My main research interest is how the brain uses networks of regions to generate higher cognitive functions, and how these networks reorganize following brain damage. Some specific projects related to these interests are described below.
Precise understanding of how brain organization changes following damage, and how this relates to what we see in behavior is critical if we are to develop and use rehabilitation interventions effectively. Recent advances in neuroimaging have allowed scientists to study intrinsic functional patterns of activity between brain areas (so-called “resting-state networks”), and observe regions with correlated activity between areas known to be involved in higher cognitive functions. My current research is examining how interventions like exercise training change these patterns of connectivity, especially in healthy older adults. My dissertation research focused on using these neuroimaging methods on a rare sample of surgical resection patients that allow us to carefully assess brain networks and higher cognitive functions (e.g. language, emotion, decision-making) preceding and following the creation of an anatomically circumscribed lesion. This allows us to better understand how removal of a focal brain area may impact the function of remote, undamaged brain regions, as well as how such changes might affect cognitive functioning. Better understanding of plasticity and recovery of function from a brain network perspective has the ability to inform rehabilitation approaches for patients recovering from multiple classes of brain injury, and drive development of novel interventions.
While many studies of resting-state brain networks have identified differences between healthy and diseased populations, much less work has been done linking these brain networks to specific behavioral measures. Combining thorough experimental and neuropsychological testing with resting-state functional imaging in both healthy and neurologically impaired participants, we hope to better characterize how brain networks intersect with behavioral measures.
The University of Iowa has a rich history studying emotion-mediated decision-making, particularly Antonio Damasio's somatic marker hypothesis — which states that affective signals help to guide optimal decision-making, and identifies a broad array of brain areas that supporting effective decisions. I have continued studies looking at the broader network of brain areas identified by the somatic marker hypothesis as important for emotion-guided decision-making, including gender differences in the functional asymmetry of the ventromedial prefrontal cortex, using connectivity data to explain decision-making deficits in patients with diffuse patterns of brain damage, and how brain areas connected with regions important for emotion-guided decision-making reorganize following damage.
One recent line of inquiry is to utilize electrocortiography (ECoG) data in conjunction with functional neuroimaging data to understand the neural properties of resting-state neuroimaging data, including the frequency bands that correspond with fMRI resting-state correlations, and the concordance between ECoG and fMRI in areas known for poor imaging signal, such as the orbitofrontal cortex and temporal poles.