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GPS: Our processing stream to automate the Granger Analysis of MR-constrained MEG/EEG data
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Classroom Contents
Large Scale High Temporal Resolution Effective Connectivity Analysis
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- 1 Intro
- 2 Outline
- 3 Challenge: When Multiple Factors Influence Judgements
- 4 Two Explanations of the Same Results
- 5 Task effects, decision mechanisms, response blases...
- 6 and the problem of making observations after the interactions have been completed
- 7 BOLD Imaging: Promise and Challenges
- 8 Identify key components of processing models
- 9 Localize components based on empirical literature
- 10 Determine Pattern of Effective Connectivity
- 11 Two intuitions about cause and effect
- 12 Granger Causation: Implementing Wiener's definition of causality
- 13 Implementation: Prediction by lagged vector autoregression model (VAR)
- 14 Critical assumptions requirements of classical Granger causation
- 15 GPS: Implementing Granger's Assumptions with Integrity
- 16 Imaging Considerations
- 17 Identifying Rols: 3. Eliminate redundant ROis based on timeseries comparison
- 18 Identifying Rols: 3. Define ROI around centroid based on timeseries comparison
- 19 Prediction and the Stationarity Problem
- 20 Kalman Filter: Model, Predict, Evaluate, Update
- 21 Measuring Granger Causation
- 22 Data Reduction Through Graph Theory
- 23 Afferent/Efferent Relationship between two
- 24 Comparison between experimental conditions
- 25 GPS: Our processing stream to automate the Granger Analysis of MR-constrained MEG/EEG data
- 26 Neural Decoding: Using the same data to probe representation
- 27 Challenges and Opportunities
