Quantitative Modeling of Brain-Behavior Relationships
When and Where
The brain is arguably the most complex object in the known universe, with ~100 billion neurons and ~100 trillion connections, from which arise human thought, language, emotion and behavior. Exactly how the brain’s anatomy and physiology map to population-level variability in behavioral or cognitive function remains a mystery. The explosion of publicly available large datasets including neuroimaging and cognitive/behavioral measures for thousands of individuals has allowed the application of data science to attempt to answer some of these questions. These approaches are not without problems however. This talk will introduce brain-behavior mapping, recent advances, current issues and potential solutions in our quest to understand the human brain in health as well as disease.
About Amy Kuceyeski
For over a decade, Amy has been interested in understanding how the human brain works in order to better diagnose, prognose and treat neurological disease and injury. Quantitative approaches, including machine learning, applied to data from rapidly evolving neuroimaging techniques, have the potential to enable ground-breaking discoveries about how the brain works. Amy has particular interest in non-invasive brain stimulation and pharmacological interventions, like psychedelics, that may be used to modulate brain activity and promote recovery from disease or injury.
Amy is also the founder and co-director of the cross-campus working group Machine Learning in Medicine, which aims to bring together ML researchers in Cornell-Ithaca/Cornell-Tech and clinicians and researchers at WCM to address medicine’s toughest problems. See the group’s website here.