Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD

Predictive modeling involving brain morphological features and other covariates is of paramount interest in such heterogeneous mental disorders as PTSD. We propose a comprehensive shape analysis framework representing brain substructures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of subcortical data and represent individual shapes by their principal scores under a shape PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. Subsequently, we use these PCs, the auxiliary exposure variables, and their interactions for regression modeling and prediction. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. This approach reveals considerably greater predictive power under the elastic shape analysis than the current approaches and helps identify local deformations in brain shapes associated with PTSD severity.

Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD

Predictive modeling involving brain morphological features and other covariates is of paramount interest in such heterogeneous mental disorders as PTSD. We propose a comprehensive shape analysis framework representing brain substructures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of subcortical data and represent individual shapes by their principal scores under a shape PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. Subsequently, we use these PCs, the auxiliary exposure variables, and their interactions for regression modeling and prediction. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. This approach reveals considerably greater predictive power under the elastic shape analysis than the current approaches and helps identify local deformations in brain shapes associated with PTSD severity.