NIH R01 · 2024
Machine and deep learning for finding multimodal imaging biomarkers in prodromal AD
Our proposed study focuses on developing deep neural networks and sophisticated multivariate analysis methods for studying episodic memory activations in prodromal AD subjects and age-matched normal controls. We are particularly interested in investigating the effects of spatial and object pattern-separation in subfields of the hippocampus, nearby regions of the medial temporal lobe, and functional whole-brain connections. In order to acquire a fuller understanding of the underlying physiological processes driving AD pathology, activations in hippocampal subfields must be investigated in further depth and with methodologies that exceed the current limitations of fMRI at 3T. Acquiring data…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.