Brain Disorder

Research on brain disorders is intensely focused on developing accurate and efficient diagnostic and prognostic tools, leveraging advancements in machine learning and neuroimaging. Current efforts utilize various deep learning architectures, including convolutional neural networks, transformers, and variational autoencoders, to analyze diverse data modalities such as fMRI, EEG, and histological images, often incorporating graph convolutional networks for analyzing brain connectivity. These computational approaches aim to improve the classification of brain disorders, identify disease subtypes, and predict disease progression, ultimately leading to more personalized and effective treatments.