Thoracic aortic aneurysms (TAAs) stem from diverse mechanical and mechanobiological disruptions to the aortic wall that can also increase the risk of dissection or rupture. There is increasing evidence that dysfunctions along the aortic mechanotransduction axis, including reduced integrity of elastic fibers and loss of cell-matrix connections, are particularly capable of causing thoracic aortopathy. Because different insults can produce distinct mechanical vulnerabilities, there is a pressing need to identify interacting factors that drive progression. In this work, we employ a finite element framework to generate synthetic TAAs arising from hundreds of heterogeneous insults that span a range of compromised elastic fiber integrity and cellular mechanosensing. From these simulations, we construct localized dilatation and distensibility maps throughout the aortic domain to serve as training data for neural network models to predict the initiating combined insult. Several candidate architectures (Deep Operator Networks, UNets, and Laplace Neural Operators) and input data formats are compared to establish a standard for handling future subject-specific information. We further quantify the predictive capability when networks are trained on geometric (dilatation) information alone, which mimics current clinical guidelines, versus training on both geometric and mechanical (distensibility) information. We show that prediction errors based on dilatation data are significantly higher than those based on dilatation and distensibility across all networks considered, highlighting the benefit of obtaining local distensibility measures in TAA assessment. Additionally, we identify UNet as the best-performing architecture across all training data formats.

Researchers may have found a new way to target the sources of certain brain disorders. In a study led by scientists at Mass General Brigham, deep brain stimulation (DBS) was able to pinpoint dysfunctions in the brain that are responsible for four cognitive disorders: Parkinson's disease, dystonia (a muscle disorder condition that causes repetitive or twisting movements), obsessive compulsive disorder (OCD) and Tourette's syndrome. The discovery, published in Nature Neuroscience on Feb. 22, could potentially help doctors determine new treatments for these disorders. The study included 261 patients worldwide -- 70 had dystonia, 127 were Parkinson's disease patients, 50 had been diagnosed with OCD and 14 had Tourette's syndrome. The researchers implanted electrodes into the brains of each participant and used special software to determine which brain circuits were dysfunctional in each of the four disorders.