A Guided Tour of AI in Neuroimaging

COEX 

Artificial intelligence (AI) and deep learning methods are revolutionizing neuroscience, radiology and medicine, bringing powerful new approaches to analyze brain images, text, and clinical data. As the vast landscape of activity in AI makes it hard to keep up with all the key developments, we offer a guided tour for neuroimagers - summarizing several major lines of work applying AI methods to neuroimaging and population-based brain mapping datasets. The lecture will help neuroimagers get up to speed with AI methods in neuroimaging, covering the main concepts and applications. We begin by explaining 2D and 3D convolutional neural networks (CNNs), Vision Transformers (ViTs), and variational autoencoders (VAEs), which can be adapted to distill useful information from large datasets of anatomical, diffusion MRI, and functional MRI. We explain AI methods for common neuroimaging tasks including image registration, anatomical segmentation, diagnostic classification, prognostic modeling, and disease subtyping with illustrative examples from Alzheimer’s disease, Parkinson’s disease, schizophrenia, bipolar disorder, PTSD and autism. We cover (1) fine-tuning of foundation models to neuroimaging data, (2) multimodal fusion methods, such as multimodal VAEs and normative models, that combine multiple types of brain maps to make inferences (e.g., diagnosis and prognosis). Next we cover generative adversarial networks (GANs), denoising diffusion probabilistic models (DDPM), and neural style transfer, which can enhance or synthesize images (super-resolution, or PET from MRI), and harmonize data across scanners and protocols. Finally, we explain how generative AI models are beginning to create large-scale synthetic brain datasets, with applications to quality control of streamlines in tractometry, and modeling disease effects on the human brain.

*The talk includes collaborative work led by Nikhil Dhinagar, Tamoghna Chattopadhyay and many members of our AI4AD and ENIGMA Consortia.