Daniel Schonhaut, PhD

I am a cognitive neuroscientist at UCSF, applying statistics and machine learning to brain imaging data and the study of memory and Alzheimer's disease.

How do our brains so effortlessly encode events in memory? How are these memories organized and retrieved? How does the brain change in aging and Alzheimer's disease, and why do these changes cause memory loss and selective cognitive decline?

My research uses advanced neuroimaging techniques and computational methods to investigate these fundamental questions about human memory in health and disease. On the neural side, I utilize intracranial EEG, high-resolution MRI, and PET imaging to probe brain structure and function. At the psychological end, I use task-based cognitive testing, immersive computer games, and survey response data to assess cognition and behavior.

Through collaboration with clinicians and researchers at UCSF's Memory and Aging Center, I am working to develop richer models of Alzheimer's disease progression that enable individual patient forecasting. Ongoing collaborations with other researchers include projects implementing neuroimaging in clinical trials and nationwide studies, and exploration of large, longitudinal datasets that connect imaging biomarkers with fluid assays, genetics, and cognitive testing.

I am passionate about open science and regularly share code and data analysi pipelines through this GitHub repository. Here you will find tools for neuroimaging processing and analysis pipelines, machine learning applications to physiological and neuroimaging data, and resources for reproducible research, including code repositories for published papers.