorders

Background: Research has demonstrated a strong link between trauma, posttraumatic stress disorder (PTSD) and substance use disorders (SUDs) in general and cannabis use disorders in particular. Yet, few studies have examined the impact of cannabis use on treatment outcomes for individuals with co-occurring PTSD and SUDs. Methods: Participants were 136 individuals who received cognitive-behavioral therapies for co-occurring PTSD and SUD. Multivariate regressions were utilized to examine the associations between baseline cannabis use and end-of-treatment outcomes. Multilevel linear growth models were fit to the data to examine the cross-lagged associations between weekly cannabis use and weekly PTSD symptom severity and primary substance use during treatment. Results: There were no significant positive nor negative associations between baseline cannabis use and end-of-treatment PTSD symptom severity and days of primary substance use. Cross-lagged models revealed that as cannabis use increased, subsequent primary substance use decreased and vice versa. Moreover, results revealed a crossover lagged effect, whereby higher cannabis use was associated with greater PTSD symptom severity early in treatment, but lower weekly PTSD symptom severity later in treatment. Conclusion: Cannabis use was not associated with adverse outcomes in end-of-treatment PTSD and primary substance use, suggesting independent pathways of change. The theoretical and clinical implications of the reciprocal associations between weekly cannabis use and subsequent PTSD and primary substance use symptoms during treatment are discussed

SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation

Each year, more than 800,000 persons die by suicide, making it a leading cause of death worldwide. Recent innovations in information and communication technology may offer new opportunities in suicide prevention in individuals, hereby potentially reducing this number. In our project, we design digital indices based on both self-reports and passive mobile sensing and test their ability to predict suicidal ideation, a major predictor for suicide, and psychiatric hospital readmission in high-risk individuals: psychiatric patients after discharge who were admitted in the context of suicidal ideation or a suicidal attempt, or expressed suicidal ideations during their intake. Specifically, two smartphone applications -one for self-reports (SIMON-SELF) and one for passive mobile sensing (SIMON-SENSE)- are installed on participants' smartphones. SIMON-SELF uses a text-based chatbot, called Simon, to guide participants along the study protocol and to ask participants questions about suicidal ideation and relevant other psychological variables five times a day. These self-report data are collected for four consecutive weeks after study participants are discharged from the hospital. SIMON-SENSE collects behavioral variables -such as physical activity, location, and social connectedness- parallel to the first application. We aim to include 100 patients over 12 months to test whether (1) implementation of the digital protocol in such a high-risk population is feasible, and (2) if suicidal ideation and psychiatric hospital readmission can be predicted using a combination of psychological indices and passive sensor information. To this end, a predictive algorithm for suicidal ideation and psychiatric hospital readmission using various learning algorithms (e.g., random forest and support vector machines) and multilevel models will be constructed. Data collected on the basis of psychological theory and digital phenotyping may, in the future and based on our results, help reach vulnerable individuals early and provide links to just-in-time and cost-effective interventions or establish prompt mental health service contact. The current effort may thus lead to saving lives and significantly reduce economic impact by decreasing inpatient treatment and days lost to inability

Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study

BACKGROUND Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. OBJECTIVE We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. METHODS We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. RESULTS Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (β=-0.68, P=.02, r$^{2}$=0.40), overall expressivity (β=-0.46, P=.10, r$^{2}$=0.27), and head movement measured as head pitch variability (β=-1.24, P=.006, r$^{2}$=0.48) and head yaw variability (β=-0.54, P=.06, r$^{2}$=0.32). CONCLUSIONS Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation

Applications of Latent Growth Mixture Modeling and allied methods to posttraumatic stress response data

Background: Scientific research into mental health outcomes following trauma is undergoing a revolution as scientists refocus their efforts to identify underlying dimensions of health and psychopathology. This effort is in stark contrast to the previous focus which was to characterize individuals based on Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic status (Insel et al., 2010). A significant unresolved issue underlying this shift is how to characterize clinically relevant populations without reliance on the categorical definitions provided by the DSM. Classifying individuals based on their pattern of stress adaptation over time holds significant promise for capturing inherent inter-individual heterogeneity as responses including chronicity, recovery, delayed onset, and resilience can only be determined longitudinally (Galatzer-Levy & Bryant, 2013) and then characterizing these patterns for future research (Depaoli, Van de Schoot, Van Loey, & Sijbrandij, 2015). Such an approach allows for the identification of phenominologically similar patterns of response to diverse extreme environmental stressors (Bonanno, Kennedy, Galatzer-Levy, Lude, & Elfstom, 2012; Galatzer-Levy & Bonanno, 2012; Galatzer-Levy, Brown, et al., 2013; Galatzer-Levy, Burton, & Bonanno, 2012) including translational animal models of stress adaptation (Galatzer-Levy, Bonanno, Bush, & LeDoux, 2013; Galatzer-Levy, Moscarello, et al., 2014). The empirical identification of heterogeneous stress response patterns can increase the identification of mechanisms (Galatzer-Levy, Steenkamp, et al., 2014), consequences (Galatzer-Levy & Bonanno, 2014), treatment effects (Galatzer-Levy, Ankri, et al., 2013), and prediction (Galatzer-Levy, Karstoft, Statnikov, & Shalev, 2014) of individual differences in response to trauma. Method: Methodological and theoretical considerations for the application of Latent Growth Mixture Modeling (LGMM) and allied methods such as Latent Class Growth Analysis (LCGA) for the identification of heterogeneous populations defined by their pattern of change over time will be presented (Van De Schoot, 2015). Common pitfalls including non-identification, over identification, and issues related to model specification will be discussed as well as the benefits of applying such methods along with the theoretical grounding of such approaches. Conclusions: LGMM and allied methods have significant potential for improving the science of stress pathology as well as our understanding of healthy adaptation (resilience)

Appreciating methodological complexity and integrating neurobiological perspectives to advance the science of resilience

Kalisch and colleagues identify several routes to a better understanding of mechanisms underlying resilience and highlight the need to integrate findings from neuroscience and animal learning. We argue that appreciating methodological complexity and integrating neurobiological perspectives will advance the science of resilience and ultimately help improve the lives of those exposed to stress and adversity