In this podcast we talk about the constant struggle shared by all scientists in this field to find the right paradigms, acquisition tools, and analysis approaches to add insight into fundamentals of brain organization and how it relates to behavior. We talk about cognitive flexibility, Autism, the salience network, and the need for an ontology of network nomenclature so that the field can better communicate, share, and understand findings. We also discuss the NIH’s goal of having a research domain criteria (RDoC) to organize and understand disorders in a more brain data-driven manner. Lastly, we discuss her perspective on advancing diversity in science. It was a fun conversation that sharpened up and put in perspective the many challenges facing functional brain imaging research.

Guest:

Lucina Uddin received her B.S. in 2001 in Neuroscience and Ph.D in Psychology/Cognitive Neuroscience, both from UCLA. From 2006 to 2008 she did a post doc at NYU School of Medicine and From 2008 to 2010 performed a second post doc at Stanford Cognitive and Systems Neuroscience Laboratory. From 2010 to 2013 she was an instructor in the Stanford School of Medicine, Department of Psychiatry and Behavioral Science. In 2014 she moved to the University of Miami and in 2018 became, as an Associate Professor, the Director of Cognitive and Behavioral Neuroscience Division. She is a Handling Editor of the journal NeuroImage and Senior Editor of the journal Network Neuroscience. She’s written two books, “Insula” in 2014 and “Salience Network of the Human Brain” in 2016. She won the OHBM young investigator award in 2017 and the OHBM diversity award in 2021.

Over the past 15 years, Lucina has rapidly risen in the ranks of respected cognitive neuroscientists who effectively and creatively use cutting edge MRI and fMRI. She and her lab investigate the relationship between brain connectivity and cognition in typical and atypical development, wielding the tools of functional connectivity analysis of resting-state functional magnetic resonance imaging data as well as structural connectivity analyses of diffusion-weighted imaging data.