Isaac R. Galatzer-Levy

Identification of a Common Neurobiological Substrate for Mental Illness

IMPORTANCE Psychiatric diagnoses are currently distinguished based on sets of specific symptoms. However, genetic and clinical analyses find similarities across a wide variety of diagnoses, suggesting that a common neurobiological substrate may exist across mental illness. OBJECTIVE To conduct a meta-analysis of structural neuroimaging studies across multiple psychiatric diagnoses, followed by parallel analyses of 3 large-scale healthy participant data sets to help interpret structural findings in the meta-analysis. DATA SOURCES PubMed was searched to identify voxel-based morphometry studies through July 2012 comparing psychiatric patients to healthy control individuals for the meta-analysis. The 3 parallel healthy participant data sets included resting-state functional magnetic resonance imaging, a database of activation foci across thousands of neuroimaging experiments, and a data set with structural imaging and cognitive task performance data. DATA EXTRACTION AND SYNTHESIS Studies were included in the meta-analysis if they reported voxel-based morphometry differences between patients with an Axis I diagnosis and control individuals in stereotactic coordinates across the whole brain, did not present predominantly in childhood, and had at least 10 studies contributing to that diagnosis (or across closely related diagnoses). The meta-analysis was conducted on peak voxel coordinates using an activation likelihood estimation approach. MAIN OUTCOMES AND MEASURES We tested for areas of common gray matter volume increase or decrease across Axis I diagnoses, as well as areas differing between diagnoses. Follow-up analyses on other healthy participant data sets tested connectivity related to regions arising from the meta-analysis and the relationship of gray matter volume to cognition. RESULTS Based on the voxel-based morphometry meta-analysis of 193 studies comprising 15 892 individuals across 6 diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety), we found that gray matter loss converged across diagnoses in 3 regions: the dorsal anterior cingulate, right insula, and left insula. By contrast, there were few diagnosis-specific effects, distinguishing only schizophrenia and depression from other diagnoses. In the parallel follow-up analyses of the 3 independent healthy participant data sets, we found that the common gray matter loss regions formed a tightly interconnected network during tasks and at resting and that lower gray matter in this network was associated with poor executive functioning. CONCLUSIONS AND REVELANCE We identified a concordance across psychiatric diagnoses in terms of integrity of an anterior insula/dorsal anterior cingulate-based network, which may relate to executive function deficits observed across diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology, despite likely diverse etiologies, which is currently not an explicit component of psychiatric nosology.

636,120 Ways to Have Posttraumatic Stress Disorder

In an attempt to capture the variety of symptoms that emerge following traumatic stress, the revision of posttraumatic stress disorder (PTSD) criteria in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) has expanded to include additional symptom presentations. One consequence of this expansion is that it increases the amorphous nature of the classification. Using a binomial equation to elucidate possible symptom combinations, we demonstrate that the DSM–IV criteria listed for PTSD have a high level of symptom profile heterogeneity (79,794 combinations); the changes result in an eightfold expansion in the DSM–5, to 636,120 combinations. In this article, we use the example of PTSD to discuss the limitations of DSM-based diagnostic entities for classification in research by elucidating inherent flaws that are either specific artifacts from the history of the DSM or intrinsic to the underlying logic of the DSM’s method of classification. We discuss new directions in research that can provide better information regarding both clinical and nonclinical behavioral heterogeneity in response to potentially traumatic and common stressful life events. These empirical alternatives to an a priori classification system hold promise for answering questions about why diversity occurs in response to stressors.

Trajectories of resilience, depression, and anxiety following spinal cord injury.

PURPOSE/OBJECTIVE To investigate longitudinal trajectories of depression and anxiety symptoms following spinal cord injury (SCI) as well as the predictors of those trajectories. RESEARCH METHOD/DESIGN A longitudinal study of 233 participants assessed at 4 time points: within 6 weeks, 3 months, 1 year, and 2 years from the point of injury. Data were analyzed using latent growth mixture modeling to determine the best-fitting model of depression and anxiety trajectories. Covariates assessed during hospitalization were explored as predictors of the trajectories. RESULTS Analyses for depression and anxiety symptoms revealed 3 similar latent classes: a resilient pattern of stable low symptoms, a pattern of high symptoms followed by improvement (recovery), and delayed symptom elevations. A chronic high depression pattern also emerged but not a chronic high anxiety pattern. Analyses of predictors indicated that compared with other groups, resilient patients had fewer SCI-related quality of life problems, more challenge appraisals and fewer threat appraisals, greater acceptance and fighting spirit, and less coping through social reliance and behavioral disengagement. CONCLUSION/IMPLICATIONS Overall, the majority of SCI patients demonstrated considerable psychological resilience. Models for depression and anxiety evidenced a pattern of elevated symptoms followed by improvement and a pattern of delayed symptoms. Chronic high depression was also observed but not chronic high anxiety. Analyses of predictors were consistent with the hypothesis that resilient individuals view major stressors as challenges to be accepted and met with active coping efforts. These results are comparable to other recent studies of major health stressors.

Peritraumatic and trait dissociation differentiate police officers with resilient versus symptomatic trajectories of posttraumatic stress symptoms.

Research has consistently demonstrated that stress reactions to potentially traumatic events do not represent a unified phenomenon. Instead, individuals tend to cluster into prototypical response patterns over time including chronic symptoms, recovery, and resilience. We examined heterogeneity in a posttraumatic stress disorder (PTSD) symptom course in a sample of 178 active-duty police officers following exposure to a life-threatening event using latent growth mixture modeling (LGMM). This analysis revealed 3 discrete PTSD symptom trajectories: resilient (88%), distressed-improving (10%), and distressed-worsening (2%). We further examined whether trait and peritraumatic dissociation distinguished these symptom trajectories. Findings indicate that trait and peritraumatic dissociation differentiated the resilient from the distressed-improving trajectory (trait, p < .05; peritraumatic, p < .001), but only peritraumatic dissociation differentiated the resilient from the distressed-worsening trajectory (p < .001). It is essential to explore heterogeneity in symptom course and its predictors among active-duty police officers, a repeatedly exposed group. These findings suggest that police officers may be a highly resilient group overall. Furthermore, though there is abundant evidence that dissociation has a positive linear relationship with PTSD symptoms, this study demonstrates that degree of dissociation can distinguish between resilient and symptomatic groups of individuals.

Trajectory of post-traumatic stress following traumatic injury: 6-year follow-up

BACKGROUND Traumatic injuries affect millions of patients each year, and resulting post-traumatic stress disorder (PTSD) significantly contributes to subsequent impairment. AIMS To map the distinctive long-term trajectories of PTSD responses over 6 years by using latent growth mixture modelling. METHOD Randomly selected injury patients (n = 1084) admitted to four hospitals around Australia were assessed in hospital, and at 3, 12, 24 and 72 months. Lifetime psychiatric history and current PTSD severity and funxctioning were assessed. RESULTS Five trajectories of PTSD response were noted across the 6 years: (a) chronic (4%), (b) recovery (6%), (c) worsening/recovery (8%), (d) worsening (10%) and (e) resilient (73%). A poorer trajectory was predicted by female gender, recent life stressors, presence of mild traumatic brain injury and admission to intensive care unit. CONCLUSIONS These findings demonstrate the long-term PTSD effects that can occur following traumatic injury. The different trajectories highlight that monitoring a subset of patients over time is probably a more accurate means of identifying PTSD rather than relying on factors that can be assessed during hospital admission.

Early PTSD Symptom Trajectories: Persistence, Recovery, and Response to Treatment: Results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS)

Context Uncovering heterogeneities in the progression of early PTSD symptoms can improve our understanding of the disorders pathogenesis and prophylaxis. Objectives To describe discrete symptom trajectories and examine their relevance for preventive interventions. Design Latent Growth Mixture Modeling (LGMM) of data from a randomized controlled study of early treatment. LGMM identifies latent longitudinal trajectories by exploring discrete mixture distributions underlying observable data. Setting Hadassah Hospital unselectively receives trauma survivors from Jerusalem and vicinity. Participants Adult survivors of potentially traumatic events consecutively admitted to the hospitals emergency department (ED) were assessed ten days and one-, five-, nine- and fifteen months after ED admission. Participants with data at ten days and at least two additional assessments (n = 957) were included; 125 received cognitive behavioral therapy (CBT) between one and nine months. Approach We used LGMM to identify latent parameters of symptom progression and tested the effect of CBT on these parameters. CBT consisted of 12 weekly sessions of either cognitive therapy (n = 41) or prolonged exposure (PE, n = 49), starting 29.8±5.7 days after ED admission, or delayed PE (n = 35) starting at 151.8±42.4 days. CBT effectively reduced PTSD symptoms in the entire sample. Main Outcome Measure Latent trajectories of PTSD symptoms; effects of CBT on these trajectories. Results Three trajectories were identified: Rapid Remitting (rapid decrease in symptoms from 1- to 5-months; 56% of the sample), Slow Remitting (progressive decrease in symptoms over 15 months; 27%) and Non-Remitting (persistently elevated symptoms; 17%). CBT accelerated the recovery of the Slow Remitting class but did not affect the other classes. Conclusions The early course of PTSD symptoms is characterized by distinct and diverging response patterns that are centrally relevant to understanding the disorder and preventing its occurrence. Studies of the pathogenesis of PTSD may benefit from using clustered symptom trajectories as their dependent variables.

Quantitative forecasting of PTSD from early trauma responses: A Machine Learning application

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorders salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

Killing and latent classes of PTSD symptoms in Iraq and Afghanistan veterans

BACKGROUND Our goal was to better understand distinct PTSD symptom presentations in Iraq and Afghanistan Veterans (N=227) and to determine whether those who killed in war were at risk for being in the most symptomatic class. METHODS We used latent class analysis of responses to the PTSD checklist and logistic regression of most symptomatic class. RESULTS We found that a four-class solution best fit the data, with the following profiles emerging: High Symptom (34% of participants), Intermediate Symptom (41%), Intermediate Symptom with Low Emotional Numbing (10%), and Low Symptom (15%). The largest group of individuals who reported killing (45%) was in the High Symptom class, and those who killed had twice the odds of being in the most symptomatic PTSD class, compared to those who did not kill. Those who endorsed killing a non-combatant (OR=4.56, 95% CI [1.77, 11.7], p<0.01) or killing in the context of anger or revenge (OR=4.63, 95% CI=[1.89, 11.4], p<0.001) were more likely to belong to the most symptomatic PTSD class, compared to those who did not kill. LIMITATIONS The study was retrospective and cross-sectional. The results may not generalize to veterans of other wars. CONCLUSIONS Killing in war may be an important indicator of risk for developing frequent and severe PTSD symptoms. This has implications for the mental healthcare of veterans, providing evidence that a comprehensive evaluation of returning veterans should include an assessment of killing experiences and reactions to killing.

Positive and Negative Emotion Prospectively Predict Trajectories of Resilience and Distress Among High-Exposure Police Officers

Responses to both potentially traumatic events and other significant life stressors have been shown to conform to discrete patterns of response such as resilience, anticipatory stress, initial distress with gradual recovery, and chronic distress. The etiology of these trajectories is still unclear. Individual differences in levels of negative and positive emotion are believed to play a role in determining risk and resilience following traumatic exposure. In the current investigation, we followed police officers prospectively from academy training through 48 months of active duty, assessing levels of distress every 12 months. Using latent class growth analysis, we identified 4 trajectories closely conforming to prototypical patterns. Furthermore, we found that lower levels of self-reported negative emotion during academy training prospectively predicted membership in the resilient trajectory compared with the more symptomatic trajectories following the initiation of active duty, whereas higher levels of positive emotion during academy training differentiated resilience from a trajectory that was equivalently low on distress during academy training but consistently grew in distress through 4 years of active duty. These findings emerging from a prospective longitudinal design provide evidence that resilience is predicted by both lower levels of negative emotion and higher levels of positive emotion prior to active duty stressor exposure.

Fear load: The psychophysiological over-expression of fear as an intermediate phenotype associated with trauma reactions.

Psychophysiological measures of fear expression provide observable intermediate phenotypes of fear-related symptoms. Research Domain Criteria (RDoC) advocate using neurobiological intermediate phenotypes that provide dimensional correlates of psychopathology. Negative Valence Systems in the RDoC matrix include the construct of acute threat, which can be measured on a physiological level using potentiation of the acoustic startle reflex assessed via electromyography recordings of the orbicularis oculi muscle. Impairments in extinction of fear-potentiated startle due to high levels of fear (termed fear load) during the early phases of extinction have been observed in posttraumatic stress disorder (PTSD). The goals of the current work were to examine dimensional associations between fear-related symptoms of PTSD and fear load variables to test their validity as an intermediate phenotype. We examined extinction of fear-potentiated startle in a cohort (n=269) of individuals with a broad range of civilian trauma exposure (range 0-13 traumatic events per person, mean=3.5). Based on previously reported findings, we hypothesized that fear load would be significantly associated with intrusion and fear memories of an index traumatic event. The results indicated that early extinction was correlated with intrusive thoughts (p=0.0007) and intense physiological reactions to trauma reminders (p=0.036). Degree of adult or childhood trauma exposure, and depression severity were not associated with fear load. After controlling for age, sex, race, income, level of prior trauma, and level of fear conditioning, fear load during extinction was still significantly predictive of intrusive thoughts (p=0.004). The significance of these findings is that they support dimensional associations with symptom severity rather than diagnostic category and, as such, fear load may emerge as a transdiagnostic intermediate phenotype expressed across fear-related disorders (e.g., specific phobia, social phobia).