How do you perceive threat? It's all in your pattern of brain activity

Association Between Negative Affect and Perceived Mortality Threat During the COVID-19 Pandemic: The Role of Brain Activity and Connectivity

The prevalence of the novel coronavirus (COVID-19) has been considered a major threat to physical and mental health around the world, causing great pressure and mortality threat to most people. The current study aimed to investigate the neurological markers underlying the relationship between perceived mortality threat (PMT) and negative affect (NA). We examined whether the regional amplitude of low-frequency fluctuations (ALFF) and resting-state functional connectivity (RSFC) before the COVID-19 outbreak (October 2019 to December 2019, wave 1) were predictive for NA and PMT during the mid-term of the COVID-19 pandemic (February 22 to 28, 2020, wave 2) among 603 young adults (age range 17-22, 70.8% females). Results indicated that PMT was associated with spontaneous activity in several regions (e.g., inferior temporal gyrus, medial occipital gyrus, medial frontal gyrus, angular gyrus, and cerebellum) and their RSFC with the distributed regions of the default mode network and cognitive control network. Furthermore, longitudinal mediation models showed that ALFF in the cerebellum, medial occipital gyrus, medial frontal gyrus, and angular gyrus (wave 1) predicted PMT (wave 2) through NA (wave 2). These findings revealed functional neural markers of PMT and suggest candidate mechanisms for explaining the complex relationship between NA and mental/neural processing related to PMT in the circumstance of a major crisis.

Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms

BACKGROUND

The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues.

METHODS

Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts.

RESULTS

The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms.

CONCLUSION

This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

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.