19 research outputs found

    Cognitive Flexibility Predicts PTSD Symptoms: Observational and Interventional Studies

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    Introduction: Post-Traumatic Stress Disorder (PTSD) is a prevalent, severe and tenacious psychopathological consequence of traumatic events. Neurobehavioral mechanisms underlying PTSD pathogenesis have been identified, and may serve as risk-resilience factors during the early aftermath of trauma exposure. Longitudinally documenting the neurobehavioral dimensions of early responses to trauma may help characterize survivors at risk and inform mechanism-based interventions. We present two independent longitudinal studies that repeatedly probed clinical symptoms and neurocognitive domains in recent trauma survivors. We hypothesized that better neurocognitive functioning shortly after trauma will be associated with less severe PTSD symptoms a year later, and that an early neurocognitive intervention will improve cognitive functioning and reduce PTSD symptoms.Methods: Participants in both studies were adult survivors of traumatic events admitted to two general hospitals’ emergency departments (EDs) in Israel. The studies used identical clinical and neurocognitive tools, which included assessment of PTSD symptoms and diagnosis, and a battery of neurocognitive tests. The first study evaluated 181 trauma-exposed individuals one-, six-, and 14 months following trauma exposure. The second study evaluated 97 trauma survivors 1 month after trauma exposure, randomly allocated to 30 days of web-based neurocognitive intervention (n = 50) or control tasks (n = 47), and re-evaluated all subjects three- and 6 months after trauma exposure.Results: In the first study, individuals with better cognitive flexibility at 1 month post-trauma showed significantly less severe PTSD symptoms after 13 months (p = 0.002). In the second study, the neurocognitive training group showed more improvement in cognitive flexibility post-intervention (p = 0.019), and lower PTSD symptoms 6 months post-trauma (p = 0.017), compared with controls. Intervention- induced improvement in cognitive flexibility positively correlated with clinical improvement (p = 0.002).Discussion: Cognitive flexibility, shortly after trauma exposure, emerged as a significant predictor of PTSD symptom severity. It was also ameliorated by a neurocognitive intervention and associated with a better treatment outcome. These findings support further research into the implementation of mechanism-driven neurocognitive preventive interventions for PTSD

    Cognitive and Behavioral Patterns across Psychiatric Conditions

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    Psychiatric conditions represent a highly heterogeneous group of disorders associated with chronic distress and a sharp decline in quality of life [...

    Patient and Therapist In-Session Cortisol as Predictor of Post-Session Patient Reported Affect

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    The importance of the role of affect in psychotherapy for major depressive disorder (MDD) is well established, but the common use of self-reported measures may limit our understanding of its underlying mechanisms. A promising predictor of patient affect is the stress hormone cortisol. To date, no studies have studied in-session changes in cortisol in psychotherapy for MDD. We investigated whether an increase in patient cortisol over the course of a session correlated with higher negative and lower positive affect. Given previous findings on healthy individuals on the contagious nature of stress, an additional aim was to examine whether these relationships are moderated by therapist cortisol. To this end, 40 dyads (including 6 therapists) provided saliva samples before and after four pre-specified sessions (616 samples). After each session, the patients provided retrospective reports of in-session affect. We found no association between patient cortisol and affect. However, increases in patient cortisol predicted negative affect when the therapists exhibited decreases in cortisol, and increases in patient cortisol predicted positive affect when the therapists showed increases. Our study provides initial evidence for the importance of the social context in the cortisol–affect relationship in MDD

    Patient and Therapist In-Session Cortisol as Predictor of Post-Session Patient Reported Affect

    No full text
    The importance of the role of affect in psychotherapy for major depressive disorder (MDD) is well established, but the common use of self-reported measures may limit our understanding of its underlying mechanisms. A promising predictor of patient affect is the stress hormone cortisol. To date, no studies have studied in-session changes in cortisol in psychotherapy for MDD. We investigated whether an increase in patient cortisol over the course of a session correlated with higher negative and lower positive affect. Given previous findings on healthy individuals on the contagious nature of stress, an additional aim was to examine whether these relationships are moderated by therapist cortisol. To this end, 40 dyads (including 6 therapists) provided saliva samples before and after four pre-specified sessions (616 samples). After each session, the patients provided retrospective reports of in-session affect. We found no association between patient cortisol and affect. However, increases in patient cortisol predicted negative affect when the therapists exhibited decreases in cortisol, and increases in patient cortisol predicted positive affect when the therapists showed increases. Our study provides initial evidence for the importance of the social context in the cortisol–affect relationship in MDD

    Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors

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    Background Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources. Methods For that, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data, alongside demographic features, were used to predict high versus low chronic stress with support vector machine classifiers, applying out-of-sample model testing. Results The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate, heart-rate circadian characteristics), lifestyle (steps count, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification. Conclusion As wearable technologies continue to rapidly evolve, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior

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

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    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

    Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression

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    Major methodological advancements have been recently made in the field of neural decoding, which is concerned with the reconstruction of mental content from neuroimaging measures. However, in the absence of a large-scale examination of the validity of the decoding models across subjects and content, the extent to which these models can be generalized is not clear. This study addresses the challenge of producing generalizable decoding models, which allow the reconstruction of perceived audiovisual features from human magnetic resonance imaging (fMRI) data without prior training of the algorithm on the decoded content. We applied an adapted version of kernel ridge regression combined with temporal optimization on data acquired during film viewing (234 runs) to generate standardized brain models for sound loudness, speech presence, perceived motion, face-to-frame ratio, lightness, and color brightness. The prediction accuracies were tested on data collected from different subjects watching other movies mainly in another scanner. Substantial and significant (QFDR<0.05) correlations between the reconstructed and the original descriptors were found for the first three features (loudness, speech, and motion) in all of the 9 test movies (R¯=0.62, R¯ = 0.60, R¯ = 0.60, respectively) with high reproducibility of the predictors across subjects. The face ratio model produced significant correlations in 7 out of 8 movies (R¯=0.56). The lightness and brightness models did not show robustness (R¯=0.23, R¯ = 0). Further analysis of additional data (95 runs) indicated that loudness reconstruction veridicality can consistently reveal relevant group differences in musical experience. The findings point to the validity and generalizability of our loudness, speech, motion, and face ratio models for complex cinematic stimuli (as well as for music in the case of loudness). While future research should further validate these models using controlled stimuli and explore the feasibility of extracting more complex models via this method, the reliability of our results indicates the potential usefulness of the approach and the resulting models in basic scientific and diagnostic contexts
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