Large Scale High Temporal Resolution Effective Connectivity Analysis
Offered By: MITCBMM via YouTube
Course Description
Overview
Syllabus
Intro
Outline
Challenge: When Multiple Factors Influence Judgements
Two Explanations of the Same Results
Task effects, decision mechanisms, response blases...
and the problem of making observations after the interactions have been completed
BOLD Imaging: Promise and Challenges
Identify key components of processing models
Localize components based on empirical literature
Determine Pattern of Effective Connectivity
Two intuitions about cause and effect
Granger Causation: Implementing Wiener's definition of causality
Implementation: Prediction by lagged vector autoregression model (VAR)
Critical assumptions requirements of classical Granger causation
GPS: Implementing Granger's Assumptions with Integrity
Imaging Considerations
Identifying Rols: 3. Eliminate redundant ROis based on timeseries comparison
Identifying Rols: 3. Define ROI around centroid based on timeseries comparison
Prediction and the Stationarity Problem
Kalman Filter: Model, Predict, Evaluate, Update
Measuring Granger Causation
Data Reduction Through Graph Theory
Afferent/Efferent Relationship between two
Comparison between experimental conditions
GPS: Our processing stream to automate the Granger Analysis of MR-constrained MEG/EEG data
Neural Decoding: Using the same data to probe representation
Challenges and Opportunities
Taught by
MITCBMM
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