Spatiotemporal Networks

Different brain imaging techniques provide different measures of neural activity. Functional Magnetic Resonance Imaging (fMRI), for example, can identify brain networks that covary over relatively slow time scales (e.g. .05 Hz) with precise spatial resolution. Electroencephelography (EEG) or magnetoencephalography (MEG) can identify brain networks that covary over faster temporal scales (e.g. 2-50 Hz) but with comparatively reduced spatial resolution. I am interested in integrating the information provided between these different imaging modalities in order to characterize brain networks that operate over their relatively unique spatial and temporal scales. An underlying motivation of this work is that the spatiotemporal activity may inform the degree in which individuals can perform tasks, and may be sensitive to (or reflect) differences in healthy vs. clinical populations.

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Attention

Our ability to interact with the environment depends on the ability to maintain focused attention to a single task, while also being able to re-focus attention to relevant unexpected events. These aspects of attention require the dynamic interplay between large-scale brain networks. My current research examines the properties and functional dynamics of these attention networks. I approach this experimentally by measuring cortical responses to an unattended flicker (the steady-state visual evoked potential (SSVEP)) as the degree of overlap between features at attended and unattended visual locations are varied physically and by different tasks. Neural responses to the flicker allow us to determine how goal-directed attention modulates unattended responses. I am currently applying Bayesian methods to understand the different attentional strategies that individuals may use during different tasks. These experiments will help reveal the mechanisms by which the brain integrates our internal goals with the external environment.