Combining Neurophysiological and Neuroimaging Tecniques to Understand Human Brain Function

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Recent advances in technology and computing power have driven a revolution in neuroscience research. These advancements have improved understanding of physiology in the healthy and diseased brain. We propose to combine two complimentary research streams (i.e., neurophysiology and neuroimaging) to better understand key physiological mechanisms (e.g., connectivity) and understand how perturbation of these physiological mechanisms underlie severe neurologic and psychiatric disorders. Combined neurophysiologic, cognitive and imaging data will be collected from a large cohort of healthy individuals (n = 100). During a neuroimaging session, magnetic resonance imaging (MRI) will be utilized to collect functional and structural brain measures. These measures will allow us to examine cortical structure (through cortical thickness analysis), white matter connectivity (through diffusion tensor imaging), and functional connectivity between regions (resting state functional MRI). This will be followed by a neurophysiological testing session. Transcranial magnetic stimulation (TMS), a non-invasive method of brain stimulation, will be combined with electroencephalography (EEG) to probe neurophysiological function. TMS-EEG will be utilized to examine cortical excitability, inhibition, plasticity, and connectivity. Finally, we will utilize an innovative method which combines neuroimaging and neurophysiology, concurrent TMS-fMRI. TMS-fMRI allows us to administer neural stimulation while measuring neural activity. TMS-fMRI allows us to directly probe connectivity between brain regions with a temporal resolution not currently possible with MRI alone. Finally, cognitive testing will be administered. The purpose of the proposed study is two-fold: to examine relationships between neurophysiology and neuroimaging (and how these may relate to cognition), and to examine inter-individual variability within a large data set. To date, very few studies have combined neuroimaging and neurophysiology. A reductionist approach to data analysis will allow us to take these complex data sets and extract a few key measures from each modality. Then, by utilizing partial least squares analysis, we can examine complex relationships between various measures collected over the course of the data. The large sample size will ensure we have statistical power to detect such relationships within the data. Individual variability will be examined using a data clustering approach. We will identify individuals who cluster together according to relationships between data extracted from our imaging, neurophysiological, and cognitive measures. In this way, we hope to identify how particular characteristics amongst individuals may drive variability within data sets. As an example, individuals showing deficits in neural plasticity may demonstrate differences in neuroimaging findings (e.g., decreased cortical thickness and connectivity) that may help us to reconcile cognitive performance. Examining these sources of individual variability may be key to understanding optimal human brain function. The proposed study will be the first large-scale database of its kind that combines neurophysiological, neuroimaging and cognitive measures. To allow maximum impact from the proposed study, data will be made accessible to researchers from around the world via online databases. Combining neuroimaging and neurophysiology with data sharing will not only provide important findings for researchers separately studying neurophysiology or neuroimaging, but will also significantly advance our understanding of human brain functioning. These advances can then be translated into clinical settings, with the potential for significant impact on the theory and treatment of neurological and psychiatric disea