Faculty Bibliography

BACKGROUND:Posttraumatic Stress Disorder (PTSD) has been associated with altered emotion processing and modulation in specific brain regions, i.e. amygdala, insula, 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 one, six and 14 months after trauma exposure (T1, T2 and T3, respectively). Participants performed the Shifted Attention Emotional Appraisal Task (SEAT), which probes three 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 (ROIs) based on task-related activations, extracted beta-weights from these ROIs and submitted them to series of analyses to examine relationships between neural activation and PTSD severity over the three timepoints. RESULTS:At T1, a regression model containing activations in left dorsolateral prefrontal cortex, bilateral inferior frontal gyrus (IFG) and medial prefrontal cortex during emotion modulation by appraisal significantly predicted change in PTSD symptoms. Specifically, greater right IFG activation at T1 was associated with greater reduction in symptom severity (T1-T3). Exploratory analysis also found that activation of right IFG increased from T1 to T3. CONCLUSIONS:The results suggest that greater early activation during emotion appraisal in the right IFG, a region previously linked to cognitive control in PTSD, predicts recovery from post-trauma PTSD symptoms.

OBJECTIVE/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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.

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.

Background: Reduced volume of the hippocampus (HC) has been repeatedly documented in Post-traumatic Stress Disorder (PTSD) patients, in whom it can represent either pre-traumatic vulnerability trait or post-exposure decay. Longitudinal brain volumetric studies of long-enough duration, proper timing, and reasonable attrition can inform that question. Here, we aimed to examine whether HC volume early after trauma is associated with the development of chronic PTSD, and whether there are time-dependent changes in HC volume during the first 14-months post-trauma.
Method(s): One hundred adult civilians, consecutively admitted to a medical center's emergency department following traumatic events, were screened for PTSD and underwent MRI scans at 1-, 6-, and 14-months post-trauma exposure (T1, T2, and T3, respectively). All participants met PTSD diagnosis at T1, as determined by a structured clinical interview using the Clinician-Administered PTSD Scale (CAPS). Cortical reconstruction and volumetric segmentation were performed using the FreeSurfer v7.1.0 image analysis suite.
Result(s): Out of the n=100 participants with PTSD at T1, n=29 participants still met PTSD diagnosis at T3 ('Persistent PTSD' group), while the other n=71 recovered by T3 ('Recovery' group). Bayesian multilevel (BML) analysis at T1 showed strong evidence for smaller right (but not left) HC volume in the 'Persistent PTSD' group (P+ = 0.014) compared to the 'Recovery' group. BML analysis examining change over time (T1, T2, and T3) showed no evidence for Time-by-Group interactions (right-HC: P+ = 0.406, left HC: P+ = 0.456).
Conclusion(s): HC volume remains stable among survivors of single traumatic events during the first 14-months following trauma exposure, whether they recover or develop chronic PTSD. A smaller initial volume of the right HC among the 'Persistent PTSD' group supports the above-mentioned 'vulnerability trait' hypothesis. Supported By: R01-MH-103287 from the National Institute of Mental Health (NIMH) Keywords: PT

Background: Post-traumatic stress disorder (PTSD) has been associated with neural alterations within emotion processing and modulation regions. Neurobiological processes following a traumatic event may predict either the recovery from or development of PTSD symptoms. We conducted this longitudinal study to understand the association between evolving PTSD symptoms and the neural processes among recent trauma survivors Methods: Ninety-eight trauma survivors (43 male; mean age (SD)= 33.36 (11.26) years) admitted to a general hospital emergency room following traumatic incidents were included. Functional magnetic resonance imaging (fMRI) was used to assess brain activation during the Shifted Attention Emotional Appraisal Task (SEAT) within 30 days (T1) following the trauma. We used CAPS-5 to assess symptoms severity at T1 (n=98), T2 (six months; n=82) and T3 (14 months; n=84) timepoints following the trauma.
Result(s): Neural activations during task conditions followed expected patterns. Specifically, we found activation of the bilateral insula and dACC and deactivation of bilateral dlPFC during implicit emotion processing; activation of bilateral dlPFC and deactivation of bilateral amygdala during emotion modulation by attention shifting; and activation of dlPFC, mPFC and bilateral IFG during emotion modulation by appraisal (all p<.050, FWE-corrected). During appraisal, combined activations in all five ROIs predicted change in symptoms (T1-T3), with a significant contribution of right IFG (all p<.050). There were no associations between other ROIs and other task conditions and CAPS.
Conclusion(s): These findings suggest that neural activation during emotion modulation within one month after the trauma could be used to predict PTSD symptom development over a 14-month period. (SA and JS: Equal contribution). Supported By: R01-MH-103287 Keywords: PTSD, Functional Neuroimaging, Implicit Emotion Processing, Emotion Modulation by Attention Shifting, Emotion Modulation by Appraisal
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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.

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)