Faculty Bibliography

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.

OBJECTIVE/UNASSIGNED: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/UNASSIGNED: = 2592 elderly. RESULTS/UNASSIGNED: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/UNASSIGNED: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.

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.

Personalized medicine has led to important discoveries and medical innovations. For the successful translation of that progress into precision psychiatry, the complexity of mental illness and its underpinning mechanisms must be considered, and data- driven approaches are needed. Computational approaches such as machine learning are important drivers of innovation and are spurred by recent advances in statistical modeling. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

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.

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 treatment^6-9 to mitigate subsequent psychopathology in high-risk populations^10,11. 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.

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

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.

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/METHODS: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.