Cortical Thickness

Cortical thickness, the measurement of the gray matter layer in the brain, is a crucial biomarker for understanding neurological and psychiatric conditions. Current research focuses on developing accurate and efficient methods for cortical thickness estimation using advanced image processing techniques, including deep learning models like convolutional neural networks and graph convolutional networks, often integrated with diffeomorphic registration or contrastive learning approaches. These advancements enable faster and more robust analyses of large clinical datasets, improving the accuracy of brain age prediction and facilitating the study of neurodegenerative diseases like Alzheimer's, particularly regarding disease progression and individual variability. Ultimately, improved cortical thickness analysis promises to enhance early diagnosis and personalized treatment strategies.