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.
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.
|Esch, Lorenz; Sun, Limin; Klüber, Viktor et al. (2018) MNE Scan: Software for real-time processing of electrophysiological data. J Neurosci Methods 303:55-67|
|Niso, Guiomar; Gorgolewski, Krzysztof J; Bock, Elizabeth et al. (2018) MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Sci Data 5:180110|
|Grinenko, Olesya; Li, Jian; Mosher, John C et al. (2018) A fingerprint of the epileptogenic zone in human epilepsies. Brain 141:117-131|
|Dinh, Christoph; Esch, Lorenz; Rühle, Johannes et al. (2018) Real-Time Clustered Multiple Signal Classification (RTC-MUSIC). Brain Topogr 31:125-128|
|Jas, Mainak; Larson, Eric; Engemann, Denis A et al. (2018) A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices. Front Neurosci 12:530|
|Florin, Esther; Baillet, Sylvain (2018) Commentary: Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach. Front Comput Neurosci 12:26|
|Pirondini, Elvira; Babadi, Behtash; Obregon-Henao, Gabriel et al. (2018) Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem. IEEE Trans Biomed Eng 65:1359-1372|
|Mamashli, Fahimeh; Khan, Sheraz; Bharadwaj, Hari et al. (2018) Maturational trajectories of local and long-range functional connectivity in autism during face processing. Hum Brain Mapp 39:4094-4104|
|Samiee, Soheila; Lévesque, Maxime; Avoli, Massimo et al. (2018) Phase-amplitude coupling and epileptogenesis in an animal model of mesial temporal lobe epilepsy. Neurobiol Dis 114:111-119|
|Hari, Riitta; Baillet, Sylvain; Barnes, Gareth et al. (2018) IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clin Neurophysiol 129:1720-1747|
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