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Overview
iMap4 is an open source Matlab toolbox for the statistical fixation mapping of eye movement data, implementing a user-friendly interface that provide straightforward, easy to interpret statistical graphical outputs. iMap4 matches the standards of the robust statistical analysis implemented in neuroimaging techniques (M/EEG, fMRI). iMap4 applies univariate, pixel-wise Linear Mixed Models (LMM) on the smoothed fixation maps with each subject as one of the random effects, which offers the flexibility to code for multiple between- and within- subject comparisons. Users can perform all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, it implements non-parametric statistics based on resampling. We developped a novel spatial cluster test based on bootstrapping to assess the statistical significance of the linear contrasts. We hope that iMap4 could provide an easy access to the routine use of robust data-driven analyses in spatial fixation mappings.
The methodological details of the spatial mapping using Linear Mixed Models and the resampling algorithm, as well as the validation of statistics implemented in iMap4 are provided in a peer reviewed paper (currently under review). For a general thoughtful introduction to mixed models, users of the toolbox should refer to Raudenbush & Bryk (2002), McCulloch, Searle & Neuhaus (2011), and Christensen (2011). We recommend iMap4 users also read Baayen, Davidson & Bates (2008) and Bolker et al., (2009) for some examples on how to perform and report mixed model analysis.
This wiki is adapted from the original iMap4 guidebook.
If you have any questions about the iMap4 usage, please email [email protected]
Getting started
Theory
- Linear Mixed Models
- Pixel Wise Modeling and non-parametric statistics
- Family-wise error rate (FWER) under H0
- Power analysis of iMap4
Data structures and function usage
- Core functions
- Input Matrix
- LMMmap
- StatMap, Posthoc and figure outputs
- Other useful features and function
Example 1 (GUI)
- Background of Example 1
- Using the GUI (1): Import Data and label columns
- Using the GUI (2): Parameters and Conditions
- Using the GUI (3): Create smoothed fixation matrix
- Using the GUI (4): Optional for preprocessing
- Using the GUI (5): Descriptive Statistics Report
- Using the GUI (6): Spatial Mapping Using Linear Mixed Models
- Using the GUI (7): Hypothesis testing and Display results
- Using the GUI (8): Post-hoc analysis
Example 2 (Code)
Example 3 (Code)
Example 4 (Code)
Future development
Additional information