By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Explores Graph Signal Processing applied to brain networks, emphasizing the relationship between brain function and structure using methods like Graph Fourier Transform and Structural-Decoupling Index.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
Explores the integration of brain structure and function using Graph Signal Processing techniques, including functional MRI and structural connectome analysis.
Explores neuroimaging basics, brain network scales, connectivity, history, and physics, emphasizing the importance of understanding data at different scales.
