Greater Early Posttrauma Activation in the Right Inferior Frontal Gyrus Predicts Recovery From Posttraumatic Stress Disorder Symptoms.

BACKGROUND: Posttraumatic stress disorder (PTSD) has been associated with altered emotion processing and modulation in specific brain regions, i.e., the amygdala, insula, and medial prefrontal and anterior cingulate cortices. Functional alterations in these regions, recorded shortly after trauma exposure, may predict changes in PTSD symptoms. METHODS: Survivors (N = 104) of a traumatic event, predominantly a motor vehicle accident, were included. Functional magnetic resonance imaging was used to assess brain activation 1, 6, and 14 months after trauma exposure (T1, T2, and T3, respectively). Participants performed the Shifted-attention Emotional Appraisal Task, which probes 3 affective processes: implicit emotional processing (of emotional faces), emotion modulation by attention shifting (away from these faces), and emotion modulation by appraisal (of the participants' own emotional response to these faces). We defined regions of interest based on task-related activations, extracted beta weights from these regions of interest, and submitted them to a series of analyses to examine relationships between neural activation and PTSD severity over the 3 time points. RESULTS: At T1, a regression model containing activations in the left dorsolateral prefrontal cortex, bilateral inferior frontal gyrus (IFG), and medial prefrontal cortex during emotion modulation by appraisal significantly predicted change in PTSD symptoms. More specifically, greater right IFG activation at T1 was associated with greater reduction in symptom severity (T1-T3). Exploratory analysis also found that activation of the right IFG increased from T1 to T3. CONCLUSIONS: The results suggest that greater early posttrauma activation during emotion appraisal in the right IFG, a region previously linked to cognitive control in PTSD, predicts recovery from PTSD symptoms.

Evaluating the Evidence for Brain-Based Biotypes of Psychiatric Vulnerability in the Acute Aftermath of Trauma.

OBJECTIVE: The weak link between subjective symptom-based diagnostic methods for posttraumatic psychopathology and objectively measured neurobiological indices forms a barrier to the development of effective personalized treatments. To overcome this problem, recent studies have aimed to stratify psychiatric disorders by identifying consistent subgroups based on objective neural markers. Along these lines, a promising 2021 study by Stevens et al. identified distinct brain-based biotypes associated with different longitudinal patterns of posttraumatic symptoms. Here, the authors conducted a conceptual nonexact replication of that study using a comparable data set from a multimodal longitudinal study of recent trauma survivors. METHODS: A total of 130 participants (mean age, 33.61 years, SD=11.21; 48% women) admitted to a general hospital emergency department following trauma exposure underwent demographic, clinical, and neuroimaging assessments 1, 6, and 14 months after trauma. All analyses followed the pipeline outlined in the original study and were conducted in collaboration with its authors. RESULTS: Task-based functional MRI conducted 1 month posttrauma was used to identify four clusters of individuals based on profiles of neural activity reflecting threat and reward reactivity. These clusters were not identical to the previously identified brain-based biotypes and were not associated with prospective symptoms of posttraumatic psychopathology. CONCLUSIONS: Overall, these findings suggest that the original brain-based biotypes of trauma resilience and psychopathology may not generalize to other populations. Thus, caution is warranted when attempting to define subtypes of psychiatric vulnerability using neural indices before treatment implications can be fully realized. Additional replication studies are needed to identify more stable and generalizable neuroimaging-based biotypes of posttraumatic psychopathology.

Longitudinal volumetric evaluation of hippocampus and amygdala subregions in recent trauma survivors.

The hippocampus and the amygdala play a central role in post-traumatic stress disorder (PTSD) pathogenesis. While alternations in volumes of both regions have been consistently observed in individuals with PTSD, it remains unknown whether these reflect pre-trauma vulnerability traits or acquired post-trauma consequences of the disorder. Here, we conducted a longitudinal panel study of adult civilian trauma survivors admitted to a general hospital emergency department (ED). One hundred eligible participants (mean age = 32.97 ± 10.97, n = 56 females) completed both clinical interviews and structural MRI scans at 1-, 6-, and 14-months after ED admission (alias T1, T2, and T3). While all participants met PTSD diagnosis at T1, only n = 29 still met PTSD diagnosis at T3 (a "non-Remission" Group), while n = 71 did not (a "Remission" Group). Bayesian multilevel modeling analysis showed robust evidence for smaller right hippocampus volume (P+ of ~0.014) and moderate evidence for larger left amygdala volume (P+ of ~0.870) at T1 in the "non-Remission" group, compared to the "Remission" group. Subregion analysis further demonstrated robust evidence for smaller volume in the subiculum and right CA1 hippocampal subregions (P+ of ~0.021-0.046) in the "non-Remission" group. No time-dependent volumetric changes (T1 to T2 to T3) were observed across all participants or between groups. Results support the "vulnerability trait" hypothesis, suggesting that lower initial volumes of specific hippocampus subregions are associated with non-remitting PTSD. The stable volume of all hippocampal and amygdala subregions does not support the idea of consequential, progressive, stress-related atrophy during the first critical year following trauma exposure.

Assessment of early neurocognitive functioning increases the accuracy of predicting chronic PTSD risk.

Post-traumatic stress disorder (PTSD) is a protracted and debilitating consequence of traumatic events. Identifying early predictors of PTSD can inform the disorder's risk stratification and prevention. We used advanced computational models to evaluate the contribution of early neurocognitive performance measures to the accuracy of predicting chronic PTSD from demographics and early clinical features. We consecutively enrolled adult trauma survivors seen in a general hospital emergency department (ED) to a 14-month long prospective panel study. Extreme Gradient Boosting algorithm evaluated the incremental contribution to 14 months PTSD risk of demographic variables, 1-month clinical variables, and concurrent neurocognitive performance. The main outcome variable was PTSD diagnosis, 14 months after ED admission, obtained by trained clinicians using the Clinician-Administered PTSD Scale (CAPS). N = 138 trauma survivors (mean age = 34.25 ± 11.73, range = 18-64; n = 73 [53%] women) were evaluated 1 month after ED admission and followed for 14 months, at which time n = 33 (24%) met PTSD diagnosis. Demographics and clinical variables yielded a discriminatory accuracy of AUC = 0.68 in classifying PTSD diagnostic status. Adding neurocognitive functioning improved the discriminatory accuracy (AUC = 0.88); the largest contribution emanating from poorer cognitive flexibility, processing speed, motor coordination, controlled and sustained attention, emotional bias, and higher response inhibition, and recall memory. Impaired cognitive functioning 1-month after trauma exposure is a significant and independent risk factor for PTSD. Evaluating cognitive performance could improve early screening and prevention.

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). METHODS: N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. RESULTS: Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). CONCLUSIONS: Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

Neural Responsivity to Reward Versus Punishment Shortly After Trauma Predicts Long-Term Development of Posttraumatic Stress Symptoms.

BACKGROUND: Processing negatively and positively valenced stimuli involves multiple brain regions including the amygdala and ventral striatum (VS). Posttraumatic stress disorder (PTSD) is often associated with hyperresponsivity to negatively valenced stimuli, yet recent evidence also points to deficient positive valence functioning. It is yet unclear what the relative contribution is of such opposing valence processing shortly after trauma to the development of chronic PTSD. METHODS: Neurobehavioral indicators of motivational positive versus negative valence sensitivities were longitudinally assessed in 171 adults (87 females, age = 34.19 ± 11.47 years) at 1, 6, and 14 months following trauma exposure (time point 1 [TP1], TP2, and TP3, respectively). Using a gambling functional magnetic resonance imaging paradigm, amygdala and VS functionality (activity and functional connectivity with the prefrontal cortex) in response to rewards versus punishments were assessed with relation to PTSD severity at different time points. The effect of valence processing was depicted behaviorally by the amount of risk taken to maximize reward. RESULTS: PTSD severity at TP1 was associated with greater neural functionality in the amygdala (but not in the VS) toward punishments versus rewards, and with fewer risky choices. PTSD severity at TP3 was associated with decreased neural functionality in both the VS and the amygdala toward rewards versus punishments at TP1 (but not with risky behavior). Explainable machine learning revealed the primacy of VS-biased processing, over the amygdala, in predicting PTSD severity at TP3. CONCLUSIONS: These results highlight the importance of biased neural responsivity to positive relative to negative motivational outcomes in PTSD development. Novel therapeutic strategies early after trauma may thus target both valence fronts.

Incidence and risk factors of mental disorders in the elderly: The European MentDis_ICF65+ study.

OBJECTIVE: While incidence rates of depression and anxiety disorders in the elderly have been comprehensively investigated, the incidence rates of other mental disorders have rarely been researched. The incidence rate and predictors of various mental disorders in the elderly were evaluated in different European and associated countries. METHODS: A cross-sectional and longitudinal multi-centre survey of Diagnostic and Statistical Manual of Mental Disorders (4th ed.) diagnoses was conducted in different European and associated countries (Germany, Italy, Spain, Switzerland, the United Kingdom and Israel) to collect data on the prevalence and incidence of mental disorders in the elderly. The sample size of the longitudinal wave was N = 2592 elderly. RESULTS: The overall 1-year incidence rate for any mental disorder in the elderly is 8.65%. At 5.18%, any anxiety disorder had the highest incidence rate across all diagnostic groups. The incidence rate for any affective disorder was 2.97%. The lowest incidence rates were found for agoraphobia (1.37%) and panic disorder (1.30%). Risk factors for the development of any mental disorder were never having been married, no religious affiliation, a higher number of physical illnesses and a lower quality of life. CONCLUSION: In comparison to other studies, lower incidence rates for any affective disorder and middle-range incidence for any anxiety disorder were found. To the authors' knowledge, no prior studies have reported 1-year incidence rates for somatoform disorder, bipolar disorder and substance misuse in community-dwelling elderly. These findings indicate the need to raise awareness of psychosocial problems in the elderly and to ensure adequate availability of mental health services.

Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors.

Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

Posttraumatic stress disorder symptom trajectories within the first year following emergency department admissions: pooled results from the International Consortium to predict PTSD.

BACKGROUND: Research exploring the longitudinal course of posttraumatic stress disorder (PTSD) symptoms has documented four modal trajectories (low, remitting, high, and delayed), with proportions varying across studies. Heterogeneity could be due to differences in trauma types and patient demographic characteristics. METHODS: This analysis pooled data from six longitudinal studies of adult survivors of civilian-related injuries admitted to general hospital emergency departments (EDs) in six countries (pooled N = 3083). Each study included at least three assessments of the clinician-administered PTSD scale in the first post-trauma year. Latent class growth analysis determined the proportion of participants exhibiting various PTSD symptom trajectories within and across the datasets. Multinomial logistic regression analyses examined demographic characteristics, type of event leading to the injury, and trauma history as predictors of trajectories differentiated by their initial severity and course. RESULTS: Five trajectories were found across the datasets: Low (64.5%), Remitting (16.9%), Moderate (6.7%), High (6.5%), and Delayed (5.5%). Female gender, non-white race, prior interpersonal trauma, and assaultive injuries were associated with increased risk for initial PTSD reactions. Female gender and assaultive injuries were associated with risk for membership in the Delayed (v. Low) trajectory, and lower education, prior interpersonal trauma, and assaultive injuries with risk for membership in the High (v. Remitting) trajectory. CONCLUSIONS: The results suggest that over 30% of civilian-related injury survivors admitted to EDs experience moderate-to-high levels of PTSD symptoms within the first post-trauma year, with those reporting assaultive violence at increased risk of both immediate and longer-term symptoms.

Five Essential Elements of Immediate and Mid-Term Mass Trauma Intervention: Empirical Evidence.

Given the devastation caused by disasters and mass violence, it is critical that intervention policy be based on the most updated research findings. However, to date, no evidence-based consensus has been reached supporting a clear set of recommendations for intervention during the immediate and the mid-term post mass trauma phases. Because it is unlikely that there will be evidence in the near or mid-term future from clinical trials that cover the diversity of disaster and mass violence circumstances, we assembled a worldwide panel of experts on the study and treatment of those exposed to disaster and mass violence to extrapolate from related fields of research, and to gain consensus on intervention principles. We identified five empirically supported intervention principles that should be used to guide and inform intervention and prevention efforts at the early to mid-term stages. These are promoting: 1) a sense of safety, 2) calming, 3) a sense of self- and community efficacy, 4) connectedness, and 5) hope.

A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor.

Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment6-9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.

Correction to: Evaluating a screener to quantify PTSD risk using emergency care information: a proof of concept study.

An amendment to this paper has been published and can be accessed via the original article.

Multi-domain potential biomarkers for post-traumatic stress disorder (PTSD) severity in recent trauma survivors.

Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semi-unsupervised method ("3C") was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants' cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala's functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results' robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.

Evaluating a screener to quantify PTSD risk using emergency care information: a proof of concept study.

BACKGROUND: Previous work has indicated that post-traumatic stress disorder (PTSD) symptoms, measured by the Clinician-Administered PTSD Scale (CAPS) within 60 days of trauma exposure, can reliably produce likelihood estimates of chronic PTSD among trauma survivors admitted to acute care centers. Administering the CAPS is burdensome, requires skilled professionals, and relies on symptoms that are not fully expressed upon acute care admission. Predicting chronic PTSD from peritraumatic responses, which are obtainable upon acute care admission, has yielded conflicting results, hence the rationale for a stepwise screening-and-prediction practice. This work explores the ability of peritraumatic responses to produce risk likelihood estimates of early CAPS-based PTSD symptoms indicative of chronic PTSD risk. It specifically evaluates the Peritraumatic Dissociative Experiences Questionnaire (PDEQ) as a risk-likelihood estimator. METHODS: We used individual participant data (IPD) from five acute care studies that used both the PDEQ and the CAPS (n = 647). Logistic regression calculated the probability of having CAPS scores ≥ 40 between 30 and 60 days after trauma exposure across the range of initial PDEQ scores, and evaluated the added contribution of age, sex, trauma type, and prior trauma exposure. Brier scores, area under the receiver-operating characteristic curve (AUC), and the mean slope of the calibration line evaluated the accuracy and precision of the predicted probabilities. RESULTS: Twenty percent of the sample had CAPS ≥ 40. PDEQ severity significantly predicted having CAPS ≥ 40 symptoms (p < 0.001). Incremental PDEQ scores produced a reliable estimator of CAPS ≥ 40 likelihood. An individual risk estimation tool incorporating PDEQ and other significant risk indicators is provided. CONCLUSION: Peritraumatic reactions, measured here by the PDEQ, can reliably quantify the likelihood of acute PTSD symptoms predictive of chronic PTSD and requiring clinical attention. Using them as a screener in a stepwise chronic PTSD prediction strategy may reduce the burden of later CAPS-based assessments. Other peritraumatic metrics may perform similarly and their use requires similar validation. TRIAL REGISTRATION: Jerusalem Trauma Outreach and Prevention Study (J-TOPS): NCT00146900.

Quality of life, level of functioning, and its relationship with mental and physical disorders in the elderly: results from the MentDis_ICF65+ study.

BACKGROUND: An ageing population worldwide needs to investigate quality of life (QoL) and level of functioning (LoF) in the elderly and its associated variables. We aimed to study the relationship between Quality of Life (QoL) and Level of Functioning (LoF) in an elderly population in Europe. METHOD: As part of the Ment_Dis65+ European Project, 3142 community-dwelling adults aged 65-84 years in six countries were assessed by using the adaptation for the elderly of the Composite International Diagnostic Interview (CIDI65+) to provide psychiatric diagnosis according to the International Classification of Diseases (10th edition) (ICD-10 Classification of Mental and Behavioural Disorders). Socio-demographic and clinical interviews, and two self-report tools, the World Health Organization QoL assessment (WHO QoL BREF), to assess QoL, and the WHO Disability Assessment Schedule -II (WHODAS-II), to assess LoF, were also administered. RESULTS: Most subjects reported good levels of QoL (56.6%) and self-rated health (62%), with no or mild disability (58.8%). There was a linear decrease of the QoL and the LoF by increase of age. Elderly with ICD-10 mental disorder (e.g. somatoform, affective and anxiety disorders) had poorer QoL and lower LoF. There were a number of predictors of lower levels of QoL and disability, including both socio-demographic variables (e.g. male gender, increase in age, poor financial situation, retirement, reduced number of close significant others), ICD-10 psychiatric diagnosis (mainly anxiety, somatoform disorders) and presence of medical disorders (mainly heart and respiratory diseases). CONCLUSIONS: The study indicates that QoL and LoF were quite acceptable in European elderly people. A series of variables, including psychiatric and somatic disorders, as well as socio-demographic factor influenced in a negative way both QoL and LoF. More specific links between mental health, social and health services dedicated to this segment of the population, should be implemented in order to provide better care for elderly people with conditions impacting their QoL and functioning.

Neuroanatomical Risk Factors for Posttraumatic Stress Disorder in Recent Trauma Survivors.

BACKGROUND: Low hippocampal volume could serve as an early risk factor for posttraumatic stress disorder (PTSD) in interaction with other brain anomalies of developmental origin. One such anomaly may well be the presence of a large cavum septum pellucidum (CSP), which has been loosely associated with PTSD. We performed a longitudinal prospective study of recent trauma survivors. We hypothesized that at 1 month after trauma exposure the relation between hippocampal volume and PTSD symptom severity will be moderated by CSP volume, and that this early interaction will account for persistent PTSD symptoms at subsequent time points. METHODS: One hundred seventy-one adults (87 women, average age 34.22 years [range, 18-65 years of age]) who were admitted to a general hospital's emergency department after a traumatic event underwent clinical assessment and structural magnetic resonance imaging within 1 month after trauma. Follow-up clinical evaluations were conducted at 6 (n = 97) and 14 (n = 78) months after trauma. Hippocampal and CSP volumes were measured automatically by FreeSurfer software and verified manually by a neuroradiologist (D.N.). RESULTS: At 1 month after trauma, CSP volume significantly moderated the relation between hippocampal volume and PTSD severity (p = .026), and this interaction further predicted symptom severity at 14 months posttrauma (p = .018). Specifically, individuals with a smaller hippocampus and larger CSP at 1 month posttrauma showed more severe symptoms at 1 and 14 months after trauma exposure. CONCLUSIONS: Our study provides evidence for an early neuroanatomical risk factors for PTSD, which could also predict the progression of the disorder in the year after trauma exposure. Such a simple-to-acquire neuroanatomical signature for PTSD could guide early management as well as long-term monitoring.

Affective disorders in the elderly in different European countries: Results from the MentDis_ICF65+ study.

OBJECTIVES: Affective disorders are among the most prevalent disorders in the elderly. The present study aims to examine the sociodemographic and clinical correlates of major depressive disorder (MDD) and dysthymia in different European and Associated countries using standardized interview techniques. Furthermore, service utilization for the elderly with depression is assessed. METHODS: The MentDis_ICF65+ study is a cross-sectional survey (N = 3,142) that was conducted in six different European and Associated countries (Germany, Italy, Spain, Switzerland, England and Israel) with a subsample of n = 463 elderly with any depressive disorder. RESULTS: Sociodemographic and clinical correlates, such as gender, age and symptom severity, were significantly associated with MDD and dysthymia in the elderly. Only 50% of elderly with any depressive disorder were treated with psycho- or pharmacotherapy. CONCLUSION: Our findings identified sociodemographic and clinical characteristics for depression risk in the elderly and highlight the need to improve service delivery to older adults who suffer from depression.