5.3. Second level models¶
Second level models in Nilearn are used to perform group-level analyses on fMRI data. Once individual subjects have been processed in a common space (e.g. MNI, Talairach, or subject average), the data can be grouped and statistical tests performed to make broader inferences on fMRI activity. Some common second level models are one-sample (unpaired or paired) and two-sample t-tests.
As with first level models, a design matrix needs to be defined before fitting a second level model.
Again, similar to first level models, Nilearn provides a function
nilearn.glm.second_level.make_second_level_design_matrix for this purpose. Once
the design matrix has been setup, it can be visualized using the same function as before,
To fit the second level model, the tools to use are within the class
nilearn.glm.second_level.SecondLevelModel. Specifically, the function that
fits the model is
- Some examples to get you going with second level models are provided below::
General design matrix setup: Example of second level design matrix
One-sample testing: Second-level fMRI model: one sample test
Two-sample testing, unpaired and paired: Second-level fMRI model: two-sample test, unpaired and paired
Complex contrast: Example of generic design in second-level models
Nilearn’s statistical plotting functions provide simple thresholding functionality. For instance, functions
nilearn.plotting.plot_glass_brain have an argument
called threshold that, when set, will only show voxels with a value that is over the threshold provided.
As discussed in the Multiple Comparisons section of the introduction, the issue of multiple comparisons is important to address with statistical analysis of fMRI data. Nilearn provides parametric and non-parametric tools to address this issue.
Refer to the example Statistical testing of a second-level analysis for a guide
to applying FPR, FDR and FWER corrections. These corrections are applied using the
Within an activated cluster, not all voxels represent true activation. To estimate true positives within a cluster,
Nilearn provides the
nilearn.glm.cluster_level_inference function. An example with usage information is available
here: Second-level fMRI model: true positive proportion in clusters.
nilearn.glm.second_level.SecondLevelModel and its associated functions can also be used
to perform voxel based morphometry. An example using the OASIS dataset to
identify the relationship between aging, sex and gray matter density is available here
Voxel-Based Morphometry on Oasis dataset.