Human systems neuroscience.
Mapping the human brain requires integrating technologies that allow measuring behavior and brain signals evolving over slower and faster time scales. Behavior is organized at multiple time scales. It take years to acquire fluency in non-native languages, but car drivers can reliably pay attention to approaching danger in congested rush-hour freeways in a matter of milliseconds. Such large operational range requires distinct, dedicated brain mechanisms. The mechanisms involved in processing slow-evolving events may depend on both, changes in the activity of populations of neurons as well as changes in the properties of networks of white-matter fascicles. The mechanisms dedicated to fast information processing are believed to be coded in the spikes and synaptic activity of neuronal populations.
I study the brain and psychology of individuals from a systems neuroscience perspective. This means that I am interested in understanding how neurons in different brain areas connect together to form networks. I combine multiple neurobiological measurements to study how the mechanisms of these brain networks determine our vision of the world, values and motivate our behavior. My research relies on a model-based approach. Models are implementations of otherwise abstract theories; they allow for generating specific predictions for human behavior or brain connectivity. Errors in model prediction can be exploited to test and falsify alternative theories. I use neurobiological measurements from two magnetic resonance imaging (MRI) technologies (diffusion and functional MRI). Using these measurements in living human brains I build models that predict either the properties of the brain network of connections (the connectome) or human behavior from brain activity.