Neural connectome prospectively encodes the risk of post-traumatic stress disorder (PTSD) symptom during the COVID-19 pandemic

Background The novel coronavirus (COVID-19) pandemic has affected humans worldwide and led to unprecedented stress and mortality. Detrimental effects of the pandemic on mental health, including risk of post-traumatic stress disorder (PTSD), have become an increasing concern. The identification of prospective neurobiological vulnerability markers for developing PTSD symptom during the pandemic is thus of high importance. Methods Before the COVID-19 outbreak (September 20, 2019–January 11, 2020), some healthy participants underwent resting-state functional connectivity MRI (rs-fcMRI) acquisition. We assessed the PTSD symptomology of these individuals during the peak of COVID-19 pandemic (February 21, 2020–February 28, 2020) in China. This pseudo-prospective cohort design allowed us to test whether the pre-pandemic neural connectome status could predict the risk of developing PTSD symptom during the pandemic. Results A total of 5.60% of participants (n = 42) were identified as being high-risk to develop PTSD symptom and 12.00% (n = 90) exhibited critical levels of PTSD symptoms during the COVID-19 pandemic. Pre-pandemic measures of functional connectivity (the neural connectome) prospectively classified those with heightened risk to develop PTSD symptom from matched controls (Accuracy = 76.19%, Sensitivity = 80.95%, Specificity = 71.43%). The trained classifier generalized to an independent sample. Continuous prediction models revealed that the same connectome could accurately predict the severity of PTSD symptoms within individuals (r2 = 0.31p<.0). Conclusions This study confirms COVID-19 break as a crucial stressor to bring risks developing PTSD symptom and demonstrates that brain functional markers can prospectively identify individuals at risk to develop PTSD symptom

PREDICTION METHODS IN LARGE-SCALE NETWORK ANALYSIS FOR NEUROIMAGING DATA

Brain functional connectivity data are critical for understanding human brain structure and cognitive disease diagnostics. The underlying genetic architecture behind brain functional connectivity is a critical topic in medical studies, which helps unveil the linkages between genetic variants and brain activity and further understand cognitive diseases and brain disorders. The rapid emergence of large scale imaging studies provides researchers with more opportunities to discover the connections between brain system and genes. However, existing methods in imaging genetics are not sufficient in dealing with the high-dimensional data with complex structure, thus limiting the discovery of biological foundation of neuro-development. Therefore, we developed novel statistical approaches for efficient analysis of imaging genetic data.In the first project, we developed a matrix decomposition based method for denoising and recovering the structure of the subject-wise network based on the assumption of factor model. We decompose the subject networks into two parts: a common low-rank basis and subject-specific loadings on the basis. A matrix L0 penalty problem was formulated to accelerate the algorithm. Meanwhile, to avoid iterative computation of high dimensional matrix, we will select a relatively lower dimension basis in the first step, which is a coarse estimator, and then do a fine-tuning in the second step based on the results in step one. In the simulation study, it showed that our approach outperformed other existing approaches in terms of recovering accuracy and computing speed. We also proved that under mild conditions, the algorithm converges fast in an exponential rate. In the second project, we proposed a matrix regression approach for imaging genetic studies. The proposed regression model includes two steps. In the first step, a marginal screening procedure was used to study the univariate associations between genetic variants (SNPs) and imaging phenotype. The theoretical p-value for the marginal screening step was derived using random matrix theories, and important SNPs were selected based on the univariate associations using knock-off. In the second step, a multivariate regression model with all the important SNPs selected as covariates were fitted, and a penalized optimization problem was solved using Nestrov methods. We studied the theoretical properties of the proposed two-stage algorithm thoroughly and simulation studies supported the efficiency and consistency of the proposed method.In the third project, we established a missing data imputation framework to address the issue of missing image modality in real data. The missingness of some imaging modality is common in real imaging data, which may undermine the statistical power in the prediction and inference. However, inaccurate imputation of the missing modality may lead to bias in prediction. Therefore, we thoroughly studied the performance of imputation approaches, including LASSO and ridge models, under different conditions, and concluded the optimal choice of imputation options under the different settings.Doctor of Philosoph