Brain Wide

Brain-wide analysis focuses on understanding the complex interplay of genetic and molecular information across the entire brain, aiming to decipher its structure and function. Current research utilizes advanced computational methods, including deep learning architectures like autoencoders and graph attention networks, to analyze high-dimensional transcriptomic and imaging data, often integrating multiple data modalities to improve predictive power for diseases like Alzheimer's. This approach is significantly advancing our understanding of brain function in health and disease, enabling the identification of disease-related biomarkers and potentially leading to improved diagnostics and therapeutics.