Dataset Information

Mapping PTSD symptoms to brain networks: a machine learning study.

ABSTRACT: Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N?=?50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R²) for total PCL-5 score was 0.29 and the R² for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, -0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p?=?0.030) as well as intrusion and avoidance scores (p?=?0.002 and p?=?0.034). It was not able to predict cognition and arousal scores (p?=?0.412 and p?=?0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.

SUBMITTER: Zandvakili A

PROVIDER: S-EPMC7303205 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Mapping PTSD symptoms to brain networks: a machine learning study.

Zandvakili Amin A Barredo Jennifer J Swearingen Hannah R HR Aiken Emily M EM Berlow Yosef A YA Greenberg Benjamin D BD Carpenter Linda L LL Philip Noah S NS

Translational psychiatry 20200618 1

Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = ...[more]

PMID: 32555146

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Project description:Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.

Project description: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 disorder's 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.

Dataset Information

Mapping PTSD symptoms to brain networks: a machine learning study.

Publications

Mapping PTSD symptoms to brain networks: a machine learning study.

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