Resilience and Disaster: Flexible Adaptation in the Face of Uncertain Threat
Disasters cause sweeping damage, hardship, and loss of life. In this article, we first consider the dominant psychological approach to disasters and its narrow focus on psychopathology (e.g., posttraumatic stress disorder). We then review research on a broader approach that has identified heterogeneous, highly replicable trajectories of outcome, the most common being stable mental health or resilience. We review trajectory research for different types of disasters, including the COVID-19 pandemic. Next, we consider correlates of the resilience trajectory and note their paradoxically limited ability to predict future resilient outcomes. Research using machine learning algorithms improved prediction but has not yet illuminated the mechanism behind resilient adaptation. To that end, we propose a more direct psychological explanation for resilience based on research on the motivational and mechanistic components of regulatory flexibility. Finally, we consider how future research might leverage new computational approaches to better capture regulatory flexibility in real time.
Can Mental Health Care Become More Human by Becoming More Digital?
Over the past two decades, advances in digital technologies have begun to transform three aspects of mental health care. The use of sensors and artificial intelligence (AI) have provided new, objective measures of how we think, feel, and behave. The ease of connecting and communicating remotely has transformed the brick-and-mortar practice of mental health care into a telehealth service, increasing access and convenience for both patients and providers. And the advent of digital therapeutics, from virtual reality for treating phobias to conversational agents for delivering structured therapies, promises to alter how treatments will be delivered in the future. These digital transformations can help to solve many of the key challenges facing mental health care, including access, quality, and accountability. But digital technology introduces a new set of challenges around trust, privacy, and equity. Despite high levels of investment and promotion, there remain profound questions about efficacy and safety of digital mental health technologies. We share our experiences from the front lines creating digital innovations for mental health, with a focus on what a digital transformation of care could deliver for millions with a serious mental illness.
Machine Learning and the Digital Measurement of Psychological Health
Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.
The Heterogeneity of Posttraumatic Stress Disorder in DSM-5
Development and validation of a brief screener for posttraumatic stress disorder risk in emergency medical settings
OBJECTIVE:Predicting risk of posttraumatic stress disorder (PTSD) in the acute care setting is challenging given the pace and acute care demands in the emergency department (ED) and the infeasibility of using time-consuming assessments. Currently, no accurate brief screening for long-term PTSD risk is routinely used in the ED. One instrument widely used in the ED is the 27-item Immediate Stress Reaction Checklist (ISRC). The aim of this study was to develop a short screener using a machine learning approach and to investigate whether accurate PTSD prediction in the ED can be achieved with substantially fewer items than the IRSC. METHOD/METHODS:This prospective longitudinal cohort study examined the development and validation of a brief screening instrument in two independent samples, a model development sample (N = 253) and an external validation sample (N = 93). We used a feature selection algorithm to identify a minimal subset of features of the ISRC and tested this subset in a predictive model to investigate if we can accurately predict long-term PTSD outcomes. RESULTS:We were able to identify a reduced subset of 5 highly predictive features of the ISRC in the model development sample (AUC = 0.80), and we were able to validate those findings in the external validation sample (AUC = 0.84) to discriminate non-remitting vs. resilient trajectories. CONCLUSION/CONCLUSIONS:This study developed and validated a brief 5-item screener in the ED setting, which may help to improve the diagnostic process of PTSD in the acute care setting and help ED clinicians plan follow-up care when patients are still in contact with the healthcare system. This could reduce the burden on patients and decrease the risk of chronic PTSD.
Modern views of machine learning for precision psychiatry
In light of the National Institute of Mental Health (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 ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
Pre-trauma predictors of severe psychiatric comorbidity 5 years following traumatic experiences
BACKGROUND:A minority of persons who have traumatic experiences go on to develop post-traumatic stress disorder (PTSD), leading to interest in who is at risk for psychopathology after these experiences. Complicating this effort is the observation that post-traumatic psychopathology is heterogeneous. The goal of this nested case-control study was to identify pre-trauma predictors of severe post-traumatic psychiatric comorbidity, using data from Danish registries. METHODS:The source population for this study was the population of Denmark from 1994 through 2016. Cases had received three or more psychiatric diagnoses (across all ICD-10 categories) within 5 years of a traumatic experience (n = 20â€Š361); controls were sampled from the parent cohort using risk-set sampling (n = 81â€Š444). Analyses were repeated in samples stratified by pre-trauma psychiatric diagnoses. We used machine learning methods (classification and regression trees and random forest) to determine the important predictors of severe post-trauma psychiatric comorbidity from among hundreds of pre-trauma predictor variables spanning demographic and social variables, psychiatric and somatic diagnoses and filled medication prescriptions. RESULTS:In the full sample, pre-trauma psychiatric diagnoses (e.g. stress disorders, alcohol-related disorders, personality disorders) were the most important predictors of severe post-trauma psychiatric comorbidity. Among persons with no pre-trauma psychiatric diagnoses, demographic and social variables (e.g. marital status), type of trauma, medications used primarily to treat psychiatric symptomatology, anti-inflammatory medications and gastrointestinal distress were important to prediction. Results among persons with pre-trauma psychiatric diagnoses were consistent with the overall sample. CONCLUSIONS:This study builds on the understanding of pre-trauma factors that predict psychopathology following traumatic experiences, by examining a broad range of predictors of post-trauma psychopathology and comorbidity beyond PTSD.
Associations among civilian mild traumatic brain injury with loss of consciousness, posttraumatic stress disorder symptom trajectories, and structural brain volumetric data
Posttraumatic stress disorder (PTSD) is prevalent and associated with significant morbidity. Mild traumatic brain injury (mTBI) concurrent with psychiatric trauma may be associated with PTSD. Prior studies of PTSD-related structural brain alterations have focused on military populations. The current study examined correlations between PTSD, acute mTBI, and structural brain alterations longitudinally in civilian patients (N = 504) who experienced a recent Criterion A traumatic event. Participants who reported loss of consciousness (LOC) were characterized as having mTBI; all others were included in the control group. PTSD symptoms were assessed at enrollment and over the following year; a subset of participants (n = 89) underwent volumetric brain MRI (M = 53 days posttrauma). Classes of PTSD symptom trajectories were modeled using latent growth mixture modeling. Associations between PTSD symptom trajectories and cortical thicknesses or subcortical volumes were assessed using a moderator-based regression. mTBI with LOC during trauma was positively correlated with the likelihood of developing a chronic PTSD symptom trajectory. mTBI showed significant interactions with cortical thickness in the rostral anterior cingulate cortex (rACC) in predicting PTSD symptoms, r = .461-.463. Bilateral rACC thickness positively predicted PTSD symptoms but only among participants who endorsed LOC, p < .001. The results demonstrate positive correlations between mTBI with LOC and PTSD symptom trajectories, and findings related to mTBI with LOC and rACC thickness interactions in predicting subsequent chronic PTSD symptoms suggest the importance of further understanding the role of mTBI in the context of PTSD to inform intervention and risk stratification.
Using Danish national registry data to understand psychopathology following potentially traumatic experiences
Research on posttraumatic psychopathology has focused primarily on posttraumatic stress disorder (PTSD); other posttraumatic psychiatric diagnoses are less well documented. The present study aimed to (a) develop a methodology to derive a cohort of individuals who experienced potentially traumatic events (PTEs) from registry-based data and (b) examine the risk of psychopathology within 5 years of experiencing a PTE. Using data from Danish national registries, we created a cohort of individuals with no age restrictions (range: 0-108 years) who experienced at least one of eight possible PTEs between 1994 and 2016 (NÂ =Â 1,406,637). We calculated the 5-year incidence of nine categories of ICD-10 psychiatric disorders among this cohort and examined standardized morbidity ratios (SMRs) comparing the incidence of psychopathology in this group to the incidence in a nontraumatic stressor cohort (i.e., nonsuicide death of a relative; nÂ =Â 423,270). Stress disorders (2.5%), substance use disorders (4.1%), and depressive disorders (3.0%) were the most common diagnoses following PTEs. Overall, the SMRs for the associations between any PTE and psychopathology varied from 1.9, 95% CI [1.9, 2.0], for stress disorders to 5.2, 95% CI [5.1. 5.3], for personality disorders. All PTEs except pregnancy-related trauma were associated with all forms of psychopathology. Associations were consistent regardless of whether a stress disorder was present. Traumatic experiences have a broad impact on psychiatric health. The present findings demonstrate one approach to capturing trauma exposure in medical record registry data. Increased traumatic experience characterization across studies will help improve the field's understanding of posttraumatic psychopathology.
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 Nâ€‰=â€‰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 AUCâ€‰=â€‰0.80 in the hold-out set. The predictive power for the binary classification yielded an AUCâ€‰=â€‰0.83 (sensitivityâ€‰=â€‰.80, specificityâ€‰=â€‰.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.