Single Season Changes in Resting State Network Power and the Connectivity between Regions: Distinguish Head Impact Exposure Level in High School and Youth Football Players.

The effect of repetitive sub-concussive head impact exposure in contact sports like American football on brain health is poorly understood, especially in the understudied populations of youth and high school players. These players, aged 9-18 years old may be particularly susceptible to impact exposure as their brains are undergoing rapid maturation. This study helps fill the void by quantifying the association between head impact exposure and functional connectivity, an important aspect of brain health measurable via resting-state fMRI (rs-fMRI).

Quantifying the Association between White Matter Integrity Changes and Subconcussive Head Impact Exposure from a Single Season of Youth and High School Football using 3D Convolutional Neural Networks.

The effect of subconcussive head impact exposure during contact sports, including American football, on brain health is poorly understood particularly in young and adolescent players, who may be more vulnerable to brain injury during periods of rapid brain maturation. This study aims to quantify the association between cumulative effects of head impact exposure from a single season of football on white matter (WM) integrity as measured with diffusion MRI. The study targets football players aged 9-18 years old.

Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks.

The effect of Type 2 Diabetes (T2D) on brain health is poorly understood. This study aims to quantify the association between T2D and perfusion in the brain. T2D is a very common metabolic disorder that can cause long term damage to the renal and cardiovascular systems.

Spatio-angular consistent construction of neonatal diffusion MRI atlases.

Atlases constructed using diffusion-weighted imaging are important tools for studying human brain development. Atlas construction is in general a two-step process involving spatial registration and fusion of individual images. The focus of most studies so far has been on improving the accuracy of registration while image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson's Disease.

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases.

Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency.

Atlases constructed using diffusion-weighted imaging (DWI) are important tools for studying human brain development. Atlas construction is in general a two-step process involving image registration and image fusion. The focus of most studies so far has been on improving registration thus image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases.