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Intro
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Source (brain) space instead of sensor space
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Contents
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Cortical Beta and Gamma Rhythm Resting-State Networks Follow Distinct Maturation Trajectories
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Neural sources of MEG and EEG
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Forward models for MEG and EEG
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Primary currents in the cortex
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Cortical Source Location Constraints
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Inflated Cortex
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Tangential, radial, and tilted sources
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MEG and EEG sensitivity to cortical sources
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Orthogonal patterns: MEG (or EEG) may benefit
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An MEG System
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Magnetometers and planar gradiometers
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Modulation of the Alpha and Mu Rhythms
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Silent sources
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Many Ways to Make the Problem Unique
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Terminology
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Example: The time-varying current-dipole model
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Dipolar field pattern: Focal sources
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Are dipoles good for extended sources?
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Filling
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Partly Heuristic strategies
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Multidipole Model for SEF
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The Source Locations are consistent
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Dynamics of Cortical Activity in a Picture Naming Task
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Visual Stability During Eye Blinks
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Viable Explanation
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Cerebellar activity associated with saccades
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Source locations and time courses
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Imitation of orofacial gestures
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Spaliolemporal analysis of the somalomotor
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Dipole models: Summary
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Source modeling priors
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Spatio-Temporal Structure of Source Estimates
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Early retinotopic mapping with MNE
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Effect of the orientation constraint
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Spatial dispersion of cortically-constrained MNE
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Visual percepts of an ambiguous scene
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Group analysis
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Long-Range Connectivity Differences Between ASD and TD Subjects (MEG)
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Functional definition of FFA
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Local Connectivity Different in FFA only
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MNE and friends: Summary
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Inverse problem ambiguity
Description:
Explore MEG/EEG source estimation approaches in this comprehensive lecture by Matti Hämäläinen from Massachusetts General Hospital. Delve into the intricacies of brain space analysis, forward models, and cortical source constraints. Examine various source estimation techniques, including current-dipole models, multidipole models, and minimum-norm estimates (MNE). Discover applications in studying cortical rhythms, visual processing, and neurological disorders. Learn about spatiotemporal analysis, group studies, and connectivity assessments in autism spectrum disorder (ASD). Gain insights into the challenges and solutions in MEG/EEG source localization, from basic principles to advanced methodologies and real-world applications in neuroscience research.

MEG-EEG Source Estimation Approaches: A Spectrum of Purpose-Built Optimal Tools

MITCBMM
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#Science #Biology #Neuroscience #Engineering #Electrical Engineering #Signal Processing #Brain Imaging
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Intro