Magnetoencephalography (MEG) and electroencephalography (EEG) provide a unique window to the large scale spatiotemporal neural processes that underlie human brain function. However, even with restrictive models, the low SNR, ill-posedness of the inverse problem, and difficulty of differentiating ongoing brain activity and other electrophysiological signals from induced and event-related changes result in unique challenges in data analysis and interpretation. These challenges include accounting for artifacts and noise, accurately forward modeling from cerebral sources to sensor space, defining appropriate source models, computing inverse solutions, and detecting and quantifying interactions. In this grant we will continue the development of our linked MNE-Python and Brainstorm software packages. Emphasis in the current software is on data preprocessing, the formation and statistical analysis of inverse solutions, and advanced, interactive display and interpretation of these solutions. We have established standard workflows for cortical current density mapping, time-frequency analysis, and statistical testing in both MNE and Brainstorm for these procedures. In the next project period in Aim 1 we will build on these procedures adding new dimensions to the data workflows for the interaction measures described in Aim 3. Under this aim we will also continue general software development (including automated testing and documentation), support and dissemination activities for users.
In Aim 2, we will expand the use of Python-based scripting to facilitate large-scale batch processing of multiple subjects and/or conditions from an extensive experimental study. We will also add the ability to import locations of intracranial EEG sensors (depth electrodes and cortical grids) for display and interaction analysis using methods from Aim 3 and for cross-validation of MEG/EEG non-invasive source models. To fully realize the potential of EEG/MEG to elucidate the spatio-temporal networks that underlie human perception, cognition, and action, we will also develop tools to investigate the interactions between cortical neuronal populations. These tools should take into account the dynamically nature of these networks, the inherent complexity of causal and inter-frequency interactions amongst neural populations, and the fact that interactions can occur between multiple brain regions. Since no single parsimonious model can account for all such interactions, Aim 3 of this grant will develop a suite of interaction modeling and powerful visualization tools for use by neuroscience and clinical researchers.

Public Health Relevance

Magnetoencephalography (MEG) and Electroencephalography are totally non-invasive brain imaging tools, which provide information on the spatial distribution and precise temporal orchestration of human brain activity. MEG and EEG can be thus used to understand and diagnose abnormalities underlying a wide range neurological and psychiatric illnesses including as epilepsy, schizophrenia, obsessive-compulsive disorder, autism spectrum disorders, and Alzheimer's disease, as well as cognitive deficits such as delayed acquisition of language. However, more widespread use of these methods especially in large populations has been problematic because of the lack of well-established analysis approaches, which map the sensor data into the brain space for detailed temporal, spatial, and connectivity analysis. This research will provide well- documented and tested novel analysis software to promote both basic neuroscience and clinical research applications using MEG and EEG in combination with anatomical MRI and intracranial EEG data.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Luo, James
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Massachusetts General Hospital
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