A major obstacle in the study of human brain function is that we currently have limited understanding of how the measurements made by different instruments, such as fMRI and EEG, relate to one another and to the underlying neuronal circuitry. Significant efforts have led to development of models within various specialist fields, but fragmentation has held us back from advancing our interpretation of the spatiotemporal characteristics of non-invasive imaging signals. Bringing together the various models that pertain to signal interpretation would constitute a significant advance in what we can learn from non-invasive neuroimaging. In this project we take such an integrative approach to the study of cortical sensory systems. We intend to develop a set of connecting, empirically driven models that will predict how sensory stimuli are encoded in neuronal population activity underlying electrophysiological measures (AIM 1), and hemodynamic measures (AIM 2), leading to a comprehensive integrative model (AIM 3). The pivotal integrative model (AIM 3) will improve our understanding of, and revolutionize the information we can obtain from fMRI, the modality with the highest potential for mapping detailed functions non-invasively in humans. To achieve this we will combine hemodynamic and electrophysiological measurements at multiple spatial scales in humans, and in rodents at very high resolutions. This will include non-invasive (fMRI at 3T and 7T, MEG and EEG) and invasive (optical imaging, ECoG) modalities obtained from healthy humans. By obtaining multiple modality recordings from the same individuals, using the same stimuli and tasks, we will be able to unequivocally link clear and specific electrophysiological information to widely used fMRI technology, while significantly improving our understanding of the electrical and hemodynamic phenomena underlying brain activity. The research constitutes a multicenter endeavor to A) develop a comprehensive model to link external inputs to neuronal population physiology to non-invasive imaging measures, B) obtain state of the art multimodal recordings from the same individuals in order to bridge modalities and inform the models, C) validate the models with data from multiple modalities (ECoG, fMRI, MEG/EEG, optical recordings) and brain systems (visual, somatosensory and motor), and D) make algorithms and data available to the neuroscience community to foster further development beyond the project's lifetime. Moreover, the research will foster reconciliation of different theories about the relation between electrophysiology and fMRI and will lead to `breakthroughs in understanding the dynamic activity of the human brain'. Such breakthroughs will be essential in improving disease models of the nervous system, which rely on inferences about neuronal population activity from non-invasive imaging of human brain activity.
A lot has been learned about how the human brain is organized for perception, thought, and behavior due to the incredible advances in non-invasive methods to image the living human bran, particularly functional MRI. The whole brain can be imaged at once with fMRI, and we obtain an indication of which brain regions are involved in a function (such as visual observation) and how the different brain regions interact. Unfortunately existing methods such as fMRI are indirect, measuring metabolic changes associated with brain activity, rather than the brain activity itself. At the same time, enormous progress has been made in the study of brain cells and brain circuits at the microscopic scale using invasive animal recordings. It is likely that many aspects of human thought, behavior, and disease will only be understood by studying the human brain with the same level of detail that we study animal models. In this project we will significantly improve our understanding of exactly what is measured by methods used for studying the human brain, and we will link these measurements to the more detailed analyses of circuits and neurons typically done in animal research. We will do so by building mathematical models that capture the complex relationships between brain circuits and non- invasive imaging, and by directly comparing the same paradigms in the human brain and in animal models. With these models the quality of the non-invasive methods can be greatly enhanced, providing scientists with advanced tools to discover how our brain works.
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